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Matching Environment to People (Notes from May 27 – June 2, 2019) June 20, 2019

Posted by Anthony in Automation, Blockchain, Digital, experience, finance, Founders, global, Hiring, Leadership, questions, social, Strategy, Uncategorized, WomenInWork.
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In this particularly busy week, I found the theme of the week to be particularly amusing, but coincidentally or not, the dominoes fell that way. Normally, a theme arises like that because everyone is in finance or the same segment or conference is aligning. I just happened to catch a week where the insight that I drew from each person reflected similarly.

Meredith Golden, a dating consultant of sorts, discussed how she assesses all levels of dating profiles for her clients. She goes through a process that she’s dialed in to obtain her optimum level of clients as well as the right approaches to proceed. Asking herself what she wanted was key in determining how she’s grown her business, especially as an entrepreneur and CEO.

Chief Instigator Matt Charney. Now that’s a fun title. And I won’t ruin it. He goes through his past with Disney and Warner Bros and why/how he moved into the HR tech doing marketing – what he saw and how it’s different now. Fascinating and fun segment.

Part of the fun of being an entrepreneur is deciding who you want to do business with. But when it’s difficult, especially at the start, you’re most excited to get ANYONE to work with (unless you luck into that massive customer to start – rare rare rare). This is Kyle Jones of iCRYO found out. Then he gained traction, quickly, and realized he needed to be a bit more diligent in who he wanted to work with – what was ideal for the business, as well as the brand moving forward.

David Epstein likes throwing wrenches, I imagine. He authored the book Range, testing the generalist vs specialist question. As a generalist masquerading currently as a specialist, I appreciated what he was talking about the strength of generalists. But I do understand the place that specialists have in our society, especially deep tech, research and other exceptional areas.

Deb DeHaas grew up under the tutelage of her mother who fought the idea of being an accountant growing up to learn and adapt to the idea of being told what she could/couldn’t do wasn’t ACTUALLY an assessment of her ability to do those things. Such a simple, fascinating concept. She could totally be an accountant, engineer, as she pleased. Took a lot of perseverance but she had a manager at Andersen (before folding) who was a woman and told her to always chase what she wanted – now she’s leading the Inclusion and Diversity team with Deloitte’s Corp Governance Arm. Quite the story of growing up and what she learned.

Not to be outdone, Kim Wilford, who acted as General Counsel for GoFundMe, discussed how she came into her role in charge of the nonprofit arm, and what they’ve done in growing the company and its donations. How to connect marketing, wearing multiple hats and helping people help others. Inspirational while metric-driven, not just dream-built.

I hope you enjoy the notes – a few I didn’t write extra here but had fascinating insights into Happiness Hacking, investing in founders and how they grew companies such as Vroom and GoodEggs. Let me know what you think!

  • Meredith Golden (@mergoldenSMS), CEO of Spoon Meets Spoon (Wharton XM)
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    • Talking about having 6-7 clients
    • Ghostwriting messages
    • Client work depends – assessing / diagnosing the problem
      • Not matching (pictures), profile, messaging, getting them to meet, etc…
    • Metrics based on what the initial diagnosis was
  • Matt Charney, Executive Editor – Chief instigator at RecruitingDaily (Wharton XM)
    screen-shot-2014-06-22-at-2.46.49-pm

    • Talking about workplace and conspiracies

 

 

  • Kyle Jones, iCRYO Franchises (Wharton XM)
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    • Franchising initially – would’ve been a bit pickier when starting but too excited to land first deals
    • Out of 100 franchises, they’ll go with ~5 or so
    • 10 franchises, working on doing a big deal to launch 100+

 

  • David Epstein (@davidepstein), author of Range (Wharton XM)
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    • Discussed how Nobel laureates and creative types are often generalists that spend a lot of time learning / making
      • Stumble on new ideas or concepts in their work
    • Generalists aren’t bad – allow to see a different perspective and combine ideas
      • Think “The Quants” – relationship between corn prices compared to research on _

 

 

 

  • Deb DeHaas (@deborahdehaas), Chief Inclusion Officer, C4Corp Gov Deloitte (Women at Work)
    gx-global-center-for-corporate-governance-new-promo

    • Discussed her mother, who had passed away at the age of 90 recently, who was told she couldn’t be an accountant
      • Wasn’t her role – she pursued it anyhow and ended up being an engineer before quitting and being a community leader
    • Worked in Gulf Oil’s accounting dept and helped her husband through med school
      • First councilwoman in her town, elder at the church
    • Deb started at Andersen until it folded, worked for only one woman but she was taught there were no barriers
  • Bentley Hall (@bhallca), CEO of Good Eggs (Wharton xm)
  • Mitch Berg, CTO of Vroom (Wharton XM)
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  • Alex Salkever (@alexsalkever), Vivek Wadhwa, authors “Your Happiness… Hacked” (Wharton XM)
    • With Stew Friedman, finding the middle ground of tech with children / teenagers and the happy medium
    • How is it that we find some things appealing but others are a burden
    • Facebook being a publishing agency – aren’t they responsible for what the product? “Newsfeed” example.
    • Google Maps or Waze as a hindrance at the local level – dangerous, maybe?
      • Extremely valuable, still, in new places / out of the country, especially
        • Different, maybe, for walking if alternative is talking and communicating with others
    • Problem with Facebook / Whatsapp – Whatsapp unmoderated group chats and only requiring a phone number
      • Encrypted, but what cost? Facebook – for Vivek, just limits to 1-way action
    • Social media as killing people – think India’s problems
  • Ed Sim (@edsim), FP @ Boldstart Ventures (20min VC 092)
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    • LivePerson, GoToMeeting are 2 of his biggest investments as lead, exited / public
    • Started a fund in 1998, DonTreader Ventures – left in 2010
      • Idea was to bring SV style to NY – VCs would look at financials / models, but they looked at people and product – focus on markets
      • Most investors were corporate but cratered after 2008
    • Started a new seed fund for sticking with what he knew as well as recognizing a shift in 2007 for open source and cloud – consumer-based
    • SaaSify vertical markets with GoToMeeting founders who wanted to do new things – $1mln, $1.5mln
      • Enterprise people were looking to get a market for small ~$1mln investments
    • Hated starting a fund – “Fundraising sucks.” – Could find a great enterprise and tech entrepreneurs at seed stage – got $1mln and made 10 inv
      • First 5-6 investments were less than $5million pre-$, sold 4 by 2012 – had option values for series A or being sold to strategic companies
        • Entrepreneurs wanted to sell in those cases, but with cloud, definitely found that it was reasonable and cheaper to do SaaS
    • First / second generation founders or single vs others – “No single founders”
      • As the first institutional round, they’re first big money in. Last few investments were second or more founders – little bigger rounds
      • If first-gen founders, funding rounds are smaller – deep expertise in their field (and have to be engineers building product)
    • “Enterprise can be fucking hard” – have to know the industry – he has 20 years, partner has 10 and new partner as building 5 companies
      • Why he went this route? Started at JP Morgan as building quant trading models as liaison Business QA between engineers and portfolio managers
        • Derivatives models to real-time pricing models – feeds from Reuters or others, risk metrics and crank out the other side
      • Enterprise was exciting to him
    • Could take enterprise founders and redo or build a new company by changing the pain point – customers can be repeat because new pain point
      • Harder to do that in consumer
    • Leads come from founders – roughly 75% as recommendations from portfolio companies (wants to be first thought or call)
      • Helps founders get their pick and decide where to go – if you have an analyst report, may not be a great market opportunity initially
    • Environment of seed funding: Jeff Clovier of SoftTech as one of few microVC’s and now it’s 400+
      • Just want to be hyper-focused and being nimble – main value add as understanding the cadence (2 founders coding together to selling)
      • Stratification of VC – best ones have gotten so large that they can’t write small checks efficiently
        • Entrepreneurs don’t want $5-10mil immediately out of the gate – mismatch, looking for less for less dilution
      • Deal flow of crowdfunding: says sometimes they will leave $250k after leading for AngelList or building new relationships
    • Jason Calcanis blog Launch Ticker, trend as rise of the developer (multiple people in company using same thing – buying licensing)
      • Messaging as another interesting trend in the enterprise space – his most used app – Slack (SlackLine – private, external channels)
    • Most recent investment – stealth investment in a repeat founder (founded and sold before) – security focused on developer
  • Kim Wilford, General Counsel at GoFundMe (Wharton XM)
    go_fund_me_logo_courtesy_web_t670

    • Talking about joining, hadn’t considered nonprofit space
      • For profit arm and the nonprofit
    • Mentioning pushing marketing and following metrics for raising vs donations
    • Can influence news stations and push for higher engagement
    • Done almost $5bn in funding across 50 million donations
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How to Humanize Data for Enhancement (Notes from May 20 – 26, 2019) June 12, 2019

Posted by Anthony in Automation, Blockchain, Digital, experience, finance, Founders, global, Hiring, Leadership, NFL, questions, social, Strategy, Uncategorized, WomenInWork.
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I’m going to keep this brief this week, as I’m trying out an added format of posting. I believe I’m going to test Nuzzel news to connect and share the links of articles I’ve read through the week (maybe once or twice – weekend one out on Monday, and another for the week through Thursday?). This post, concentrating on my week’s podcast or segment listens, will still go out. That’s my goal, though.

The week I listened to these, I was actually on my way to Monterrey with my family, though, so I had a solid 90 minutes each way to go through some longer episodes – thankfully, I found Doug’s take on culture and leading a community with Eat Club very insightful, as well as a blockchain discussion by the a16z squad particularly fun.

With the news and media focused on AI, Bias, Diversity and Ethics of ML and scariness of algorithms, it’s good to hear opinions that are focused, but thoughtful on the premise of what is being proposed. In a vacuum, yes, these can be dangerous. However, that’s not often the case when models are being deployed into production. Some are unintentional or started with a simple question of relationships (think Cambridge Analytica and how it started via Likes research on music at Facebook). That’s not necessarily justification for something that clearly had a large impact, but we’d like to know that teams are improving and have that awareness now more than ever before. That’s a positive.

I believe that these episodes, though, take a bit more of a nuanced approach of mathematics and numbers and attributes a human eye that has acquired the necessary expertise to design better models or come up with a better framework for action. We hear that with Dan Waterhouse of Balderton Capital (of a delightful Applied Math degree), who had to learn to apply more of an operational and human/market behavior investor since he had the metrics focus while learning. Then, a fascinating segment with Laura Edell, a sports fan, who struggled to believe that the metrics that were widely accepted as ‘most accurate’ (re: not necessarily max accuracy) included all components of what could be measurable – made some assumptions to test based on twitter/media analysis and coupled it with the data that was widely acceptable to come up with an excellent Final 4 Model.

To continue the theme, not to be outdone, Geoffrey Batt and Nikos Moraitakis each spoke of differing aspects of metrics that they honed in on and ignored the common threads of why those weren’t valid, despite what they saw as opportunities. Geoffrey as it pertained to Iraq investing and Nikos in the form of starting and building a company in Greece, not exactly the traditional hotbed. However, each persisted and are successful today – so there’s a lesson in doing the things that we want to do.

Remember, ultimately, numbers and averages usually take the aggregate, collection of those that may not have had the timing, willingness or ability to push but with enough persistence and support, some will certainly persevere.

Hope you enjoy!

  • Doug Leeds (@leedsdoug), CEO at Eat Club (Dot Complicated, Wharton XM)
    cropped-logo-highres1

    • Talking about going from Ask.com to Eat Club – other companies, as well
    • Quiz leading in was on different likes/dislikes
      • Harry Potter-themed eating pop-ups
      • Anti-spoiler pin (digital, season and episode to avoid conflict)
        • Anything was good that brings community together
        • Said that there were employees at work that took off Monday after GoT finale since they hadn’t watched
    • Delivery people are employees, not contractors – become regulars at corporate events and a part of the culture
      • Help build the culture of the locations – food and how they match
      • Building culture of Eat Club with improv – ability to improve skills there, as well
    • Eat Club numbers – 1000+ companies served, about 25k lunches provided
      • Regional difference of Indian focus in NorCal, Mexican focus in SoCal
        • Acquired an Indian food provider for new techniques – Intero?
        • Acquired another, as well
  • Five Open Problems Toward Building Blockchain Computer (a16z 5/16/19)
    ah-logo-sm

    • Ali Yahya (@ali01), Frank Chen and looking at crypto
    • New paradigm has to improve upon one or two particular ways for new applications, but likely sucks in most others
      • Mobile phone as enabling behaviors for instagram/uber and such with camera and gps in the phones
      • Have to trust the company currently to do what they claim to be doing – trust Google with so much to have the interactions
      • Now, have a computational fabric that separates the control of human power, self-policing and security bottom-up
    • Difference in communication cost – bounded on the end by speed of light
    • Trying to make networking efficient – miners propogating to other miners – blockchain distribution network
      • 1 MB vs 2 MB blocks but can kill some small miners
      • Agreeing on blocks or updates are final
    • Non-probabilistic vs fast – need to be faster than 60 minutes, for instance
      • Improvements on networking layer with Ethereum 2.0, Cosmo and cost or Proof of Stake (vs Of Work)
        • Who gets to participate? PoW requires everyone to compute an expensive proof of work.
        • PoS – intrinsic token that you have to own in order to buy participation, 2% ownership says 2% of the say to say yay or nay
        • Cost of participating is less expensive because it’s intrinsic
      • Practical for everyday use, small interactions between people and machines or people
      • Trust as the bottleneck to scale
    • 3 Pillars of Computation/Scalability: Throughput, latency, cost per instruction
  • Daniel Waterhouse (@wanderingvc), GP at Balderton Capital (20min VC 091)
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    • Sits on boards of Top10, ROLI, Lovecrafts, TrademarkNow etc
    • Was 5 year partner at Wellington Partners and invested in EyeEm, Hailo, Yplan, Bookatable, SumAll, Readmill (sold to DropBox), Qype (sold to Yelp)
    • Break in 1999 by getting to work at Yahoo for about 10 years before getting to venture in 2009
    • Applied Math background, tend to understand complexity and appreciate solutions in technology – different experience for entrepreneurs
      • Learned to be less metric-driven – less obsessed on numbers and data, more human and market dynamics
    • Operators vs career VCs – successful firms take different skills at different times
      • Exit, raising money, portfolio management, scaling, growing
      • Important to watch how a business is grown, whether it’s yourself or by being a long-term investor or close in other ways
    • Entrepreneurship as a career path and as a global one, investors in similar mindset and ambition – can take it anywhere
    • More seed capital and smaller checks than later in Europe at this time – maybe more competition higher stages
    • Thinks that consumerizing SMB software has a lot of room to grow, as well as enterprise software – easier UI-driven tools
      • Developments in AI – mentioned real-time trademark analysis (TradeMarkNow, for instance)
      • How to give great recommendations after gathering consumer tastes and insights
    • Book: The Brain That Teaches Itself – neuroplasticity of the brain
    • The first $10mln you invest, you’ll lose – advice
    • Overhyped sector: food delivery; Neglected: apply behavioral science to tech problems
    • Favorite blog/sector: longs for better content – thought TC was great when it was started, but now it’s Medium individual articles
    • Most recent investment Curious.ai – made it in 40 minutes & ambition was compelling
  • Geoffrey Batt (@geoffreysbatt), Nature of Transformational Returns (Invest Like the Best, 4/9/19)
    • Iraqi equities – history of asset classes
      • Time and patience is a big factor for how you look for results
      • Japan, for instance, 1950 – 1989 100,000% return but now is still below the peak from 1980
    • May have a decade of not working out return-wise
      • Nothing to show for investments, incredibly challenging, especially now – hedge funds or LPs
        • Reporting weekly or daily basis, long-term investment could be 6 months or 1 year – pulling money after first issue
      • Hard to ride out periods of long-term returns (think: re-rating countries)
    • LPs as agreeing to the time horizon – maybe investment committees that are making decisions on career-risk for institutions
      • Iraq to one of those – not likely to get paid off if it works, but if it doesn’t, get demoted or fired
      • Had to approach HNW and family funds – adjacency risk for people next to seed investors (if one is weighted on fund)
    • As student, he majored in psychology – said there was a guy in the seminar class that was a unique thinker
      • Daniel Cloud had co-founded a fund that invested in post-Soviet Russia before doing his PhD
      • Asked Geoffrey to come work for him and learn investment markets
    • Wanted to find next big thing – first, find a place that everyone thinks is awful (in 2007, this was Iraq)
      • Does perception meet reality? How many people dying in the war every month?
      • Country portrayed as “failed state” but oil production was increasing, CPI was 75% yoy but now 5-10%
        • Currency appreciating against the dollar, now civilian casualties in a month were down to 200 from thousands
      • Normal visit to Baghdad – mundane here, out-of-place was that 2 guys in middle of afternoon were playing tennis, kicking soccer
        • Visit companies, stock exchange, meet executives, go to a restaurant, how easy is it to hail a cab, get around
        • Critical process of infrastructure – are people paying in dollars (bad sign), local currency?
    • News media is just not set up to convey complexity to the audience – alienate, progressivism as a service, readers change
      • Experts don’t have skin in the game so they don’t face career risk – betrayal if you suggest otherwise, likely (even if true)
    • On his first trip, mentions Berkshire – early 1990s where they first invest in Wells Fargo
      • Managerial skill in banking is paramount if levered 20:1 (make 5% mistake and you’re done)
        • Don’t want average banks at great prices – want great banks at slightly unreasonable prices (thought about this in Iraq)
      • CEO of first Iraqi bank – unsecured loans to taxi drivers – less likely to take out the loan “between you and I, I’m a cowboy”
      • Entrepreneurs in these areas have to be better than others because of the instability vs the stable environments
    • Stock and capital guys – stock may trade at 3x earnings or 4-5x FCF, top and bottom lines growing at 20% per year
      • Usually just put there as knowing someone powerful but they can be bigger as allocators
      • 2008-09: Size at the time of the stock market – traded 3 days a week, 10am – 12pm on a white board
        • 60-70 companies, maybe 20 suspended from trading because 0 annual report – $1.8bn, smaller than Palestinian market
      • High growth company that is super opaque – can’t meet or won’t meet with CEO, maybe some others that state-owned enterprises that just want to keep paying salaries; maybe 5-10 companies that are investable
        • Equally weighted these companies initially and was still learning – now, he developed relationships and is on the board for companies
      • Does he want to put more $ and concentrate on the companies that he trusts and follows the guide
    • Largest holding for them – Baghdad Soft Drinks (Pepsi bottling and distribution for Iraq) – mixed-owned enterprise in the 90s
      • Local businessman (Pepsi is the dominating market share – ~70-80% vs elsewhere where it’s split) saw it was mismanaged
      • By 2008-09, were going to default on the loan they were floated – businessman bought the loan, fired management & 2000 ee’s, switched equity
        • Within a year, it was fully certified, tripled production, profitable business
    • Now, 5 days a week but still 2 days a week – had foreign investor interest, $5-6bn, couple IPOs successful
      • One telecom has about $1bn market cap, 14.5% dividend yield, 33%+ FCF yield
      • Foreign money came in 2013 but ISIS scared them off – coming back and interest from banks – arranging trips and momentum
      • Key problem – no 3rd party custodians (compared to Jamaica or even in Africa, HSBC or anything) – working on getting one
        • Makes it difficult to bring in foreign money – exchange is the custodian (which is actually safe)
        • No margins, cash-based market and settlements of t+0, no short selling (can’t sell unless have stock)
      • Oil collapse depressed prices and ISIS issue but has been up over last 2-3+ years, cheaper today than when he invested 11 years ago
    • Multiple expansion is the question for returns – 1x to 25x or 4x to 15x – depends on what they are as compounding
    • Kobe Bryant complimented him after a junior year game in high school – already looked at as a superstar – saw Geoffrey was dejected
  • Nikos Moraitakis (@moraitakis), Founder, CEO Workable (20min VC FF024)
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    • VP of BD at Upstream, enterprise sales in 40 countries in 4 continents
    • Wanted to start a company with type of what they wanted – centered around product, engineering company, starting in Greece
    • SMB over Fortune 500 because of product-focused and not corporation or enterprise-based
    • Applicant-tracking systems (ATS) – keep track of arcane process, don’t want to touch things – collaborative processes
      • Simple interface to solve problem of recruiting model (which is still 50+ years old)
    • Problems aren’t location-based – they’re conceptual – designing product, PMF
      • Opening with $500k or 50-200k if needing to get started, not necessarily $5-10mln like other markets or tech hubs, industry
      • Hiring engineers that are good in Greece isn’t so much a problem if you have a good company and network to attract talent
      • Moved to US not for VCs (doing good business, VCs pay attention) but they had 50% of customers without having anyone in US
        • Wanted to start customer support infrastructure, services and otherwise
        • Talked about marketing or accounting businesses on tech that taught companies that it may be worth it to update
    • Metrics that he pays attention to: month-to-month above $2mln annual revenue, ratio of new biz and lost biz
      • Not celebrating fundraising – few drinks but says it’s like having new shoes at the start of a marathon
      • Talking about investors and the relationships built to work together
  • Jaz Banga (@jsbanga), cofounder, CEO of Airspace Systems (Wharton XM)
    airspace-metal-crest2x

    • Meeting Earl, cofounder and maker, at Burning Man – drone being annoying above him while he did tai-chi
    • Prototyping the interceptor drones after seeing military request proposals – had an issue with drone over nuclear subs
    • Can place thermal scans and other security or rescue methods
  • The Transformer (Data Skeptic 5/3/19)
    • Encoder/decoder architecture of vector embeddings word2vec into a more contextual use case
    • Keyword lookup may not work (using ex of bank – river bank vs financial)
      • Humans at 95%+ accuracy, computer maybe around 50%
      • Encoding as correct interpretations and weights for context – emulating process
  • Laura Edell (@laura_e_edell) from MS Build 2019, NCAA predictions on Spark (Data Skeptic 5/9/19)
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    • Been at Microsoft for 3 years, supporting Azure for all their customers – quantum physics and statistics background from school
    • ML in cloud for customers – don’t know why they want it, just think it will be useful
      • Ex: image recognition (on techs with bodycams), HVAC documentation and augmentation or signal processing in anomalies of wave patterns
        • holoLENS use case
      • Played 2 sounds for Build presentation – her son blowing on her arm vs HVAC system custom sound – training sets and transfer learning
    • Can take a few real image and do a bunch for training: rotating, zoom, Gaussian blur, cut out background
      • Sound – same: take out environment, pauses and silence
      • Can turn 10 images / sounds into 30-50 per class
    • Active learning: model over time that can train itself and then retrain itself
    • Business domain expertise – her Final 4 model
      • #1 feature was wins, 2nd SoS, 3rd home court advantage / location – let machine validate the expertise
      • Validation of revenue drivers from machine – more importantly, if the opposite occurs – revenue doesn’t agree with data features
        • Used statistics to train data from the start in football, sports data – ncaa – teams, tourneys, prior history
          • Brought in her assumption of player-social index where they scraped sentiment and video analysis for team effects
        • Chose to use Azure Databricks (Spark background), store it in Blob and only retraining on Just-in-time in a Docker image
      • ML Flow, set of score for model – training set and .py and score.py or source data gets grouped together in Azure
        • Docker pulls them together easily and image is built, Azure DevOps can do VC

Thinking Through Data (Notes from May 13 – 19, 2019) June 5, 2019

Posted by Anthony in Automation, Blockchain, Digital, education, experience, Founders, global, Hiring, Leadership, medicine, questions, social, Strategy, Uncategorized.
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This week we had a hefty dose of medicine, biotech and data discussions. However, it isn’t just focused on the technology and innovation for these incredible people, but rather the frameworks of the thought process that goes in to their decision-making. We can ask “Where did we go wrong?” or “What assumption do we make that should/would be challenged?”

Annie Duke is excellent in assessing this on the spot, but she sheds light on the steps she takes to get to that level. Much of what we believe / view tends to be outcome-based, which is fine because that’s easiest to see. However, it’s rarely consistent in the path to get to that success point once we change the problem. That’s what we should focus on – consistent thought processes to make strategic decisions. Outcomes are one metric of measuring the decisions, but not an end-all, be-all.

AI mixes with a few of our guests, including Mir Imran and Eric Topol. How does it play a part in how we will progress? What may AI teach us about our historical research? Are there more optimal methods of delivery?

Emily Oster and Hanne Tidnam discussed societal effects of technology and decision-making. With technology becoming more and more ingrained as a part of everyday life, is there a point-of-no-return or is it okay generally? Or, is there a limit? Time will certainly tell – studies / surveys don’t have the information available to give us any insight since the time-scale is so recently biased. Technology has outpaced the level at which we can make data-driven decisions in this manner.

Bruno Goncalves discussed text mapping and language processing for how different dialects of the same language can indicate demographic patterns in various locations (focused initially in Brazil). He mentioned it was interesting to track words / phrases and how they changed throughout history for English, primarily – harder to track Spanish. If there’s more data available (say, mobile text / audio or email?), may be easier to break down by parts of towns/cities, but currently, it was limited to general, larger blocks based on Twitter text (which is sparse, generally, anyway).

Suranga of Balderton Capital discussed his movement from London to San Francisco and what he’d observed as a tech employee, then executive and founder and the difference between the environments. His excitement for the future comes from the societal change that infrastructure of technology may enable. Then there was a darker side when we heard the author of Ghost Work – Mary Gray – as she said there is a larger split of work designed for specificity as we improve the AI / ML models that have been deployed. What is needed work-wise or what can be automated? Are those good? Biased? We’ll see.

I hope you enjoy my notes and hopefully you’ll check out a few that sound interesting! Thanks again to the people that continue to keep me up to date with what I love to learn.

  • Eric Topol (@EricTopol), author of Deep Medicine, cardiologist (Wharton XM)
    deep-medicine-cover
  • Deep AI and medicine – asking how to properly apply deep learning or AI to medicine
  • Yet to have a drug made by AI
  • Bayesian principles failing in medicine community
    • 12% of women get breast cancer, yet we ask them to do 1-2 years mammograms
      • 10 year span, 60% will get a false positive (yikes!)
    • Adopters of Apple Watch and its cardiogram / heart information are treated unilaterally
      • Young, healthy and curious people may not have any arrythmia so any abnormality (totally normal) may be misdiagnosed
        • Signal from heart rhythm from one person may be different from another
    • Says it’s a sign of being behind (3rd industrial vs 4th for the rest) when Stethoscope (invented 200+ years ago) is still sign of industry
      • Analog, no option of recording and is still very subjective to the doctor listening
      • This, despite advances in imaging and scans otherwise
  • Mir Imran, Chairman & CEO of Rani Therapeutics (Bay Area Ventures)
    21bf244196ea49469935c6e51910eec6

    • Talked about the big equity stake his fund took in the company, wanting it to be a big hit
    • Drug development, say, an insulin pill compared to injections (spent 150+ years on solving this)
      • Stomach / pill form in past is only 0.5% or < 1 % efficacy – can’t intake that many pills or cost-effective
    • Designing pill that is pH-shell-dependent to identify intestine pain receptors are limited (can inject no problem)
      • Pill recognizes pH change by dissolving outer shell (to pass thru stomach), then sugar-needle injects
      • 1000s of animal trials and just passing pills
      • Started trials ~18 months ago in Australia for pill traveling (using x-rays every 30 min to track)
        • Then progressed into drug for giantism, along with looking at other biologic diseases / solutions
    • Don’t want to limit biologics to a single buyer (Novartis, Genentech, etc…) – keep it open
      • Investments from Novartis and Google initially ~$150mln worth now
  • Mapping Dialects with Twitter Data (Data Skeptic 4/26/2019)
    • With Bruno Goncalves about work studying language – now @ Morgan Stanley
    • Started with research in CS and Physics, moved eventually to Apache weblogs, email, big router logs, Twitter conversations to study human behavior
      • Turned into looking at Twitter check-ins with the log using longitude and latitude
      • Language used: order maps, areas dominated by specific language (drawing boundary between French and Belgian in Belgium, for instance)
        • Intermingling cities that attract many languages
    • Spanish changing from one areas to another – everyday words, phrases
      • Can use the location data to determine the area of dialects – splitting Brazil, for instance (South, North and then central American)
      • Dividing grid cells into km x km – maybe not determinate of gradients of English vs Spanish since they were testing dialects of Spanish
    • Each row corresponds to each cell and words, but the matrix essentially loses the meaning
      • Ran PCA analysis and K-means on the clustering
    • He’s gathered 10 tb of data from Twitter, corpora and looking at millions of tweets – too few data to look over time
      • Measuring language changes over time was difficult for Spanish, but easier with English
      • Used Google books, for each language and counting bi-grams in how many books – popularity of words
        • Corpus of books published in UK vs US for Google books (1800 – 2010) for reliable data, but further back was less popular
        • Could normalize for words on how American vs British words were (and the mixture)
    • Recently, looking at demographic splits of language now with more digital / online presence
    • Training spellcheck based on dialect or demographic splits
    • Doing this stuff part-time now – how to train a model to detect use of language in this sense
      • Using word embeddings to detect automatic meanings of slang to determine the different meanings
  • Matt Turck with Plaid co-founder, Podcast
  • CSR in Gaming Industry,  (Wharton XM)
    • Discussing how it can be weird to gain competitive advantage and share
    • CSR as large subjective to the person looking in
    • Gaming companies chip in to gambling addiction hotlines / help / etc
    • Particularly in Las Vegas / NV, CSR survey determined that direct opportunities in water conservation, energy and green energy
      • How can they more efficiently run such large operations
    • Survey had about 80% of the gaming industry represented, from servicers to manufacturers to casinos themselves
  • Suranga Chandratillake, GP at Balderton Capital (20min VC 090)
    balderton-capital-logo-1

    • Founded blinkx, intelligent search engine for video/audio content in Cambridge in 2004
      • Lead company for 8 years as CEO through journey to SF, building a profitable biz and going public in London
    • Early ee at Autonomy Corp, engineer in R&D and then CTO when he went to SF
    • Belief in technical person shouldn’t be CEO – idea has been replaced in US (probably split)
      • Jack Dorsey, Zuckerberg (went into Dorsey and the idea 2 companies that aren’t really overlapping)
    • Been impressed by both Gates and Zuckerberg
      • Knowing the code and going through to understand everything
      • Zuckerberg’s acquisition of Instagram (ridiculed for $12bn and how right he was – surpassed Twitter’s users)
    • 2 Things for Tech going forward – changing infrastructure
      • VR and including drones or autonomous vehicles – hardware progression and mobile phones for what CAN be done
      • Societal difference – how we change to gig economy or how we work and spend time each day
    • Changing investments – just announced investment in Curious.ai
      • Shorter term where society is changing (eg: Nutmeg for planning pension plans, cheaper and available)
    • Structure for Balderton as equal partner VC – not sure it’s the most efficient model, but thinks it’s the best
      • Equal partnership where the partners have all the same number of votes, compensation and identical split
        • Removes the politics and impacts the partners and the stakeholders (founders that may be affected)
      • No single group that can carry the vote – hiring becomes very difficult because you need an equal
    • Knowing when to stick and when to switch
      • Deciding when to say “We don’t do that” and pivoting and being right
    • Exciting company for investment that he’s done recently – Cloud9
      • All development being done in the cloud now
  • Nick Leschly, CEO Bluebird Bio (Wharton XM)
    bluebird-bio

    • Purpose Built, 5/14/19

 

 

  • A Guide to Making Decisions (a16z 5/12/19)
    • Emily Oster, Hanne Tidnam discussion
    • Health and personal spaces – “right” thing isn’t a difference for some
      • Cocoa crispies, eggs and the preference for why you eat them, not just health-related
    • Impact of breast feeding vs not and looking against obesity
      • Less likely to be obese if breast feeding
      • Random trial for this is from Belarus in the 1990s where they requested parents to some breast fed
      • Siblings in choice to breast feed – almost no correlation if they did or did not
    • Constrained optimization – money or time
      • As parent, making choices based on preferences but have constraints
      • Recency bias for current literature – only data will be problematic and that it can only support a relation
    • Regression discontinuity as drawing a line and then seeing interventions and how they may change or affect outcomes
    • Screen time as having evidence that’s just very poor
      • Not easy to run randomized trial on – iPad or lifestyle would have to change for trial
      • Apps in short term doesn’t have the info available for results / outcomes
      • Studies currently don’t have any conclusions on phones/tablets – little
      • Bayesian updating on uncertainty – if 2 year old spends 50% of waking hours, that’s bad. Where’s the limit?
    • Last time went to doctor – very concretely made a recommendation of “2 hours max in a day”
      • How does the information get to that level of the system?
      • Conversations occur with doctors where they essentially make the lines that stick, pass on
    • Recommendations go out as arbitrary but now are made as truth – terrible without the decision
      • People that take the recommendations are likely more different or health-conscious
        • Added another layer (vitamin E, for instance) – those that adopted did many other things
    • Just be wrong with confidence – can be right, lots of good options
  • Innovating in Bets (a16z 5/8/19)
    • Annie Duke (@AnnieDuke), Pmarca, smc90
      annie-duke-thinking-in-bets-199x300
    • Every organization and individual makes countless decisions every day, under conditions of uncertainty.
    • Thought experiment: posing Seahawks run the ball and are stopped vs passing and throwing pick
      • Once there’s a result, it’s very difficult to work backwards to assess the decision quality was.
        • Outcome was so bad – it was in the skill bucket. Or – oh, there was uncertainty.
      • Very slow in NFL, for instance, to adopt analytics according to the numbers
      • 4th and 1 – should unanimously go for it since numbers say the other team will get 3, expected
    • As a decision maker, we likely choose the route that keeps us from getting yelled at
      • We allow uncertainty to bubble to surface – conflicted interest – long run vs short term
      • 2×2 matrix of consensus vs nonconsensus and right vs wrong
        • CR fine, NC-R genius, CW – fine (all agreed), NC – W really bad
        • Outcomes are right and wrong – hard to swipe the outcomes away
      • Thinking or allowing uncertainty in human driving and killing a human vs autonomous vehicle
        • Black box not understanding autonomous vs THINKING understanding another human (“Didn’t MEAN to do it”)
    • Anybody in business – process, process, process
      • R/E business – everybody in room after appraisal is 10% lower vs 10% higher (similar analysis)
      • Outcomes come from good process – try to align that an individual’s risk matches with corporation’s
        • Rightest risk – model could be correct (tail result) or risk decision (and deploying resources)
    • Most companies don’t have SITG – how to drive accountability in a process-driven environment so results matter?
      • How do you create balance so outcome caring about is the quality of forecast?
        • State the model of the world, places and what you think. How close are you to the forecast?
      • When you have a bad outcome and you’re in the room?
        • How many times do people say “Should we have lost more? Should we have lost less?”
      • Learning loss – negative direction on how to figure out? Poker example of betting X, X-C or X+C
        • Bet X and get a quick call. Should’ve done X+C (learn, regardless of opposite getting a card)
    • How much can you move an individual to train thinking? Naturally, thinking in forecasts more. Will have reaction and lessons quicker.
      • Getting through facts quickly and having the negative feedback – not robots
      • Improved 2% which is amazing – what are YOU doing to not make it worse?
      • “Results-oriented” as one of worst for intellectual work – need a process.
    • Story of “something is happening” is not a good story. Hard to read journalism now for him because they’re both “non-consensus”.
      • She’s optimistic that people can be equipped to parse narrative to be more rational. Pessimistic of framing and storytelling currently.
      • “Not making a decision” is making a decision but we don’t think of it that way.
        • Really unhappy in a position. Did the time frame thing “Will you be annoyed in 1 year?” – Yes. Nondecision didn’t feel like decision.
      • Mentions not having kids – time, energy, heart and decisions associated with indecision.
    • On individual decision, you have: clear misses, near-misses, clear hits
      • Bias toward missing – don’t want to stick neck out. Have to see it in aggregate. Forecasts.
        • Anti-portfolio / shadow book is really about when you include clear misses vs near-misses.
        • Fear is that the ones that hit are less volatile or less risky will be returning less than shadow portfolio.
      • Says it’s difficult to do with 99:1 turndown vs investments. Sampling.
        • Time traveling with portfolio – bounce out and see if “if this is 1 of 20 in portfolio” vs “invest vs not”
    • Conveying confidence vs certainty
      • I’ve done my analysis. This is my forecast. Is there some piece of information I can find out that would change my forecast?
        • Bake it into the decision. Not modulating the forecast – 60% to 57%. Costs and time differences.
      • Putting confidence interval on earliest dates and another probability, inclusive, on latest day
        • “I can have it to you on Friday 67% of time and by Monday 95% of time.”
        • Terrible to ask “Am I sure?” or others “Are you sure” compared to “How sure are you?”
    • Pre-mortems in decision analysis
      • Positive fantasizing vs negative fantasizing (30% closer to success by thinking of hurdles in front of you)
      • What happens if you ask out crush and they say yes? What happens if you ask out crush and they say no? – No’s more likely.
      • Teams get seen as naysayers – individually write a narrative on pre-mortem – good team-player is how do you fail?
      • After outcome, we overweight regret – don’t need to improve this much. Look out a year and see if it affects you.
  • Mary Gray, author of Ghost Work: How to Stop SV from Building a New Global Underclass (Wharton XM)
    51zsewdgdol._sx331_bo1204203200_

    • Research with coauthor at Microsoft Institute
    • Discussion of the b2b services that run in the background – used Uber’s partner as example for driver verification ID
      • Beard vs no beard on default, for instance – need close to 100% accuracy and algorithms/vision can’t pick that up just yet
    • Humans that run mechanical turk or various tasks on classifications
    • Technology used to be cat vs dog and now it’s species of dog – likely tech will enable more specific classifications but not remove intervention
      • Running with surveys, captioning, translations, transcription, verifying location, beta testing are all tasks
    • Used another example for “chick flick” meaning or 2012 debate with Romney’s “binder full of women” comment
      • Needed humans to assign relevance and to provide or connect proper context (Twitter, for instance)
    • Facebook and its content moderators, most social media companies do this
  • Trends in Blockchain Computing, (A16z, 5/18/19)
    • Get paid for AI data or encrypted data – can still train a model on it but nobody would know exactly what the data is
      • Long term accrual – depends, also, on if it’s on AWS vs open-source
    • Gold farming and ex-protocol websites in WoW, for instance – virtual worlds

Leadership: Data and Strategy (Notes from Week of May 6 – 12, 2019) May 30, 2019

Posted by Anthony in Automation, Digital, experience, finance, global, Leadership, questions, social, Strategy, Uncategorized, WomenInWork.
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This was a very fun week for listening. I caught a ton of material and insights from very creative leaders on how they’ve looked at strategy and building great teams, align companies and progress in a successful manner. Their methodologies or frameworks didn’t always align, which was also refreshing to hear. We often get stuck on the same methods if we hear them repeatedly – I’m under the impression that this becomes dangerous if held as a truism when others may question this way or go away from it – there isn’t a one-size-fits-all to building businesses! Especially when information is plentiful, and people / ideas are a few clicks away.

Zeke Zelker is a super-creator who has pushed the envelope of creating and producing engaging videos, whether it’s marketing material or tv or films. Definitely worth checking out, especially as media content in video / audio has increasingly been the mode of consumption.
Benito Cachinero of the Egon Zehnder Leadership program talked about 4 things he looks for and teaches in leadership. Also, he covered the importance of how to strategically allocate resources when looking for growth or expansion, both in economic and human capital.
A CES overview of consumer tech trends by the a16z squad was intriguing! Alarms, smart home and other products that caught their eye as options to drive the future of homes and how we’ll interact on an everyday basis.

Caryn Seidman-Becker  discussed privacy of data and personal biometrics as the CEO of CLEAR – trying to improve the ease of security while fighting the image that people think of when hearing what it is they do to enable this. Brett Hurt was fascinating – building multiple companies early in life and calling up his friends to start a new one. Deciding the name? Hilarious. All of this before getting into Clarabridge’s sentiment analysis with Ellen Loeshelle. She realized how much different types and ways to look at data / text could help in analyzing a plurality of business cases, across many industries – not just customer data for their clients. Last but certainly not least, Stephanie Cohen needs no introduction – but she discussed how she perceives data for company progress and leading the groups needed to achieve her goals.

I hope you all enjoy!

  • Zeke Zelker, Creative Transmedia Branding (Wharton XM)
    • Talks about his films being cine-experiences, and as a DIY videographer how he can control all aspects
      • Considers business along with creative direction – makes sure to align them
    • 4 phases of content: ignite, sharing short form content, main event, reward
    • iDreamMachine – his prod co. & producing Billboard about 4 people living on a billboard
    • Has 7th most viewed drama on Hulu (InSearchOf) – engaging audiences to become a part of the story
    • Encourages people in the environment to create content, whether it’s a blog or short snippets – become part of the overall story
  • Chris Carosa, author of From Cradle to Retirement: Child IRA (Wharton XM)
    51ip9g4cewl._sx331_bo1204203200_
  • Making money as child, pre-teen and then teen
    • Child: golf balls selling – golf course, lemonade stands, etc
    • Preteen: babysitting, lawn mowing, card collecting
    • Teen: W2 eventually, card collecting, babysitting and other types – photography, writing
    • Parents can save $ for child by investing in child IRA ~$1k or up to gift amounts of $6k now as long as W2 income

 

 

  • Benito Cachinero, Egon Zehnder Leadership Solutions (Wharton XM)
    • Previous President HR at DuPont HR
    • Discussing potential ~4 factors for leadership (direct contradiction to measurements in being accurate)
    • Aligning resources for growth strategy in new markets – China vs Midwest, for instance
  • Pulse Check on Consumer Tech Trends (a16z 1/17/2019)
    ah-logo-sm

    • With Benedict Evans, Steven Sinofsky
    • Trends at CES – no consumer product themselves, just a lot of all parts of supply chains / manufacturing
      • Batteries in 10k – 100k, so know what you want
    • TVs were not curved (nobody bought curved) – had 3 ft bar and tv came out above it in 10-15sec
      • Or edgeless Samsung blocks “The Wall” where you could make them as large as you wanted – LCD in any shape/size
      • Sizes could be anything now, amortized supply chain and manufacturing plants vs idling
    • Media content providers and apps
      • Pausing / syncing and Samsung apps with Apple video – clunky or AirPlay hardware
      • NorCal vs SoCal or California vs other states (think Apple phone vs the rest)
    • Easiest product to get alarm from 12+ companies for an hour to plug stuff in and it’s done
      • Proprietary electrical wires until they got low energy Bluetooth and now it’s everywhere
      • Lock or other nuts and bolts having SKU proliferation or new homes
        • Have to know gen contractors, Home Depot, developers and fragmented
    • FirstAlert smoke alarms – mesh wifi since they’re hardwired or battery
      • Put wifi in the alarm (to go to phone, etc…) – lots to do it with insurance or risks
    • Alexa chip supplier to connect everything
      • Apple tried to do Home Kit but eliminated everyone because almost nothing was implemented – wasn’t easy
      • Amazon has leverage for hardware but it has to benefit them for Alexa and being useful
      • If all makers saw HomeKit, could join war for Alexa vs Assistant (now that everything has their discovery appliances / connect)
      • Compare electric toaster to holding bread in front of fire and similar progressions
    • Show about running experiments is CES vs show about finding business value
    • Cultural part of CES – Japanese hand clapper
      • The founder of Ukrainian and employee and others that were hustling
  • Caryn Seidman-Becker, CEO of Clear (Wharton XM)
    kc6vuppj

    • Biometrics and buying Clear
    • Talking about the reactions for people getting to skip lines – make it more efficient
    • Allow TSA agents to work on what is actually important
    • Trying to describe to change customer behavior in the privacy aspect of what they keep
    • Biometric data but encrypted and secure

 

  • Brett Hurt (@databrett), co-founder and CEO of data.world (Wharton XM)
    logo594x144

    • Discussion of Edgecase (Compare Metrics), BazaarVoice (running backend for a lot of ads)
    • How they stumbled onto the data.world site – calling his co-founders of next big idea
  • Rod Hochman, MD and President, CEO of Providence St Joseph Health Leadership (Leadership in Action, WhartonXM)
    • Talking about being a junior member of the board of physicians when he started out
    • Leadership and how he went into the administration side as a young physician
    • Administration for many physicians is beyond the time / scope of many – hard to think of it without doing MBA or taking time
      • Important to combine the two for the expertise and management
    • With this business, how much is going on between clinics or hospitals and the network
  • Sentiment Analysis, Ellen Loeshelle, Dir. of Prod Management at Clarabridge (Data Skeptic 4/20/19)
    clarabridge-analytics

    • How positive or negative a customer may be expressing a review or otherwise, polarity
    • Academically, may have entire text as positive vs negative
    • Clarabridge – helping their clients understand their own customers
      • With hotel experience: could be multiple levels – service, cleanliness, check-in, overall
      • Using a clause, individual tokens, lemmas, parts of speech and how they’re related
    • Dealing with 21 languages natively, and having computational linguists on staff to understand the grammatical syntax or individual contractors
      • Vocabulary can change, but not necessarily syntax (think: sick)
    • Sentiment is rules-based engine as BI tool for her end users – full control / transparency for analysis
      • ML in place with w2vec for tuning rules in the engine since those change based on context/industry
    • Flipping sentiment or negating and modifiers as using the extreme ends of sentiment analysis (-5 to 5 scale)
      • Structured stays similar, but lexicon changes contextually and sarcasm / transcriptions as more difficult unless obvious or explicit
    • Sentiment goes along with their emotion or effort analysis for customers
      • Enterprise tool and APIs for engine on enriching internal systems
      • Considering sentiment analysis as table stakes now – different than when they first started when they were ahead
    • Client in small kitchen appliances used Clarabridge to treat sentiment on competitors, specifically for pressure cookers
      • Eventually saw that the sentiment split for pressure cookers and that pushed them into doing Instapot
  • Stephanie Cohen, Evolution of M&A and Corporate Strategy (a16z 5/7/2019)
    A Goldman Sachs sign is displayed inside the company's post on the floor of the NYSE in New York

    • Stephanie – CSO for Goldman, member of management committee, Was in M&A and investment banking for Goldman
    • M&A is experience-based business, M&A with same people – rarely would be one-and-done – just a method of executing strategy
    • Bad examples of M&A – likely hard to keep up with growth or expectation of growth but tries to buy the growth
    • Her worst example: Fiat Chrysler with government owing, Canada + US and pension
    • Trends: velocity of M&A is greater (cited $1tn of M&A last quarter), amount of private equity has about $1.5tn for buyers (divest vs sell)
      • When she started, needed a strategic buyer – now, just need to provide an answer to how the business is a good alone activity
      • China / Asia as higher volume in general
    • “Anti-trends”: still very analog as M&A, person-to-person; continued evolution may come with digital capabilities
    • Preparing or thinking of selling:
      • Don’t wait too long to sell (assets no longer strategic, more the business will atrophy) – be proactive of business portfolio
      • Build relationships early on with financial or strategic buyers
    • Best M&A banker he’s seen: Tim Ingrassia (analyst originally) – corporations, legal, bankers
      • Friendly, relationships and doing business without ‘playbook’
    • Figuring out strategy, which companies you want to buy and the alternatives (top targets, organic version, next a, b, c plan)
      • What to pay, rumors of what others would pay – what’s it cost to you?
      • Thinking creatively about deal? How to design the right compensation packages? What’s the integration strategy?
      • Clients are thinking of deal with integration people and how to get synergies to work the best
      • “Charm offensive” – ultimately, most sellers make decision on valuation – if you’ve developed best relationships, you get other information
      • Walk-away price
    • Top down vs bottom up strategy – mentions $CAT as shifting toward RR vs sales, and not unique to just financial services
      • Not one instead of the other (fee-based vs recurring) – good deposits bring in other clients
        • Creating and building business with the right economy within various parts of the business
      • Going forward, people running businesses everyday have the best idea of how to exploit markets
        • If client-focused and outwardly-focused, should come up with great ideas together and to push forward
      • Exploration is hard or unnatural – high-energy, client-driven and solving in a creative way
        • Creative and quiet thoughts – leaders need time away, but people have to be exploratory to consider new plans
        • Example for tech and how to have the right conversations based on seeing what other companies are doing
        • She says that with how fast tech is growing, they need to work together and partner
    • Accelerate as trying to push new ideas
      • Committal vs part-time – allowed them to fund and go with their idea, or keep them head of board
    • Belief in fintech for a huge opportunity – have tended to build things on their own, but have pivoted to not do everything
      • What should we buy? What should we build? Want fintech to come and partner with Goldman.
      • Most of life is on phone and it’s almost seamless – but not financial life for mobile
  • Ruth Zukerman, Co-founder of FlyWheel Sports and SoulCycle (Wharton XM)
    • Creative director

Dissecting the B2B and B2C Models(Notes from April 29 to May 5) May 23, 2019

Posted by Anthony in Automation, Digital, education, experience, finance, Founders, Hiring, questions, social, Strategy, training, Uncategorized, WomenInWork.
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Here we see a few longer episodes to discuss investing into different biz models. I listened to a collection of founders that started funds, did a bunch of investing, bet on themselves, worked hard and ultimately caught a few breaks in spaces that were extremely unconventional and some that weren’t.

Keith Rabois – if you don’t know who he is, I’d strongly suggest looking up some of his work – went over the differences he sees as an operator compared to being an investor. What do you want to focus on in each position and what competitors to focus on.
David Frankel is another current MP who started by building and exiting his own companies. His discussion focused on how he tries to align founders and investors at the early stages of start-ups – how he frames this to be most productive. His secret though: following the founders, themselves, and trusting they have ideas that can carry ideas.
Another MP at Founder Collective is Eric Paley – though he came from the biotech space before joining.

Amy Frederickson was a fantastic listen on the Business Radio channel. Taking second hand, vintage items and giving access to others who see beauty in them. She connects contractors in upholstery and sourcing furniture that may not be seen for what it could be and connects them with those that may have great use. I’ve heard similar stories to this being done in the second-hand space like boutiques or even goodwill – connecting foot-traffic-primary stores to the internet and allowing everyone a chance at these creative, hand-picked items.

Angela Bassa was on DataFramed talking about her managerial experience in the exploding data science field (and before). How she’s had to adapt, how she treats peers and effectively communicates – all very useful in discussion of solving the right problems in an organization.
This was a great segue for me into another a16z episode on decision-making. How to ask the right questions and ensure you get a sufficient answer. Mining the proper data for it and turn those into insights.

To finish up and not make the intro super long – I kept notes on a few other investment managers and the differences in strategies for different clients, investors and the framework for posing performance in the right light.

I hope you enjoy the notes and please check out everyone’s episodes!

  • Keith Rabois (@rabois), Inv Partner at Khosla Ventures – If You Can’t Sell Them, Compete With Them (Invest Like The Best – 12/18/18)
    investors-khosla

    • Investor, entrepreneur at Paypal, Square – investments in AirBnB & Palantir, Opendoor
    • Paul Graham’s “Clients too stuck in their ways, compete with them” – when a person doesn’t want to take advantage of tech
      • Creating money, vertically-integrated business – build the platform (adoption risk, sales cycles, economic issues for being reliable on others)
        • Provide end product to customers
      • Quintessential example as Apple – control component to create user experience – derivative product doesn’t control own fate
    • 7 Powers – book for strategic leverage
    • Irrational for the 2 guys in the garage – has to be unexpected reaction to market, team, etc — but can’t take over from scratch by following ‘playbook’
      • He’s in the business for investing in a top 100 company
      • Strategic leverage should be that the accumulated advantage should be easier – skill / talent ability normally degrades
      • Anomalies give you insight into a paradigm shift (can’t get 10x growth from UI, etc…) – end of why questions should be incrementalism
    • Secret is a belief system about the world that others don’t appreciate it – time determines if it’s true or false
    • Home as primary home as more a commodity than an art – touch/feel. Not works of art.
      • Focuses on digital health, where data is abundant. Network effect there.
      • References – take Opendoor – could make an offer for house that’s fair but you’d be uncertain with money or closing on time
        • Knowing people will make the credibility factor easier – trust matters by vertical/industry
    • Paypal had a $100k guarantee for the $ – partnered with Traveler’s Insurance as trusted brand, and used FDIC for insurance up to $100k
    • Healthcare costs ex-LASIK seem to be going up – mediated through payers (ins co’s)
      • Can improve UX, reduce cost and improve quality at same time with technology enabled – Guardant Health with liquid biopsy
        • Made it significantly cheaper to get biopsy results
    • Giving founders feedback on what they’re doing – how are they liking what they’re doing
      • As an operator, needs to make a 70% conviction and decision (as an investor, he needs to be in the 10-50% suggestions)
      • Assessment of talent is similar and understanding tradeoffs may be both of operator/investor
      • Risk profiles are different – understanding as operator where the strategy changes over time for the company (investor may be 0x to 10x)
      • Investor gets paid to learn new things, try new things – much more like baseball
        • Operator, conversely, may be like football where you should try things but need buy-in from others with your team
    • Lean start-up as stupid idea – cohesive strong strategy that can be done with less capital
      • Product-market fit isn’t required for validating fixing an idea / postulation (fat start-up – $10mil to fix real estate, for instance)
    • Steve Jobs mentioning saying No to good ideas (10% ideas) vs the 10x ideas – need experiment to get to that capability
      • Bad ideas in venture: lots of failures – 30% in baseball is good
      • Why does nobody emulate Apple or other successful companies do? Avoid the failure mentality.
        • Obsession about design and practical thinking – not empirical thinking. Book: Creative Selection
    • Interview question:
      • If you are a product, how would you describe your value proposition? – initially had product instincts – wasn’t world class, but knew business
    • Founders want to affect the real world – computer was escapist initially, but now it’s a controller for the real world
      • New capabilities / opportunities, lots of people leverage that for positive behavior, so now he says there are more ‘hard science’ innovation
        • Healthcare, biotech, autonomous, etc…
      • Early stage, pricing matters less because you just need to be correct directionally for the company, not so much off
        • How much, though, is risked by industries or risk/reward – what’s on table?
        • Later stage matters more for balancing portfolio.
    • Learning through osmosis with someone that’s very smart
      • Calling people to get feedback on certain ventures based on other talented people’s responses
    • Is there high-growth startup ex that hit escape velocity that a large competitor has beat?
      • Being paranoid is smart, but focused and talented team will out-execute a large entity
    • Narrative violations – common being fake news – average American is more informed than any other American in history
      • Average American is more informed than any other person in history, by orders of magnitude
      • Interesting question: given the resources, is the person smarter or dumber than what they used to be? Voter more/less informed?
        • Accessibility to products is so abundant now – anyone can Google or find other information
          • Definitely true in the US, maybe harder for other areas in world
      • Platforms are now more democratic versions of printing presses
    • Different components to acquiring and learning skills (athletes as needing to do, guitar probably playing songs, surgeon both reading/dexterity)
    • Most investors forget the lessons of strategy, he thinks – differentiation is your friend (mentions YC as having different mentality, economics)
      • Not much pioneering at VC level – Horowitz (and his autobio) initially, but not much innovation since – Khosla, Lux as vertical integration, maybe
      • Midlevel manager of engineering can be efficient from recruiting standpoint – what level you’re at, where you can pick from 350 companies (to 10)
    • Upside of Stress – book that’s very important, he believes — more stress and tolerating it is how you can be more successful
    • Things that stick with him – how they remember how others impacted him or vice versa, little things
      • Cascading of good/inspiration & how it changed trajectory – rewarding
  • David Frankel (@dafrankel), MP at Founder Collective (20min VC 088)
    f9bf1622-04df-11e7-a9d6-0242ac110003.founder-collective-logo-black-tinypng

    • Founder, CEO of Internet Solutions (ISP provider in Africa) then became a super angel
    • Founded FC with his partners for seed stage investing
    • Graduated 1992 in Elec Eng undergrad – IS acquired eventually for $3bn in mid-90s
      • Was doing ventures with FCF, made many huge mistakes, he said and remained on Board for acquiring co
      • Went to HBS – hated first 3 months but then graduated in 1993 – b2banking, b2consulting (jokingly) – but met a ton of great people
      • With capital, could back almost all of his classmates starting (first or entire checks) – had 27 companies after graduating
    • Challenge for FC was to ask how to institutionalize seed stage investing?
      • Not a lifecycle fund, just seed stage – may follow in Series A (not as lead), but hadn’t through the first stage
      • Wanted to create most aligned fund with founders – not net buyer when company is net seller
      • Believe they can make the most difference up front but not as they go forward
        • Who can be first hires? Management team gelling?
        • 2 years in, he says, they become much less useful – happy to be on board or pull off
    • Working with Chris (over at a16z) – says it’s a waste of time to look at incremental
      • Chris pitched 2 ideas before Site Advisor
    • For people not in the network – David loves hanging out with people and is very curious
      • People default to what we love doing – have to enjoy hanging out with them
    • Invested in Uber but he didn’t know in a million years how that would have been predicted on what they did
      • In the moment – Groupon just completed $5bn round and they were invested
        • Was excited about a competitor in Korea as he liked the founder, even though he believed it was “house of cards” industry
    • Comparing engineering student to business school – eng 1 in 1000 idea is Facebook, but volatility very high – business school lower volatility
    • Term Sheet as read blog, uses Twitter / TweetDeck to curate lists
    • Typically anti-sector because he follows founders moreso than industry specifically
    • East Coast vs West Coast (center of universe) – output of talent from Boston and east coast is different
      • Depends on types of company (consumer / mobile is Bay Area-centric) – Boston good for tech/biotech
    • One of favorite portfolio companies-PillPack in disrupting pharmacy & something simple
  • Amy Frederickson, Founder of Revitaliste (Wharton XM)
    revitaliste_3

    • Vintage furniture in interior design space – making it very simple to reupholster or otherwise refinish furniture
    • Discussion of her partners on the furniture side – volume doesn’t necessarily make it better if they can’t do more work / spend more hours
      • Limitations that she’s had to be careful – try to change mindset and buoy them
  • Eric Paley (@epaley), MP at Founder Collective (20min VC 089)
    • CEO, co-founder at Brontes Tech before acquisition by 3M for $95mil
    • Started a web developer company with brother and cousin in 1990s, had a bunch of startup clients and others that weren’t
      • Abstract Edge – still run by brother/cousin, but when the dotcom bust happened, sees overconfidence
      • Bad times – may learn better – he wanted to go to biz school & learned a ton
      • Looked at 3D imagery while in business school thru MIT partnership – interesting and looked at the space – had to ask “What to do with it?”
        • Facial recognition, industrial inspection, endoscopy, video games, etc…
        • Late in game, struggled with money raising and decided to look at dentistry (mass customization – every orthodontic device as singular/unique manufacturing, dental impression but if you could change this, you could have a lot)
    • First investor was David Frankel (from before – $500k into Brontes)
      • David calling and say “Thought the founder liked me but would you mind doing reference call with them?”
      • “Can you sit down with the entrepreneur and let me know what you think? – I’m out of town for 2 months in South Africa, so I trust you.”
      • Started to look for deal flow for David while he was out – with the other guys
    • As he was looking at leaving 3M, he was talking to venture companies and saw that top quartile VC’s didn’t feel like they were doing as well as they had
      • Came together with the 4 partners and should start a fund – underlying premise with better alignment at seed stage
      • Pro rata doesn’t align founder to venture – founders don’t get the option if they’re not doing well
        • Dollar average up cost-basis vs down. $8, 10, 15 million valuation vs $30, 50 or 100 million – but it’s more along the average dollar
          • Weighted later with pro rata investing
    • Believes there are plenty of seed funds that are doing well, but he’s surprised by the limited amount of funds that stick to seed stage
      • Conventional wisdom / FOMO for lifecycle / follow-ons
      • They have 3 unicorns at that time as well as a lot of good returns outside of that
    • Fooled By Randomness – NNT book as his favorite applicable to VC, frameworks for tilting the probability
    • Founder role model for him – said he was lucky to have Kelsey Worth, founder of Invisalign ($1bn company in 5 years)
      • She was on the board, would come out a day a month and help him out – dive deep and give an opinion without being dogmatic
    • Mentioned a recent investment as Cuvee – attempting to increase wine storage / pourability to 30 days
  • Angela Bassa, Director of D/S at iRobot (DataFramed #48 11/12/2018)
    irobot_green_logo

    • Managing D/S Teams and how to organize development of algorithms and the processes
    • Corporate business organization of data science teams vs packaging and product building or open source work – known for more of that
    • Undergrad in Math, went to Wall Street after – got a lot of data analysis in the market, wasn’t a match for her ~15 years ago
      • Then went to strategy consulting – focused on pharmaceutical strategy, testing and experiment analysis
      • Went to marketing services industry – finally saw big data – (no longer any single machine work)
    • Talked about excelling as an individual contributor and moving to management as a different discipline in itself
      • First person she managed: quit the first day, had been a PhD graduate and assumed he was working with her, not for? (What a prick?)
    • Worked with teams in ops, finance, IT, engineering, R&D, etc…
      • Re-orgs for data science portion – always changing branches
      • If data science isn’t the product, within legacy/corporation, the team needs time to figure out the objective of the organization
        • Get past exploration and become experts
        • Her take on managers would be that they create space (o-line) for individual contributors to do their work as quarterbacks
    • As teams grow in size over time (using her experience as Manager and Director from ground up), potential vs low-hanging fruit
      • High visibility and high sophistication to give a leg up on what could be possible for the organization – low-hanging fruit is easy
      • Starting data science team have generalists but very good to mature into a better team, specialization
    • Humility for data scientists – avoiding the correlation factors that you build from gathering and going through data initially
      • What kind of questions should be answered?
    • Parts of data science that you can’t teach – how vs wanting to answer questions
      • Certain bootcamps are worthy of what they teach, organize – mentioned universities as not having programs until recently
        • Mentioned a team member trained initially as marine biologist – traveled and researched pods of dolphins
          • Modeling expertise for a fleet of robots as operating independently and together
    • Harder for C-suite to not be able to talk data in the strategy sessions for decision making
      • Common pitfalls of manager:
        • Data team doesn’t know how the data is gathered or where all it’s coming from
          • Have a data party or something to organize the data creation, designed, labeled, and stored
        • Not overpromising or underpromising
          • Lend credibility to actual outcome – being honest, transparent with other disciplines to interrogate situations
    • Her paper for HBR – Managing Data Science for AI
  • The Future of Decision-Making (a16z May 1, 2019)
    • Frank Chen and Jad Naous (via YT initially) of Enterprise Investing team
    • Digital transformation where industries are shifting to this design
      • Changing from manual to automated, digital processes and more agile
      • People’s roles will start to shift around – demand for new tools and dynamics for who wins in spaces
    • Product management – features or bugs would have been surveys manually or collecting data to figure out problem and sort them all
      • Now, the tools automate these from the product itself, often – now they can look at the dashboard of numbers
    • Marketing side: Growth hacking and market engineering – low cost to increase growth in certain parts of customer segments
      • Decision-making and creative work is the human part that can’t get automated
      • More people in the middle of the enterprise are becoming analysts – BI tools aren’t going to be enough
    • Types of tools should be operational tools that give answers to questions that they need immediately
      • Where is the bottleneck in the funnel? How to eliminate?
      • Competitor is having a flash sale – how much revenue is impacted or what segment should I target?
      • Generally, analysts would have to spend time and $ to get an answer (“$10mil to get a report that you didn’t need in the first place.”)
      • A/B test has to be continually monitored
    • Jad worked at AppDynamics – one of easiest things to sell is Performance Monitoring Tools – prevent systems from going down
      • Harder to prove ROI to other orgs – sales, marketing if they need continual results / ongoing
      • Want to have self-service tools vs full-service from someone else
      • Not analysts but instead the functional operational people – marketer, growth hacker, product manager, business people
    • AirBnB already open sourced SuperSet – ad-hoc access to data for results, used by 100s co’s – presentation layer product toward technical
      • Imply (one of his investments) for analytics and processing layer – store streaming data into database and do the analytics / presentations
      • DataBricks – processing layer
      • ETL layer is the one that has not gotten traction – domain specificity (healthcare vs ride-sharing or finance)
        • Currently too much integration issues and organizing
    • 3 categories – operational intelligence – sell tools for incumbents to enable intelligence
      • Target csm or sales or product manager (crowded currently, hardest to win)
      • Segment-focused vendors – sensors and analytics to oil & gas companies, for instance
        • Vertical solutions for industry
      • Vertically-integrated, operational intelligent company that competes against incumbents – Lyft / Uber, AirBnb, etc…
        • Biggest value but hardest
    • Non-IT buyers: Grocery, Construction, Oil & Gas – operationally efficient and commoditized as long-standing business
      • Minimal change in efficiency can be a huge value (Costco at $12.5bn ’17 on 11% margin)
      • Capital deploy for Exxon Mobile ($230bn capital invested, ROIC 9.5%)
    • Particularly excited by SuperSet, Imply – infrastructure tools – people seeing analytics and tools as necessary for business
      • Software vendors into large, existing industries – hardest would be economic profiles will be very different
      • Selling into stagnant markets (minimal margin) and not used to new tech – cycles will be long
        • Huge businesses to get in
      • Need to educate/prep investors – really bright light at end of tunnel
      • Need to become experts and trusted advisors in the domain
      • Help with software and services in the industries
  • Josh Wolfe (@joshwolfe), founder/MP at Lux Capital – Tech Imperative (Invest Like Best, 4/23/2019)
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    • Tackling massive scale problems – China as infrastructure power vs the states
      • State or story-sponsored role becomes more powerful with internet-enabling
    • Checklist of 5 main things (Xander of GoPro, now SurveyMonkey)
      1. Nail down the strategy of company – what are you going to do?
      2. Deliver capital to pursue strategy – clear, cohesive and sell
      3. Brilliant team to execute, drop others to start mission.
      4. Communicate the hell out of it – partners, competitors, media, press – keep consistent answer.
      5. Hold people accountable – if people aren’t and the goals aren’t clear, not effective organization.
    • Story – memorable, easy to repeat, conveys meaning in a clever way
      • Want to elicit an emotional reaction – putting meaning in a story for an individual
      • Portable ideas as superpowers – leaders being able to harness this, or the audience (maybe of the shared values)
        • How to aggregate the ideas
    • Abundance of liquidity to illiquidity or leverage (eg $200mln check in growth-equity round at $1bn (from $100mln) but if down-round, then the check has a big stake in it as creditors)
      • LPs and endowments are overextended – he’s telling people to look at secondaries, not venture
      • Sequoia was appealing to greed – sop it up and have to write bigger and bigger checks (get a big fund and put to work)
        • SoftBank as big problem pricing up rounds – either visionaries or producing paper assets as collateral against debt
        • Tesla as horrible balance sheet and illiquidity
    • Zoom doesn’t need to need a big business, but Uber/Lyft depends on strangers and investors to buy in to future
    • TurboChef (fast like a microwave but toasty like toaster) – Subway vs Quizno for $4k ovens
      • Sell to Subway – 20k places for purchase orders – but they got Coca Cola to buy the contracts for Subway in exchange for them to be in the stores
      • LatchAccess (one of his co’s) – remote by cloud from phone to consumer
        • New build and buildings (now 1 in 10) – did contract with WalMart / Jet
    • Some firms get lucky and parlay it into success – maybe wrong in process
      • What was process? Where did you get lucky? Where were you smart? How did you structure deal?
        • Benefit you, founders, investors
      • Price vs intrinsic value – public doesn’t do this, but path-dependent in portfolio (repeat entrepreneurs)
        • Team vs sole GPs – total equal partnerships and all mixes
        • Portfolio mix, super early stage, low probability of high financing risk
        • Others who are good at metrics / business, growth metrics
        • Subsector – fintech, crypto, etc… as experts
    • Tribes with a mantra
      • “Life sucks” – gangs, people homeless
      • “My life sucks” – 9-5 and get home and just crack a beer and grow for that
      • Like what they do – “I’m great, you’re not” – silo information, zero-sum and leave as free agents
      • Lux as “We’re great, they’re not” – robbers cave – how to get people to bond vs competitor / enemy
        • Sometimes it’s an entity – exogenous threat, devil – big oil, martians
      • Ultimate “Life is great” – mission driven, maybe Google / Facebook initially – cause/effect of money
        • Still climbing mountain, goal to reach – complacency maybe
    • Judgment: should we be disciplined about price?
      • Andreesen said only 10 good companies but you want to be in each one – but there are 1000s of decisions to be made
        • Pay any price for the ‘best’ or be discriminated – lead to FOMO and price action
        • Mentioned Cruz and setting up GM deal ($20mil at $60post vs $20mil at $80post, but GM came in and paid 11x)
      • In private markets, if you rejected them, you don’t get another chance.
    • Values: observable around morality (tech around morality and morality around tech)
      • Existence of an option is a good thing – military as a hot topic, tech as both sides affected
        • Had invested in Palantir offshoot for virtual wall for Homeland – has lots of immigrants who were deeply affected
      • Drone options or even autonomous driving (say, those who die as organ donors for the donor list)
      • Compares China’s pipeline from government to technology – decisive advantage will let them be ascendant
        • Moral discussions slow this down – barriers to experimentation
      • Real value of CRISPR isn’t the feature, but what it leads to in the platforms (ex: X-Men / Cerebro – Variant for rare populations)
        • 23andMe and Ancestry as targeting the ‘boring populations’ vs what they’re doing
          • 1000 individuals for rare conditions that have a metabolic rate that raises in the evening – what if this was monogenic / targetable?
    • Sci-fi vs Sci-fact as narrowing — ‘it will rot your brain’ as doing the next $10bn+ industry
      • Mentions engineers and Fred Moul (founder of Intuitive Surgical) starting Orace – just betting on him to recruit the right people
        • $8mn at $20mln valuation – for 5 years $90mln forecast and $450mln – then got a bunch of investment)
          • Exit for 63x for $6bn to J&J – completely flawed process on an order of magnitude
    • Directional arrows of progress if spotted increases probability of success on subsector
      • Lighting: burning flame -> bulb -> led; memory, energy density
      • Talked about Calliopa – he wanted to focus on gut-brain access – taste / sugar receptors (Charles as Chilean professor at Columbia)
        • Half-life of tech: 50 years ago, 25 years ago personal computer, 12.5 years ago laptop, 6.25 years phone, 3.5 iwatch, 1.5 airpods
          • More intimate over half-life and improved
        • Had to meet “Rearden” – “I can get rid of that” – Bill Gates’ right hand guy, polymath, PhD neuroscience after undergrad as Classics/Latin
          • Put on wrist strap that could detect 15k neurons that innervate the 15 muscles in your hand – perfectly model this
            • Can control it just by thinking of turning on whatever you’re speaking of
          • We don’t have input problem – we have output problem — too linear
            • Series A and Google/Amazon invested $30mln – want to sell after maximum value
      • Do you find companies that touch near the directional arrows?
        • Don’t need to implant in brain, can read the neurons – 5 years ago you didn’t have everything that was required – power, IoT
    • Moral imperative to invent technology, instruments to invent genius – encounter the technology that eventually inspires others
      • Losing touch with humanity – where is the song after sung? Find way to reduce human suffering.
    • Are there enough entrepreneurs in real technology frontiers? Is vs ought (jokes about competition)?
    • If you can spot “What sucks?” – can you discover something “Wait, what?”
      • 100mln mice – can’t you put sensors/automation for this?
      • Document storage (Mushroom vs atomic storage, not REIT for storing docs) – banker data, scan them – IronMountain can’t do it
      • Entropy information – he gets more optionality by giving information, but death of privacy is coming with convenience
        • Mentioned graphic novelist “Why the Last Man?”, side one called “The Private Eye” about everyone being surveilled – wearing masks
        • Socially and personal privacy is a losing battle but industrial side makes sense
        • Mentions blockchain for voracity – Banksy for private store (analog), authenticity
    • Special operations spending time for 2 weeks – Asia: Philippines, Thailand, Malaysia, Singapore, Japan
      • Coalitions forces, training, sniper, subsea, Seals, cutting edge tech – able to look at things for laser targeting
        • He was there for “What sucks?” – humbled by voracity, proud by the intelligence and what he could do and who he was with
      • Optical signals for those that get through program are the opposite of the big guys – stunning, talented, quietness “stoic intensity”
  • Ayan Mitra, Founder, CEO at CODE Investing (formerly Crowdbnk) (20min VC 089)
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    • Enterprise architect and tech mgr, worked with M&S, Orange, and First Direct
    • Software eng by trade, started in mid 1990s and built internet framing for Bank Offers Direct
    • Was in NY when Kickstarter kicked off in 2010, and saw the regulation was ready for this type of investing
      • Made the concept popular, regulated funding, or Kiva-type – early stage investing is a lot more popular in Europe/UK
      • JOBS Act as regulation freedom for positive step for alternative financing
    • Wave of changes where technology is being brought on the systems and the benefit goes to the investors and markets
      • Quick and transparent – believes it would’ve happened regardless
    • Crowdbnk – reactively do due diligence, price and valuations – invest alongside with investors on their platform
      • Look to raise growth capital for equity and debt – not a pure platform/marketplace
      • Minimum / maximum – equity looking for $500k – 2mln pounds, debt – secured/asset-backed $1ml – $5mil
        • Investors – $10k pounds a year to be diversified and properly investing
    • Valuation class by Ashwin (NYC) – intrinsic valuation (creating, discounted by time and risk) or momentum valuations (price willing to pay)
      • VC could benefit from diversifying investment base – early round by Index recently
    • In crowdfunding, consumer brands may have an easier time going down crowdfunding pick
      • Harder for others to understand some of other sectors / SaaS, for instance
    • Debt funding is #168bn and growing, but small compared to financial services
    • Drawing attention as a focus over time, consumer behavior changes
      • By being more efficient, they can return value to investors and people on the platform
    • Book mentioned: Intelligent Investor – Ben Graham
      • Seth Godin’s blog
    • War chest vs planned capital injections – not a binary answer (eg: compete against Uber – good luck without war chest; tech-enabled services)
    • Funded a company called Breezy – simplifies user interface for older generation, potentially – team/value and invested by US VC’s
  • Andrew Hohns, President, CEO of Mariner Infrastructure Investment Management (Wharton XM)
    • Conceptualized and founded IIFC Strategy as part of his dissertation at Penn
      • Funding gap in project finance to address world’s infrastructure needs
        • Talked about growing projects in Africa, India and others
    • Started a fund as he finished school – raised $500mln for capital projects
      • Including a $1bn transaction with African Development Bank completed with multilateral bank and private investors
        • Provided approx $650mln in additional lending capacity
      • Credit Agricole in 2017 that was “biggest impact investing deal yet” by Financial Times to allow an extra $2bn of funding toward green projects
    • Managing the originations networks for funds with relationships with many global financial institutions

Marketing and Investing Large (Notes from April 22 – 28, 2019) May 14, 2019

Posted by Anthony in Digital, experience, finance, Founders, global, Hiring, questions, social, Strategy, Uncategorized, WomenInWork.
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As a fintech-focused external analyst on pre-seed startups, I see and track as many as I can in the space. It’s allowed me to follow along, at a distance, some of the very interesting companies that are growing in finance and financial services industries. The glut of capital available has produced some successes and many failures for those looking to disrupt the industry – especially one that’s so large. And many are finding out that there are plenty of reasons why it tends to move particularly slowly and, at times, frustratingly inefficiently. You need a ton of users / customers (and the right ones) to make sure the business is sustainable at the level of efficiency you’re requiring to offer up a ‘better’ (re: cheaper) product. But then you must still maintain those levels at a much larger scale. And that’s where companies see you have to pay extra to acquire customers by marketing or offering other avenues in, thus decreasing that margin that was supposed to streamline the business and disrupt that portion of the industry. Cat-and-mouse or dog chasing its tail, often.

What tacks on to that difficulty? Well, the big fish in the pond aren’t just wallowing in comfort, waiting for disruption. Nope. Quite the opposite now. They’re flush with cash or the economics to develop solutions in-house. Or, when they see they’re scheduled to be beat, they come knocking on the door of the ones interested in an exit – purchase, M&A. Banks and the big institutions acquire the necessary innovation to diversify and improve their product offerings. Disruption – not so fast.

Ultimately, I’m not asking for these innovative start-ups to stop. Quite certainly the opposite. Their improvements and new ideas catch the attention and hasten the pace at which the incumbents must move. And it all makes for an exciting follow!

For my notes, I listened to an Ally Invest CFO discuss why he sees getting into mortgages is good for a rising banking company (one of our fintech follows). Then, a Senior VP of Marketing at Coca-Cola talked about how they align creative direction with their brand, despite not pounding red or drinks all over advertisements or songs. What have they learned to be so successful (for SO long)?

Then, one of my favorites in a while was a segment with the authors of Nine Lies About Work. I’ve followed Marcus Buckingham’s YouTube channel for a bit now where he spells out some misconceptions about the typical ‘culture’-speak of workplaces. One of my favorites: Culture is a myth. Simply, workplaces typically have an aura and vision around them, but once you’re there, this may dissipate or be something that depends on the person you’re speaking with (how do THEY view the workplace). In smaller, start-up teams, they’ll likely agree with each other – culture is the similarity of the workers. However, especially as the company grows – this will often change drastically, and by levels.

Last but not least, we had a few fantastic women on episodes exploring The Muse and Tough Mudder. The Muse co-founder, Kathryn, discussed wanting to be a very different company from what she’d experienced before. And how to give people insight into various places. Then Rabia talked about the trajectory for the Tough Mudder races and what may be on deck to bring in more of the family.

Hope you enjoy!

  • Tom Desmond, CFO Ally Invest (Wharton XM)
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    • Went to school at Kellogg, talked about how they were trying to make it easier for investing
    • Ally getting into mortgages and seeing why – thought market remains attractive
      • Mortgages as being on 10-yr timeline, quite different than other offerings
  • Geoff Cottrill, Senior VP Strategic Marketing at Coca-Cola (Marketing Matters)
    coca-cola-logo

    • Talking music and the tie-in between marketing and brands
    • Artists controlling a lot more of their own brand – becoming easier as agencies aren’t as prevalent as in the past
      • Can start and produce on their own, without agencies
    • Talked about a commercial with Pharell, who was incredulous at not having to mention the company – gave him creative freedom
      • Coca Cola ran a song and gave it away first (via their site)
      • Different types of connections, brands
    • Have to be authentic in brand, customers and consumers can sniff out if it’s not intentional, on-brand or paid without authenticity
  • Ashley Goodall and Marcus Buckingham, Nine Lies About Work authors (Wharton XM)
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    • Talked about how the company doesn’t particularly matter, it’s what you’re doing there – even though people say that
      • If culture is so monolithic for a company, why would your experience be different than another person? It doesn’t.
      • We care about the companies we join, not the companies once we stay.
    • Lie #2: best plan wins – more details and variables for the nitty gritty, but the plan becomes irrelevant as you spend time on it
      • Rendered moot because the real world doesn’t wait – plans scope the problem but not present the solution
      • Wanting to know what action to take – plans are rearview
    • Lie #4: Best people are well-rounded – theory of excellence is wrong (excellence can be defined in advance – which it can’t)
      • In order to grow, defined definition of excellence and we can tell you how to get to Warren Buffett success (we don’t say that)
      • Excellence isn’t homogenous
    • Lie #5: People need feedback – based on 3 false beliefs
      • 1: I am the source of truth about you – have to tell you that you lack empathy or charm, or otherwise.
        • Only thing a manager can do is be a reactor – “I am confused, bored, etc..” – a rater of myself.
      • 2: Learning is filling empty space. Insight and patterns recognized within – more revealing what is vs not there.
      • 3: Excellence can be defined in advance. Defined above. Used Rick Barry as example (granny-style vs ‘normal’)
      • Can be an audience to others, and help grow and excel, but in a different manner.
    • Lie #6: People can reliably rate other people. Mediated or seen through this lie through work.
      • Can see the ineffectiveness on rating systems (pattern / tool is an idiosyncratic rating – mirror, not window).
      • Not biased – it’s a natural pattern of ratings based on who you are, not who you’re rating (variation is ~60%+).
    • Lie #7: People have potential. (Performance and potential grid)
      • Person has substance – potential and we buy into the ‘bucket’ or exponential growth for people
        • There is no measurable data on these people. Can’t do it. Immoral.
    • Lie #8: Work/Life Balance Matters – who has ever found this?
      • Balance as a recipe for stagnation – how to replace this with an aspiration
    • Lie #9: Leadership is a Thing. (US has $15bn industry for this)
      • Defined in advance and in isolation from the person doing leading.
      • Only thing that is common amongst leaders is that they have followers – followers into the uncertainty of the future.
      • Create the sense of confidence that trumps the future – the way, though, is very different by leader.
  • Rabia Qari, Senior VP of Marketing & Sales Tough Mudder (Marketing Matters)
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    • Settling on Tough Mudder 5k and the full, instead of the half – had brand ambassadors that understood the growth opportunity
    • Now have Little Mudder and Rough Mudder (dogs), family affair
    • Had tried a half marathon before the 5k but community and team decided it wasn’t going to work – removed some of the spirit.

 

 

  • Kathryn Minshew (@kmin), CEO of The Muse (Wharton XM)
    small_logo

    • Talked about how the companies always looked the same
  • Steve O’Hear (@sohear), Techcrunch writer (20min VC FF021)
    tcJoined TC in Nov 2009, but had taken a break in June 2011 to found Beepl

    • Landed funding and in Nov ’12, was acquired by Brand Embassy
    • Enterprise over consumer from out of Europe – network effect is stronger in the US as English / general language
      • Spotify, gaming companies as the exceptions, possibly
    • Liked the fundraising meetings – thought it was fun but scary
      • Absolute conflict of interest where you want to tell the VC whatever to get the funding, but as soon as you’re signed, you’ll be partners
    • PR and coverage as a distraction – but he doesn’t think that TC makes / breaks a start-up
      • If you make a bad product, you’ll be found out by users/customers immediately
      • If you are making a good product, you’ll be found out still – didn’t matter for TC coverage or not (though probably brought in more eyes)
    • TC does more meet ups and conferences, less moderation of comments and conversation via Twitter or in community
    • As a writer/reporter, they don’t have to pay attention or worry about stuff
      • Editorial freedom but can write what they feel or passionate about – unique to TC / type of journalism
      • As publications grow, they usually lose the freedom, but in this case – they’re one of the best places to write
    • Best interview was Wozniak, as a fan of those that brought the pc to public
    • Inspiration from politics for journalism
    • Harry as saying that Eileen was the only one who had ever said “No!” and he was a bit annoyed, though enjoyed.
  • Josh Levin, CSO OpenInvest

Data Science in Your Business (Notes from Week of April 15 – 21, 2019) May 8, 2019

Posted by Anthony in Automation, experience, finance, Founders, Hiring, questions, Stacks, Strategy, training, Uncategorized.
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It feels appropriate to have the week of Google I/O’s conference to be the one that aligned with my notes where data was the primary focus, especially when Google was pushing ease of technology (centered around giving them access to more data). There were some excellent memes/pictures around for the differences of Facebook asking (hah) for data compared to Google (where they have a ton of it already but they mention the stuff coming).

Kaggle, a research data competition site, held a conference centered around hiring and careers in data with guest speakers from some of the most interesting companies working with data, including Google. Listening to career-based or hiring podcasts related to the field gives insights to how corporations or orgs focus on the spectrum of people vs skills. The other side of this would be a discussion on how data science teams can impact the business at value. What can be done with the data? Is it helpful? Which metrics are measurable and important?

A few episodes went into the business and general application of research in the data. Research on how personality and music are interrelated or satellite imagery from NASA to provide various live solutions – and to what extent they can be designed to be used. A few non-data science-specific podcasts dealt with FinTech, HealthTech and marketplace tuning. How do startups fight against incumbents in various marketplaces? Are their offerings sustainable or do they break the model of what we have seen?

Hopefully my notes provide some incentive to go back and listen to one or each of the podcasts. Or connect via Twitter to talk more!

  • Validating D/S with QuantHub, Matt Cowell CEO (BDB 4/2/19)
    e9qpszxn_400x400

    • Also with Nathan Black, Chief Data Scientist at QuantHub
    • Talking data science – math with business and IT skillsets
    • Companies are manually doing tech assessments with candidates / roles – programmer-based primarily
      • QuantHub looks at a comprehensive, scientific approach to assessment of the stack of what may be necessary
    • NLP of resume, and then Bayes’ updating for input and results from that
      • Assessment platform at the core and using it for hiring (natural use-case) but then benchmarking organizational skills
      • Aggregators of content and matchmaking (say, data analyst up to data engineer – wrangling, SQL improvement)
    • Assessments done and individuals won’t be charged – overall value in helping talent
      • Building the training side in the next quarter
    • How do companies engage QuantHub? 5min to get running – align the incentives for using it / relationship.
      • What are the challenges? What are the skillsets? What do you mean that you want them to do?
      • Requirements changing by different statistical methods (along with computing power, designing algorithms vs latest research)
      • Knowing/vetting data scientists as having to do the role / job – can you mirror the actual job requirements? (try vs buy, potentially)
    • Cloud computing or hardware innovation as ‘cool’ in a world of software – highly critical, depending on certain organizations
      • Some orgs NEED the data improvement there (Kubernetes, Docker, Cloud, Spark vs Power Excel user)
    • Matt as a product strategy guy – book “Monetizing Innovation”
      • How do you determine what the market and customers want
    • Nathan’s book – “Make It Stick” on how you can improve learning methods
  • James Martin (Staffing Lead, D/S at Google) – Getting Noticed in D/S (Kaggle CareerCon 4/17/2019)
    google-cloud-platform-for-data-science-teams-4-638

    • Looking at field – ML Engineer to Quant/Statistician to Product Analyst to Data Analyst
    • Research tips: Open source projects (understanding current trends, gain experience, make connections)
      • Job descriptions (take time to research the differences, tailor approach)
      • Market research (professional networking, connect dots between companies you’d consider)
    • Resume tips: Concise (focus on telling a story on the experiences to highlight outcomes)
      • Factual (if listing skills or strengths, use examples to support them)
      • Related experience (highlight specific projects related to area you’re applying)
    • Networking tips: Professional profile (be detailed but concise about the skills you use and experiences)
      • Targeted outreach (connect after a conference, target approach for conversation)
      • Conferences (meet/greet if possible, follow up via email, LinkedIn, twitter)
  • Gidi (Gideon) Nave (@gidin), Assistant Professor of Marketing (Marketing Matters)
    • Cambridge Analytica before Cambridge – music research and how it relates to certain traits
      • Extroversion and openness were 2 big ones that they could pull from 5 traits (MUSIC)
      • MUSIC: Unpretentious, Sophistication were 2 of them
    • Could pull personality cues from 20 second, unreleased clips based on scores of 1 to 7, also
      • More agreeable people had higher scores in general
    • Personality on 5 (OCEAN – Openness, Conscientiousness, Extroverted, Agreeable, Neuroticism)
      • Questioned whether they could use music to test for the personality (as opposed to the other direction)
      • Personality is established at a young age, so can music likes on Facebook give you a personality side – as mentioned, it did ~2 better than others
  • Fintech for Startups and Incumbents (a16z 4/7/2019)
    • With GP Alex Rampell (@arampell) of CEO/cofounder of TrialPay and partner Frank Chen
      tumblr_nkfx32192w1tq3551o1_640
    • Assembling a risk pool (good and okay drivers subsidizing the bad drivers, or healthcare – same)
      • No economic model for skipping a segment – psychology for half price insurance (say, going to gym)
      • Half the number of customers – taking the ‘good’ ones, profitable ones
      • Insurance has mandatory loss ratios for different industries
    • HealthIQ – mechanism for exploitation on ‘health’ – in FinTech, it was SoFi on HENRYs
      • Positive vs adverse selection – debt settlement company ads, for instance – negotiate on your behalf to settle
      • Healthier people as living longer than non-healthy people – left them more profitable for proving being better
        • Gives them good customers (adverse selection for ‘quick, no blood test, 1 min’)
    • SoFi as stealing customers from the normal distribution – better marketing message “you’re getting ripped off, come to us”
    • Branch as investment – collect as much data as possible and look for correlations – small, mini-loans
      • Induction as pattern is a willingness to pay (credit is remembered) – went and got data from your phone
      • How many apps did you have? Did you look like you went to work? Are you gambling?
        • Counterintuitive potentially: battery goes dead leaned default, gambling app meant more likely to pay, etc…
    • Earnin – phone in pocket for 8 hours, last paycheck and RTS data confirming – will give money that you have earned but don’t have yet
      • Can tip interest or not – can you encourage people for positive community and people driving safely
      • Nurture better behavior – helping to turn customers into correctly priced customer (vs bank that doesn’t want them)
    • Vouch as company that failed but your social network had to vouch for you – Person X is okay, so you can even put up $
    • Tiffany & Co for a long time was owned by Avon lady – but its brand was massive and one of most renown jewelers
      • Could make sense for acquiring more customers, though
    • Killing Geico – take 20% of customers but only take the good ones
      • Selling negative gross widgets, for instance – probabilistic ones (and the bad ones aren’t needed)
    • Turndown traffic strategy – Chase turns down a lot of people for problems (can’t profitably do $400 loan, for instance)
      • Here’s a friend after they rejected them (but see traffic) – Chase will tell you to go to a startup for better underwriting
      • Amazon got right – HP book, for instance – had ad for B&N right next to Amazon (bought) – would make $1 on the ad click at 100% profit
        • Used this to reduce the price on their site and wasn’t sharing it
    • Rapid fire: “Always invest super early” – 9 weeks to decision vs 1 day – can’t get good deals at length
      • Best things aren’t cheap – they’re often expensive – better strategy can be plowing in late (“Can’t believe we’re putting this much $”)
      • Gating item for entrance into a space or into different models – cost of capital and distribution as often the unique thing
        • Geico could easily add additional traffic to start-ups
      • M&A strategy early? Encouraged and used Facebook – buy existential threat (surrender 1% of market cap to buy Instagram)
        • Facebook spent 7% of market cap for WhatsApp, Oculus, etc…
        • Buy the guys that failed trying – courage to build something new -> take them and put him in charge for person that was successful (at big co)
          • Trying to build this thing for ~10 years vs start-up that built something in 1 year (put this one in charge)
          • Ex: AmTrak buys Tesla – worse thing “You work for us” but you want products to push distribution and talent for understanding
        • Only difference was distribution and the possibility to do that
  • Jennifer Dulski, author of Purposeful, Head of Groups & Community at Facebook (Wharton XM)
    • Talked about their group initiatives at Facebook – communities policing themselves as well as methods to flag content
    • Mentioned example of having an employee that came up to her and asked if she had done a good job, she just wanted a bonus or something $
      • Taught her about incentives and why people do what they do / good to know the motivators
      • What drives people?
  • Anth Georgiades, CEO of Zumper (Bay Area Ventures, Wharton XM)
    z-pm

    • Purchase / hire of Padmapper in 2016 that added quite a bit of Canada business (size of California, real estate-wise)
    • How to match both sides for a marketplace – suppliers vs customers
      • Chicken and egg – focus on one, improve other and repeat
  • Word2vec (Data Skeptic 2/1/2019)
    • Produces word embeddings – autoencoders as NN for something like compression to retrieve output successfully
      • m down to n via mathematical representation (m < n)
      • Language compression for vector rep
    • Running the algorithm training on Google’s full internet, Facebook’s news article, Wikipedia, etc… to achieve similar words/spaces
      • Not super adaptive – nonsense place for words it hasn’t seen
    • Real world application – king for word2vec and subtract male – then add in female and you get queen
      • 300 dimensional space, semantics of that example
      • Bad example: training on entirety of internet results in something like doctor – male + female = nurse (gender neutral data)
    • Feature engineering for bag of words, good example for transfer learning, also (train model on text and then use parts of it on smaller area)
      • Very large corpora for NLP but can use pre-trained models of word2vec and use it in other models
  • Sean Law (@seanmylaw), D/S Research and Dev at TD Ameritrade (DataFramed #59 4/1/2019)
    sean_law_quote_card_3

    • Colleagues thinking he tends to ask lots of interesting, hard ?s – hopefully with answers
    • If he’s a hard worker, then he’ll do great – being in industry for 3 months time – has to juggle effective time spend
    • Molecular dynamics is short time scale and lots of computing power – parallel computing before and now the growth / usage of GPUs within days
    • Hypothetical example for alternative data solutions – driving to work and listening to NPR where NASA had a new dataset that was sat imagery
      • Pollution ORA dataset for air quality – area of high commodity necessity with pollution joining
    • If building ML as a binary classifier – but don’t know where the data is (do we have to collect? 3rd party API? Internal?)
      • How much effort to get it usable in the pipeline? Then, what’s the reasonable accuracy level – better than 50-50?
      • Some signal in the noise
    • Exploring chat/voice – query account balance, stock price, news articles via Alexa/Echo
      • Headless / device-agnostic option – audio to parsing of text, understanding what customer wants (NLP) and then what it means
      • Following PoC and into production
      • PoCs can miss: scalability (unless claim is to get scalability), model accuracy (not best model immediately), real-world applications (use case in mind)
    • Interpretability standpoint – regularization, L1 and linear – constraining coefficient can be very useful (background noise from video, for instance)
      • Time-series pattern-matching as non-traditional
    • Calls to action – data failures of things that didn’t work or negative results

Impact and Data to Growth (Notes from April 8 – 14, 2019) May 1, 2019

Posted by Anthony in cannabis, Digital, experience, finance, global, NLP, questions, social, Strategy, training, Uncategorized, WomenInWork.
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I started watching Street Food on Netflix and in the Osaka episode, the chef makes a great claim that can work for today’s notes: “If you want to create your own current, you cannot live your life by going with the flow.” Granted, you can use the current as a guide, but to truly create something unique, you have to hop out of the path and try your own luck. Today, I was listening to an episode even with Keith Rabois on Invest Like the Best, and he’s a proponent of not making 10% decisions, but rather investing into 10x ones – the riskiest that can pay off are the ones that will be truly incremental. The 10% experiments may improve a bit, but won’t exponentially get you scaling.

I had a great mix of NLP / Machine Learning podcasts, social/responsible corporations like United by Blue and Everytable, to go with sales thought processes, data ethics, and finance starts on a global scale. Each person / founder / company tackling unique challenges based on their individual experience that got them to that point. How can you approach the problem? And more importantly, what’s the right way that your expertise leads you to a solution for this problem?

For some, it was how to release the stigma in the cannabis industry to expose people to the health benefits as we’re legalizing in more states? United by Blue’s founder wanted a truly sustainable business model that supported his beliefs in giving back. An expert data scientist by the name of Debbie continues to improve women relations in tech and data-related fields by 1) supporting others graciously and 2) providing, particularly Latin American women, the opportunity to see how her passion for learning sparked her adventurous career.

Hope you enjoy! Leave a comment or follow along!

  • Su Wang, Elisa Ferracane in Authorship Attribution, UT (Data Skeptic, 1/25/19)
    Link to ACLWeb for paper

    • Discourse units in addition to others
      • Rhetoric structure theory (RST) – 2 elementary clauses (as Elementary Discourse Units – EDU)
      • Relation is related by an ‘elaboration’ where the 2nd sentence elaborates on the previous sentence
      • Rows are sentence pairs and the cells show the relations between the 2 (1st, 2nd; 2nd, 3rd, etc…)
    • Plagiarism detection, authorship attribution as semantic inference (both authors as computational linguistic PhD)
    • Can be unsupervised (classification of text to an author style) or supervised (accuracy or how closely it matches an author – assign key)
    • For the paper, they looked at 9 texts via Project Gutenberg and did a CNN – high-level baseline
      • Had 2 months to get it to the next level, optimization – said that LSTM performed the best but too slow for translations or 1000s of words
      • CNN can be as good as LSTM or better depending on architecture
      • Tried grammatical matrix, columns are entity, rows as sentences – subject, object, other
    • Used dataset of 19 books and 9 authors as extension of prior state-of-the-art paper
      • IMDB as another dataset – short texts with many authors (tried to do with Twitter but can’t get structure/sentence)
      • Initial data set was ~15% more accurate (99.8%)
      • 98.5% accurate for extended novel classification ~50 texts – SVM did well also of about 84-85% (more data may allow them to be more acc)
    • Looking at the different types of features – RST was more sophisticated in that the models did better in all experiments
      • Could embed or use the one factor as a distribution over other set of features
    • For IMDB dataset, discourse features nearly didn’t help – too short to establish structure
    • Human as the ‘gold standard’ but certainly not perfect. Authorship probably different task, though.
      • Would require expertise on the authors’ part. Machine can pick up on far more patterns.
    • Next for him – semantic narratives and story salads (grant via DARPA?)
      • Coherent narratives, shuffle the sentences and reconstruct the story.
  • Sylvia Wehrle, founder CEO of June CBD Apothecary (Wharton XM)
    uuonxuko

    • Talking about the difference between CBD, THC and other strands
    • Humans as growing up with various forms of hemp oil – additive and purposeful for our evolution
    • Using the appropriate properties to go through benefits – getting the common questions out of the way
  • Donald Robertson (@donjrobertson), author of How to Think Like a Roman Emperor: Stoic Philosophy of Marcus Aurelius (Wharton XM)
    • Book discussing difference between stoic and Stoic, cynic and Cynic, etc…
    • Calm and indifference is different than how it may have been perceived
  • Sam Polk (@sampolk), author and CEO of EveryTable (Wharton XM)
    allen_181217_everytable-14

    • Sustainability at Feast (prior company)
    • Using Feast as test-tasters for EveryTable menu / offerings
    • EveryTable as sustainable, healthy food for people in an affordable way
      • Restaurants with partnerships of cities/areas that match the pricing (Santa Monica different than Watt or Compton)
      • Can order on app and go pick up meal for < $8 – able to do this with scale – try to ensure this early
    • Rolling out BlueApron-style weekly meals at the same price as in store
    • Corporate offerings where they have EveryTable coolers / fridges that take a credit card / payment and can pull out your order
  • Brian Linton, United by Blue founder CEO (Wharton XM)
    962afe495a8ae8ea0aaabcf099e9c715.w1583.h658

    • Originally moved from Singapore / Asia, went to college in Michigan – boredom satisfaction with sales
      • Started with ‘guady’ jewelry that was travel-related (tourist-style jades, emeralds, etc…) that he would source from home
      • Travel down to Florida / other areas and sell to region
    • Believed in doing good, so he would donate ~5% of all proceeds to ocean conservation – realized this wasn’t sustainable
      • Random donations of $1000s or %’s
    • Finally started United by Blue to develop the sustainable business model and what he believed in
  • Right Way to Get Your First 1000 Customers with Thales Teixeira (@thalesHBS), associate professor at HBS (HBR IdeaCast #676, 4/2/2019)
    • Startups failing because they try to emulate successful disruptive biz and scale instead of learning about initial customers
    • First customers are more than the money, word-of-mouth, R&D and free feedback
    • Etsy, Amazon, Netflix, Uber had no new technology (just finally had the map to see if there were cars coming)
      • Etsy went to craft fairs to recruit sellers, who then attracted more buyers
      • Pinterest tried to create a culture initially to set the tone for quality
      • AirBnb was awful initially in NY, so the founders wanted to find out – places were great but pictures were awful
        • Rented a nice camera and offered to take the pictures to improve the ones on the listings
    • What is the primary driver of value to the customers to deliver? How does technology play a role in this?
      • AirBnb had 1 engineer (founder) for a long time – increase the utilization of an expensive asset
        • Hid the options initially – didn’t have much inventory so they would email / find out and then get back to customers
        • Show availability – needed to stay in a house in the places
    • Technology is the enzyme / enabler of the start-up or experience and acquire the customers to purchase the product
      • People that like smaller companies, try new things, explore products and tell them
    • Unlocking the Customer Value Chain (Thales’ book)
  • Critical Thinking in D/S – Debbie Berebichez (@debbieberebichez) (DataFramed #58, 3/25/19)
    • Debbie is a physicist, TB host and CDS at Metis in NY (first Mexican woman to get a PhD in Physics from Stanford)
      • Promoting women in STEM, especially hispanic women
    • Metis is a data science teaching company as an arm of Kaplan in NY, San Francisco, Seattle
    • Did 2 postdocs around Columbia before going to Wall Street to work as a quant – but money wasn’t the only motivator, so she left
    • At Cambridge, she remembered speaking about Astronomy 101 as her first intro to physics class – was on 2 years of scholarship
      • She took a walk with her friend Rupesh and said that she was crying – “I just don’t want to die without trying physics.”
      • Passion drew attention and professors – offered her for a 2 year physics degree (skip first 2 if she could pass a test with complicated derivatives)
        • Had 2+ months to learn calculus, basics to mechanics and more – passed her test (9am to 9pm)
    • Mentioned going into high school to discuss data science – class was doing coding/SQL/data look on animals
      • Had 1 group that was looking over turtles – couldn’t answer the units for weight (triple digits) – not lbs, but grams
      • How this made sense – how to piece together reasoning / bias – how needed this skill was
      • Not bothering to check outliers or some data was exhibiting – why do we do it all?
      • Danish astronomer built and designed 1000 stars, which wasn’t much, but Newton and Kepler, Copernicus all derived theories from
    • Large datasets vs small datasets – insight more important vs size (big data as sometimes unnecessary)
    • Feynman quote about fooling ourselves – bias that we create.
    • History of Statistics – Stiegler, normal distribution and derivation of central limit theorem by Gauss and Laplace (1809 with Jupiter’s motion around sun)
    • With her bootcamp – she wants to attack the question of using the right algorithm and how to analyze the problems at hand
      • How to choose a data project in what you’re interested in – madewithmetis on Metis site
    •  Singular value decomposition (SVD) and reducing dimensionality, worked with Genentech founder – healthy DNA vs patient’s DNA and cancer
      • Reducing dimensions to the ones that were most relevant – NLP also
    • Think deeply, be bold, help others – Grace Hopper celebration talk
  • Dean Oliver (@deano_lytics), Data Analytics (Wharton Moneyball)
    video_default

    • Talking about how far behind NFL is behind NBA in tracking
    • There are people doing video for football, but not much – not widespread
      • Position groups will gain entirely different/new insights into how they’re playing
  • Cordasco Financial Network Planning + Sri Thiruvadanthal (Behind the Markets, Jeremy Schwarz)
    • Discussion of hedging dollar vs not – if hedging, probably wise to diversify with global
      • If not hedging, then europe may not be as great
    • Current markets say that liquidity isn’t as high with central banks, stocks start to couple and lose diversification / value
      • Decoupling early on in cycles
    • Relative value may be fine but not absolute for the dollar compared to other currencies
  • Jeppe Zink, GP at Northzone (20min VC 087)
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    • Invested in Spotify, Bloglovin, TrustPilot with focus on SaaS, fintech, mobile
    • Worked at Deutsche Bank as analyst in corporate finance, tech banker – left with 90% of team
      • Convince bank by buying principal investments before IPO in late 1990s – worked out
    • European cycles of tech – 100mln to 3bn people online, digital increase and telecom infrastructure
      • First VC firms in existence were doing integrated buyout model, which failed initially – too transaction focus
      • VCs have the talent that’s aligned with the founders now – 90% of VC firms that existed in 2000 had died in 2002
    • 10 year cycles where the great companies withstand, others don’t
    • Stage agnostic for them, series A to D rounds
      • Nordic companies of unicorns for what he has had success with
      • Europe as dropping trade barriers initially and in the 90s, broadband and smart phone starts (Nokia, Ericsson)
    • Has offices in the north for Northzone but he makes it up every other week or so
    • Try to emulate the start-up and have hunger/ambition always
      • Not trying to stagnate – venture capital vs patient (he thinks impatient is better – learn through failure and testing)
      • How fast can you learn to level up and deliver the best product? Continuous measurements, KPIs.
      • For Jeppe – momentum in product development
    • Most intrigued by fintech investing – Peter Thiel as one of his favorites
      • Most recent company was CrossLend – consumer lending with European bank lending
      • Book: Startup Growth Engines as collection of random founders and interviews

Changing Tides – CPG, Fin Plan, VC (Notes from April 1 to April 7, 2019) April 24, 2019

Posted by Anthony in Digital, education, experience, finance, Founders, global, social, Strategy, Uncategorized.
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Spring is in full bloom! The California weather vortex mixture of April showers bringing May flowers. Only, we skipped flowers part and went straight to 70, 80 and now nearly 90 degree weather. Dry land. If that doesn’t work for some outdoor BBQ, wine tasting and good friends, I don’t know what would.

That didn’t stop the a16z podcast from having a collection of people on that focus primarily on CPG. What is changing in the industry – if anything – as it’s notorious for being slow moving? We see attempts at the various points in the distribution side but it becomes about scalability. Amazon/Whole Foods combo? Or will it be primarily food delivery? Seems similar to the car/taxi/autonomous question of ‘last 2-3miles’.

Then we have a similarly plodding industry, which was the last 10 years of growth seen in startup financing for Europe and London. How did the dynamic change for the founder of Hoxton Ventures once he left Silicon Valley for green pastures of London? Why is it that he maintains a global view while in Europe but keeping tabs on the US market?

Lastly, I wanted to reiterate a theme I’ve focused on previously, which is asking the right question and how that determines the plan for action going forward. A discussion I listened to with an NLP expert at AT&T Labs as well as in asset management and personal finance where people need to find better ways to match expectation with reality – whether it’s in data of tv usage patterns, network effect results from cell data, or agents looking to align incentives for a customer portfolio and their book.

Hope you enjoy the notes!

  • Hussein Kanji, founder Hoxton Ventures (20min VC 086)
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  • Started at Microsoft, went to Accel Partners as board observer (Playfish), acquired by EA
    • Seeds into Dapper, OpenGamma – yahoo acquisitions
    • Founded Hoxton and raised $40mn
    • Moved to London in 2005 for graduate school and had a colleague from Bay Area that made an intro for him at Accel to connect
    • Accel as $500 mln fund, to break-even on the fund, you need 40x’s on early stage, but bigger funds focus on late stage for big money and returns.
    • He became bullish on Europe in 2009, 2010 – shifting for platforms that were global.
      • Europe was historically underfunded, so focused on their domestic area (and would then run into US competitor that was bigger)
    • Raising the fund took about 3 years (‘normally’ 12-18 months)
      • They budgeted for 24 months and had aimed for $25mil
      • Americans said that venture investing wasn’t viable for Europe – “nothing on the ground floor”, can’t see, etc…
      • Europeans couldn’t see it after conservative – investing as gambling, etc
    • Once leaving Cali, “prove you’re smart” or “rolodex works” – network is just that you’re out means you’re disconnected
      • Some still have this sense, others don’t
    • At this time – US $ makes up about 2/3 of Series B or later funding
      • At the time, NY could do funding for 1Q that would be London for a year
    • Blog – Abnormal Returns – mentioned Flowers for Algernon (book take)
    • Follow up amounts – just backed a digital healthcare company
      • AI and live video with physicians – app for something wrong or not
  • Chris Maher, CEO OceanFirst Financial (Wharton XM)
    oceanfirstpressreleasimage1a

    • Talking about knowing someone when they’re nearly in default
    • Being prompt and succinct with bad news – doesn’t improve if you delay
  • Who’s Down with CPG, DTC? (a16z, 2/16/19)
    2b3add47cee2f6ff06eb421d32f8e4bf

    • With Ryan Caldbeck (CircleUp, Jeff Jordan (GP a16z), Sonal
    • DTC movement with tech VC firms focused on selling prominently to consumers – not product innovation
      • Marketing companies vs innovating – can’t change distribution to compete with Amazon, so proprietary SKUs not on Amazon or retailers
      • Dozen brands have 10 companies trying to compete for you, as a startup – ecommerce can’t get big in US outside of big 5
        • $1bn profit and sales
      • CAC marketing dollars are too high now when they scale
    • Unilever (DSC) and other CPGs with biotech / big pharma buying innovation
      • Clorox and Colgate as breeding ground – “If you can sell sugar water” – Jobs / John Skully
      • Selling the same product for 30 years – wouldn’t happen in tech
        • Harvard dean book called “Different” – small improvements (mentions wider mouth on toothpaste)
          • 99% exactly the same vs 1% is different – Harley Davidson, Red Bull, etc…
    • Internet enables $5mln revenue line, not tv – long-tail discoverability – can make hits on this
      • Proliferation of cpg (Sonal questions why no DTC, though)
        • Harder to a/b test or change packaging with ‘atoms’ vs ‘bits’ – trying products, packaging
        • 2-4 sets of year with 5 stores for 6 months, then 50 for another 6 months, etc… while you’re growing (Disney example)
      • CPG companies can’t start in 5 Safeways, do it in 200 but if you miss – it could be over
    • Sonal at Xerox Park – had a big CPG client whose challenge was what happened after customer purchased product
      • Worse – knew what they sold to retailers but not what product was bought at the retailer – CC cos don’t sell that
      • Some retailers may have loyalty cards but not in way that aligns everything
      • InstaCart compelling because revenue from grocery, consumer, cpg companies interested in accessing consumer
        • First performance marketing – know everything consumer has bought
      • Used an example of Steak next to wood chips at a Safeway – different than buying an end cap which would’ve been 20+ ft away
    • Food is < 5% of online – food will be delivered locally – hard to strip out costs from 2-3% net margins
      • Tech needs to penetrate cost margins – China experimentation with restaurants inside grocery stores – Asia / India not necessarily core differentiator
      • Delivery will be a convenience over an experience (as it is now) – could be 50 year vision
        • Price, experience, convenience, assortment
    • Loyalty cards don’t actually give you much more – very self-selected but at least SOME data, though adverse selection
    • Large brands losing share to small brands – decline in distribution costs that are more shifting fixed costs to variable costs
      • Big ones are struggling to work with the smaller brands (new chocolate bar – $100k to get onto the shelf which has decreased vs internet $0.99)
      • When Jeff was managing ebay – tv would be $1mln to produce ad and $10mln to distribute for what may be efficient
        • Now, marketing is a $10k youtube or facebook ad and you can hit your target audience
      • Number of brands proliferating but grocery / CPG only growing 1-2% – $ per brand comes down (more choice vs less)
        • Sonal brings up the ‘right’ choice to the right people – not stripping the choices (say, Coke vs Pepsi)
    • Offline world has been impossible to get the data you want but it’s difficult to get what you want pulled together
      • Entity resolution – google results, Instagram, Facebook, Amazon, sold on Whole Foods – different names and decide how a product is what it is
    • 3G Effect – large South American company delivering shareholder value, R&D is first – 2% of sales; tech is ~14%
      • Some private markets in CPG – quantitative funds with AI / data scientists – repeated in CPG with all the same business models
      • Private equity / training data would be really hard to match that
    • Quant funds as looking at tech – miss outliers, no pattern, apriori; CPG could be patterned since winners are similar
      • Brand intensity with consumers, Product must have uniqueness – Vitamin Water / Kind bars
        • Kind bar had insight that they show their food (packaging see-through) and you can see that it’s not processed
        • Must resonate still (could put fish in product but may not be good)
      • Distribution gains is how you win – big winners don’t only sell in stores
        • Breadth and quality – where product is being sold (Whole Foods / Costco / brands want you to know it)
  • Josh Brown, Ritholtz Wealth Management (Wharton XM, Behind the Markets)
    • Discussing how he thinks it should be required to have managers match the client offerings
    • Takes longer but they take risk of getting to know clients and plan ahead of putting into place
    • Going after the right CFPs for the overall view – location not necessarily important
  • John Odnik, Consulting for Wharton Small Biz Dev Center & Principal at The Ondik Group, (Wharton XM)
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    • Talked about operations and sales improvements, what he looks for

 

  • Noemi Derzsy, Senior Inventive Scientist at AT&T Labs, D/S and AI Research (DataFramed #56, 3/11/2019)
    acumos-small
  • Started in academia but didn’t have bandwidth for O/S projects until NASA “datanaut”
    • Provide a community for supporting women in the open source, women in ML and D/S
    • Government forced NASA to opensource over 30k datasets
    • Chief Knowledge Architects, David, is very supportive of the community and open to question
      • Launch application opportunities to be selected yearly, typically, for datanauts (open.nasa.gov)
    • Teaching and pedagogy for her – network science focus on complex systems, done many workshops
    • 2006 as she finished her bachelor’s degree, she needed a thesis topic (Physics + CS)
      • Built a network (both directed and undirected) system between European universities and students who went between them
        • Small dataset snapshot from 2003, matrix form (value of university to another in a row/column)
          • Professors’ network influencing students’ movement among universities
          • Initial data was most interconnected by the level of partying done at university
    • Brief about business at AT&T Labs – solving hardest problems, and improvement should allow for efficiencies
      • AT&T owning Turner and how much tv data that allows them more recently – made a whole division
      • Advertising jump after AdNexus (now Xander) improving ads in the entertainment space with all of their data
        • She’s fascinated by bias and fairness in advertising marketing
      • Creating drones for sat tower analyzing – DL-base to create real-time footage for automating tower inspection and anomaly-detection
    • Her projects: human mobility characterization from cellular data networks – how to move through space and time and interactions
      • Large-scale anonymized data – mentions her frustration from interviewing the prior year where positions were in completely new fields
      • Nanocubes – AT&T creation that’s opensource and visualize realization
        • Large-scale, real-time data set availability with time and space
      • 2006-2010 paper about seeing the anonymized data where at a certain time in a certain city, there would be stopping of texts and move to calls
        • Turned out to be calling taxis at the end of night from bars / out
      • Networks as everywhere: protein interaction, brain neural, social, street/transportation, power, people
      • Topology can show basic features – degree of nodes/connections and their distribution, most nodes have very few but small hubs have very large connections // mentioned Twitter with few users at 100ks
        • Clustered node networks or are there homogenous subgroups – filter bubble / echo chambers
        • How to influence people – distribution understanding and seeing the dynamic processes dependent on the network structure
        • Cascading failure: info flow, nodes have assigned capacity – 1 failure reallocates the load to neighboring (power grids)
    • Product management fellowship related to data science and what vp of marketing, c-suite needs to learn or know as it pertains to data science
    • One of her favorite – unstructured data and text data in NLP as fascinating projects where you can pull features

 

Striving to Learn Yourself (Notes from March 18 – 24, 2019) April 11, 2019

Posted by Anthony in Altucher, Digital, education, experience, finance, Founders, global, medicine, questions, social, Strategy, TV, Uncategorized.
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There’s nothing wrong with the selection of a well-traveled path. It’s paved with a full network of people who have gone ahead of you. If you’re active and curious enough, that can lead to opportunities aplenty. But some of us feel as if that can lead to a pigeon holing or limit on what we FEEL like we could achieve. For better or worse – maybe we’re curious along different lines – following a boundary, or making a new path altogether into the woods. Jump into a new space or, adjacent markets, as popularized by Peter Thiel.

It’s hard to go against what we’re generally comfortable with. Habits have been grooved into our system of processes for a reason. I believe that if something is eating at you or there’s an overarching sense of obligation toward a challenge, relishing that opportunity is vital – and should be celebrated. 25+ years ago, maybe less so – as the fallback could have been harder. But now? Nonsense – networks are as connected as we make them out to be – reach out via Twitter to someone, LinkedIn, Facebook, Reddit, Instagram, Slack or the tried-and-true in-person coffee chats, conference meet-ups or otherwise. Interconnectedness has never been this high before. But you have to put in the effort.

 

  • Ashish Walia (@AshishW203), co-founder and COO at LawTrades (20min VC FF 019)
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    • Portfolio company of 500 Startups
    • Undergrad in Queens, then law school – read through entrepreneurship opportunities, spoke to lawyers – knew he didn’t want to be full time
    • Decentralize major corporate law firms – before you’d have to go to bottom barrel legal service or stuck paying $600 an hour for routine stuff
      • Middle boutique firms could save money and they were looking for work
    • Joining law firm is like everyone wanting to get in and everyone in wants out – if he knew this already, why not do a different thing
      • He wanted to figure things out for himself and work his tail off for what he wanted to do
      • Idea for LawTrades came up in his 2nd year of law school – businesses and lawyers-directory service, terrible traction initially
      • Using all of the resources – blogs, podcasts, videos, etc… diving in
    • Gary Vaynerchuk as sales and customer experience, as he wasn’t a tech/coder
      • This Week in Startups, Jason Calicanis as well
    • Law school as encouragement for a corporate firm, not to apprentice and then start your own thing
      • More legal technology and open, incubators popping up but not traditional
    • Attorneys with big law experience that want more control over their work-life balance are their target for LawTrades
    • Had started a podcast to drive traffic to LawTrades – had a guest on as founder of Pigeon and he thought they should apply to 500
      • Applied a few days before deadline, had Brian Wang interview while in NYC the day after, then a skype with Elizabeth Yang
        • For LawTrades, they cared about 2-3 recs after learning about the business
      • 4 days after, got in and moved from NY to CA
      • Really wanted 500 Startups because they wanted to drive distribution (vs YC as product-focused)
    • Raised small seed round with the vision, no customers that were just angel investors
    • Enjoyed BrainTree founder Brian Johnson as a nontechnical founder to make it large
      • Altucher, Thiel’s Zero to One, Quora, Medium
  • Dave Sonntag, Gonzaga Associate VP / CMO (Launch Pad, Wharton XM)
    • Discussing university brand, marketing
      • Smaller school of 6000 comparatively, but large brand name
    • Funny to me that he said that the basketball brand was priceless – invaluable
      • Primarily over last 20 years
    • Only the 2nd CMO in the history of the school
    • Started as marketer at Eastern Washington before seeing opening at GU (alma mater)
      • Trying to line up brand exposure to campaigns – bracket + donation set up for the week of tourney start
      • Last year, drove about 1/4 of the site traffic – had to prioritize the home page and stories to make them compelling
      • This year, accentuating professors and their stories (named #1 university professors by US News)
  • Chris Riccobono, UnTUCKit founder (Wharton XM)
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    • Had failed at multiple companies before thinking of returning to finance but landing on doing this co
    • Building a brand around shirts that aren’t to be tucked in – seasonality isn’t necessary
      • Built to have that offering as compared to types of shirts (Tommy Bahama – floral, Armani – club, etc…)
      • 50 stores now and the plan is to have them distribute clothing to better control distribution/supply
    • Increasing market in areas that they put a store – both online and in store
      • Tracking customer data as they go back & forth between online and in-store
      • Control experience of touch and customization to drive conversions
    • Doing once a month “fast fashion” with 4-6 designs that are only available for 48 hours or limited time for attention and marketing – demand driver
  • Linda Crawford (@lcrawfordsfo), CEO at HelpShift (LaunchPad, WhartonXM)
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    • Getting over imposter syndrome, realizing she is certainly an expert
    • From Salesforce and Siebel, had ran and grown a ton – wanted to get into start-up land again
    • Building the right team immediately, making sure everyone was on the same page
      • Had been recruited by headhunter and wasn’t predicting going into CRM, again – maybe fin or healthtech
  • Denali Therapeutics, (WhartonXM)
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    • Focusing on neuro degenerative diseases, isolating proteins that cause damage
  • For the Billions of Creatives Out There (a16z, 3/16/19)
    SHOW_KO.eps

    • Brian Koppelman, Marc Andreessen, Sonal Chokshi
    • Creativity of business, talking about Brian’s original script with his partner – Rounders
      • Failed initially, people turned down and it wasn’t even a box office hit – super small chance to get the repeated viewings
      • Only about doing the work themselves, the rewards would come
      • Worked as bartender / music exec – would work for 2 hours to write a script
      • Were given a chance $5k to be partner – took advice from Horowitz of Beastie Boys’ sister
        • She said “if someone would pay you money without seeing it, then you should write it and you’ll have options.”
    • Balancing the success – his state initially (creative impulse being down)
      • Toxicity that made him bitter if he let the creative impulse die, even if he had other stuff going on
      • Knew he needed to do the work even if it meant failing
      • The job that was mundane / bitter (music exec), he felt better doing because he had already put in 2+ hours of writing to try
    • In Rounders – rejected by every Hollywood agency
      • Some said overwritten, some said underwritten (he still says unsure)
      • He sold the script over a weekend and Monday to Miramax
        • By Tuesday, every agency that had passed tried to sign them – he read them their notes on why they’d passed
        • All told him that they didn’t read it (assistant, reader)
      • Wanted to overstep to make success by getting a director that they agreed on the vision/leverage
    • Up to you to manage the relationships (founder and investors)
      • Learned at a young age how to talk to powerful people – outside of having college paid for or something
      • Father would put him in position to talk to people – in meetings, in production studios, etc…
      • Don’t treat them with a sense of awe or condescending. Also, make them laugh and you’re comfortable in your own skin.
        • Be able to grow, better yourself, relax and they’re not all-knowing.
      • For shows – make it on budget, crew taken care of, make people heard and listening – take notes only on what can make show better
        • Artie from Larry Sanders (show) – network executives discussion
    • Podcast Brian is not script writing Brian – major leagues now, not getting nurturing Brian
    • New Brian and Adam wouldn’t pitch movie Rounders now, it’d be show Rounders
      • Movies were the way that they communicated from the time
      • Televisions now and visual literature as much better than movies
    • Not letting emotional response dictate your actions – how do YOU comport yourself, not the other
      • Especially in partnership types (founder / CEO or other setup)
      • Has to be more important that the other gets to make the decision than you to be right (both need it)
    • Tim Ferriss w/ interviews, 90%+ meditate (quickly Marc says never so in the minority)
      • Brian does 2x / day, 20min & reduces the physical manifestation of anxiety
      • David Lynch for Transcendental meditation (David Lynch foundation)
      • Argue about journaling for introspection vs meditation as a respite or calming of thoughts
    • Billions stuff: As Good as It Gets scene response to how he writes women so well
      • More the result of everything he’s ever read, done, watched while he sits on his couch with music blasting with his laptop
      • Wants to write the characters to all be smarter than the writers are
      • How he stumbled on Vince Staples’ Street Punks in Axe’s bachelor pad (the scene and debauchery and debased)
  • Tony Kunitz, StatsBomb (Wharton XM at SSAC)
    • In london now, paying attention to premier league
      • Progression passing and going through pressure
      • Building the data, paying people to note and augment with computer vision
    • How baseball has gone through 3 stats progressions
      • First value of players and contracts
      • Changing how to play on the field
      • Now changing training and player development (swings, angles, etc…)
    • Also have changing coaches guard – need people to be able to coach properly or the new developments
  • Maria Konnikova (@mkonnikova), The New Yorker (Wharton XM)
    Books: Confidence Game & others

    • Psychology study and approaching poker after reading John von Neumann’s work on game theory
      • Appropriate mix of human decision making – very different than Go or chess
      • Luck and imperfect knowledge of others – strategy vs luck
    • Approaching Erik Seidel to be her coach – intrigue at her book research, and figured if it succeeded or not – could build a bigger audience
      • Still gives her a tough time at not knowing how many cards in a deck (52 vs 54)
      • Using the “marshmallow test” decision-making of Walter Mischel to see if people with high levels of self-control made better risky conditions
        • Made me think of how high school students beat AI experts at Berkeley AI conference after just an hour of teaching
    • Frame of references and deviations can thrive in environments of change
    • Talked about how pros de-leverage themselves by buying in on other pros (can be up to 75-80% of each other)
      • Know that one thing can boot them from a tournament, even if the math is in their favor
  • David Blanchflower (@d_blanchflower), Prof of Econ at Dartmouth (Wharton XM)
    • Children, Unhappiness and Family Finances paper, with Andrew Clark
    • How to gather the data for unhappiness and finance – not based on income
      • Did it based on “Do you struggle to pay bills?” – always, sometimes, never
      • Found that more people with kids struggle to pay bills
        • But children make them more happy, when asked and measured
    • How to change this – other countries have tried to address child care subsidies or tax breaks
    • Younger kids were also considered to be more happy than teenagers or older ones
  • Steven Rogelberg, author of The Science of Meetings (Wharton XM)
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    • Employees suffering by tons of meetings that don’t return as much
    • What type of meetings would be preferred? – Remote. What type of meetings are the least productive? – Remote.
      • Have to dial in how to make meetings more productive, especially when remote.
      • Shorter, planned meetings are better – Remote < 30min, for instance.
  • Caring Capitalism, Miriam Schoning author (Wharton XM)

Brands, Strategy and Consumer Spend (Notes from March 11 – 17, 2019) April 3, 2019

Posted by Anthony in Digital, experience, finance, Founders, global, Hiring, questions, Strategy, Uncategorized, WomenInWork.
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For this week, I wanted to reach outside of my usual Wharton/Business XM channels and the typical 20min VC, so I dove into Discussions in Digital, which is a podcast done by McKinsey & Company, focused on sales and marketing in the digital space. What happened for the week, then, was a convergence on marketing strategy and the pull from physical stores to online media. We’ve seen considerable growth of digitalisation across a bunch of different industries, so the collection and reading through the notes helps to shed light on how different companies are engaging this through the 2010s and beyond.

However, we start with none of those, and I wanted to highlight her work. Christine Heckart, who recently became CEO of Scalyr, has been an expert in the technology space since her childhood around her father but more importantly, as a veteran of Microsoft, Juniper Networks, Cisco, and Netapp. She went into depth on what it means to be a master operator in the tech space as well as how she has managed being a new executive in an established company, which can often be a perilous position.

  • Christine Heckart, CEO of Scalyr (Bay Area Ventures, Wharton XM)
    scalyr

    • 2016 Woman of Influence by SV Business Journal
    • Talked about always being the lone woman in an industry of tech and general – always curious
    • Acts on BoDs for Lam Research and 6sense
    • Building the team at Scalyr, sitting and watching when she first replaced the founder to establish some trust
      • Not breaking things immediately, but gathering information and having the strategy
      • Her responsibility to have the vision, make the strategy and execute it all within the culture of the company
    • Previously with Microsoft, Juniper Networks, Cisco and NetApp
  • Discussion with the Marketing Matters professors
    • Talking about how Target and Walmart have been killing it again (Target french)
      • Physical locations, where they have operational excellence
    • DNVB (digital native vertical brand?) that opens a showroom/store to much success
      • Can have people try items, feel them and get a sense before placing an order online or in store
        • Have it delivered very quickly
    • Cannibalism of Gap / Old Navy on market
      • What’s the difference? Old Navy was hip and cool, initially.
      • Now, Gap is just more expensive Old Navy or vice versa
      • Releasing them as new brands (Gap/Old Navy / Banana Republic, etc…)
  • Reimagining CX in Mobile Age (Discussions in Digital, 6/28/2016)
    mckinsey-logo

    • McKinsey’s Brian Gregg, Michael Jones (RetailMeNot), Mahin Samadini (McK DL), Dianne Esber (McK), Mark Philips (McK)
    • Radio 38 years to reach 50mil people, tv – 13 yrs, twitter 9 months, Google+ 88 days
    • Mobile as a phone or what? Transforming experience and every industry – beauty, connectivity and in both B2B and B2C
      • Mark was at Candy Crush – how do you understand users? Not maximized yet.
        • How to construct the journey through the product – mapped out the customer journey
        • Did a lot of pre-curation depending on IP, time of device and other numbers for the apps (before even using)
    • Can start to see that people plan shopping earlier – if outside of geofence (home, elsewhere) and look where they want to shop
      • Partners can see – RetailMeNot – when they want to shop
      • “Snacking” – personalized commerce, faster and easier to buy – more you frequently purchase
        • Reiterated by Candy Crush (90sec, even, with female demographic – put kids to bed and on way to bed or wherever)
    • 80% of transactions by 2020 will still be in physical locations in the US – 80-90% will probably be researched prior to entering store
      • This number is a bit high – it’s closer to 70%?
      • Seamless vs “Elegant seams” – customer knows where the difference is between channels
      • Mobile is still conversion-based, whereas people are commitment-phobes, so they want an experience
    • Went to RetailWest in Palm Springs – a CEO had a kid who took a picture of shoes that he could make
      • Posted on Instagram, saw how many likes
      • Eventually bought the shoes, got likes, etc…
      • Friends asked him about where he got them and he ended up building that ecosystem (but it’s not just shoes, it’s everything)
    • Toothbrush industry – how do you tell people to get a new one?
      • In 1980s, created an indicator bristle to tell customers to buy a new one based on the data
      • “What’s your indicator bristle?”
    • Existing companies out there – reality for mobile being a thriving way out
      • No singular model anymore
    • Stepping stone to AI: messaging / chat – voice – AI
    • Enabled physical world with digital (gift card originally with Apple and could take a picture and get it into iTunes)
      • Talked about OpenTable reservations – expected to have it, otherwise painful
  • How Strategy is Evolving / Staying in the Hypergrowth Digital World (Discussion in Digital, 1/18/17)
    • B. Gregg, Jacques Pommeraud (fmr Salesforce), Jon Weinberg (Sephora), Dianne Esber
    • Still a role for 3 year vision?
      • Yes – any company, any phase for what you’re shooting for.
      • Strategy could be an annual process with execs getting together – not likely going to work. Needs to be more often.
    • Sometimes smaller companies don’t see the need – how do we get bigger companies to do the vision quicker?
      • Bigger/broader vision (eg geographic expansion) hard to get passed out to the company as you get larger
      • Value of strategy comes from mobilizing the strategy
    • Architecting the strategy – has to be crisp enough to make pivots without issues, consistent framework
    • At Salesforce, what matters is customer trust, then customer success
      • V2MOM – vision, values, methods, obstacles and measures
        • Vision helps define what to do, values establish the most important
          • Customer trust / success
        • Goes down all the way in the company via a memo, accessible for everyone
      • With Facebook, used example of Sheryl Sandberg’s “Have hard conversations.”
    • At Sephora, rapid expansion – how do you continue to fuel the growth? Client-centric ideas, but how do you support it?
    • Is there a role of strategy to recruit?
      • Culture of leadership – used example of sciences like data scientist / VR value and what talents can help any company
      • Need app developed, whether it’s Bird or GE or Sephora, have to bring in people to work
    • At Salesforce – Jeffrey Moore has the 2×2 matrix for 4 zones
      • Efficiency zone (non-core activities: SG&A, support), Performance zone (core biz: 80%, execution)
        Innovation (lab, test – few things to try – new biz, products), Transformation (performance hopefuls)
      • In tech – birth of AWS – complete leader and supply chain
        • Siemens – institution-based, machines/phones/manufacturing and now high-tech medical instruments and big data
          • Data lakes, analytics and data-providing
    • Ultimate investment in next 3-5 years: People, data curation (either over-investing in tools that don’t need) or army of people (same problem)
      • No difference between retailer and tech or other companies
      • Personalize experience, data, customers
  • David Zhao? Baidu (Mastering Innovation, WhartonXM) – same day as ThumbTack march 14
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    • Talked about AI in cars (from SF Bay Area)
    • Ford’s “Faster horses” comment compared to just building the cars
      • Infrastructure came next – roads, highways
    • Initial autonomous thought sensors would have to be everywhere, infrastructure improved
      • Autonomous and sensors started taking hold once prices came down and placed into cars
    • Didn’t have a great guess – but 10 years, and he wasn’t sure how
    • Said a confidence interval would place the number of miles needed at 4 billion miles (and they’re at 800k?), 5000x necessary for Autonomous ‘comfort’
  • Michele Gelfand, author of Rule Makers, Rule Breakers (Wharton XM)
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    • How tight and loose cultures wire our world
    • Establishing relationships based on being tight / loose – none of us are across the board
      • Different tendencies – clean / neat, on-time, etc…
      • Working looseness or tight
  • Allbirds Co-Founders Tim and Joey (Built Product, Wharton XM)
    allbirds-logo-fb

    • Talked about the business strategies they’ve had to employ, learn
    • Shoe laces that were excellent but cost too much
    • Having different soles that were 3x in cost, carbon 0
  • Adam Pritzker (@adampritzker), Chairman & CEO Assembled Brands (20min VC, 3/15/19)
    • Working capital and financial services, along with cofounder of General Assembly
      • Forbes 30 under 30, VF, amid others
    • Many in his family are entrepreneurs, so he felt it naturally
      • Empowering entrepreneurs – GA develop digital, and with Assembled Brands – products and goods
    • Optimistic about retail
      • Consumer spend much higher, and retail is closing faster – closes > openings
      • 90 of the top 100 brands lost market share (which means others are growing quickly, smaller / emerging brands)
      • Khait (partnered with a founder), and was able to look at the data in order to get a capital infusion for d2c side
    • Could map out the value chains
      • Incubated, operated emerging mobile-first, built distribution, developed creative content, financial p&a, benchmark and underwriting
      • Exists before vs exists today – as they re-platform retail, financing brands can be done differently
      • Traditional: purchase order would be collateral for bank but now buyers are individuals (banks and factories can’t underwrite this effectively)
    • One brand as brilliant vs another category not working
      • Very few SKUs and strategy can underprice incumbent by selling direct to consumers (mattresses, eyewear, razors, etc… as exception)
      • Adding channels adds complexity
        • Online/offline, direct/wholesale
        • Technologies for attending to business Shopify, Google Analytics, HubSpot
        • Creative/Quantitative marketing and product dev / manufacturing all to coordinate
    • Providing the network of founders and capital based on some of that data
    • Bigger problem isn’t price, it’s finding customer value wrong
      • Happens often where costs change or aren’t understood
        • Too many SKUs, optimization of prices around products
    • Saturation of marketing channels
      • Founders don’t realize that they can get to $30mln and cut growing to just get to profit
        • Safely growing, cutting can be analyzed – if costs remain the same but revenue squeezes, or dips
      • Instagram is the new QVC (best leveraging for social discovery)
        • Instagram empowers the content organically – ex of Halo Top
    • Fundraising infrastructure as broken – said it’s getting better
      • Old: Made samples, showed to stores, got purchase orders, use them as collateral for $ from bank, made goods and sold in store
      • New: Software and systems need to get streamlined to make it easier as the channels have gotten more complex
    • Niche exits more often than big ones
      • 70-80% ownership of the company vs multiple rounds and only 5% – can be similar exits if capitalized properly
    • Coddling of American Mind & Upside of Stress (Strive to be antifragile and to invite stress, fragility) as books
    • Design / brand that he likes: Uhuru Design, founded in 2004 in high-end furniture, contract division built out over time (d2c to enterprise)
    • Scarcity of financing and benchmarks, no IRL community for physical goods
    • Organic referral and repeat purchases (always delighting customers should be great way for a virtuous cycle)

Data + Opportunities for Masses (Notes March 4 – March 10, 2019) March 28, 2019

Posted by Anthony in education, experience, finance, Founders, Hiring, medicine, NFL, questions, training, Uncategorized.
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This week was all about different types of people chasing and building what they wanted to build. What drives people – what are they drawn to? Passion, energy and asking the questions to further the quenching of thirst for the next step. Reading the notes I had for this had me down a rabbit hole for each one – thus the delay.

Interestingly enough, these founders, presidents, authors and data scientists / explorers are in different industries. We had digital tech and marketing, strategy, data science as it applied to healthcare, NFLPA / financial literacy, and education of cs and tech stacks through ISA’s.

Believe that you can learn from others to further what you do to progress forward.

Steve Mast, President and CIO at Delvinia (Measured Thoughts, Wharton XM)
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    • Using Methodify for geolocation data / surveys
    • Digital tech to help marketers, researchers and leaders collect, visualize and enable data
    • Educated as an architect, then video game designer and producer in the 1990s
    • Joined Delvinia in 2000 to build interactive design and digital marketing
      • Talked about doing events where they get volunteers to sign up for brand / marketing analysis
      • Ask 2-3 questions that are pointed, geo-enabled for brand / important points at the event
      • Makes sure not to have personal identifiers
  • Joseph Jaffe (@jaffejuice), author Built to Suck (Wharton XM)
    • Admiral, co-founder at HMS Beagle, strategy consulting for surviving
    • Talked about how Harley Davidson is in every marketing book but what are they doing now? Floundering
    • Nike ads – never talked about the product (shoes), but call to action – Just Do It
      • Nike as providing the tools for which you act
      • Used their stores as ex of environments for their product – having treadmills
        • Each employee was a runner, wearing Nike and touting the products, experts
    • Remembers asking his class if they knew the first bank to implement ATMs
      • Didn’t provide the answer – jumped into 4 P’s – one student asked what the answer was
        • Answer was that it didn’t matter because every single bank mentioned had ATMs
      • Only thing that mattered – first-mover’s “advantage” if you can keep it
      • “What are you doing now?”
  • Chris Albon (@chrisalbon), Getting First Data Science Job (DataFramed #55)
    2672ecf4-b76e-478c-9407-fc48877479bf-1515514797324

    • Data Scientist at Devoted Health, helping to fix healthcare system
    • Co-host of podcast Partially Derivative, since stopped, and had a kid / moved
    • Humanitarian non-profits, working on team for building companies with a soul
      • Devoted – health insurance company started by Todd (CTO of US) & Ed Park (CEO of health company)
        • Creating company that you’d want family members be a part of
        • Make healthcare that works (primarily senior citizens, Medicare)
    • His background is from quantitative political science – politics and civil wars
      • Perspective of research, experimental, statistics – PhD with these fellows
      • Meeting friends with a ton of amazing, applied projects (LinkedIn, etc…)
      • He needed to be applied vs research in order to get out of academia – joint Kenyan nonprofits (election monitoring and disaster relief)
      • Real data or fake reports, safety, ethic and morals come up – threat models aren’t the same
    • First hire at Brick (free wifi to Kenyan homeless, etc…)
      • Using established tools to provide others data / analysis – for a team to not know that going in, it was impressive (wizardry)
    • As a team, you can hire and absorb senior data scientists
      • People who got first time jobs at Facebook or something, got to see scale and experience that they can move on easily
      • At a Facebook/Google, end up doing heavy data analyses for the massive scale and is a big role
        • Hard, analytical challenges
      • Smaller companies may ask someone to do a ‘full stack’ / general data scientist that has to build everything on their own
    • Early on in hiring process – ex with Master’s in ML, and that’s what you want to do
      • Generalist builders at Devoted, but not strictly ML or other thing
      • Heavy AI or ML would be theory-based, dissertation level technical discussion (obvious focus)
    • Doing data science generally – many other problems – Bayesian analysis, RF, etc…
      • Far more jobs for those that are generalists at companies for business data – predicting drones watering crops, customers churn, illnesses
    • With different backgrounds, should figure out how to feature yourself & experience
      • Side projects, blog posts, portfolio, visualizations in a way that’s easy – testing, GitHub, versioning
    • Talked about his first meeting at Devoted Health – 4 data scientists in the room with a doctor, discussing the coding of health / diagnosis
      • Said he was fascinated in the meeting as he wanted to know that side, new business
    • He genuinely enjoys new techniques, analysis that he doesn’t know and learning about it – passionate about what they are and learning
      • Not hiring for junior – it’s because you will want to grow into senior
    • RF > SVM since it works out of the box, but said SVM is an awesome mathematical tool
      • Used it as a teaching point and visual – but in production, he’d never seen it
  • Eric Winston (@ericwinston), President of NFLPA (Wharton XM, Leadership in Action)
    • Talked about how important relationships and the soft skills were
    • Financial literacy as a passion of his – talked about how little players know going in, especially after college
      • College finance doesn’t teach it, either
  • Austen Allred, Founder/CEO at Lambda School (20min VC 3/8/19, FF)
    untitled

    • Bedrock, GGV, GV, Stripe and Ashton Kutcher as investors – $48M so far
    • Prior, Senior Manager for Growth at LendUp and co-founded Grasswire
      • Income inequality, financial health thoughts – nothing was moving incomes
      • Was in a small town in middle of nowhere, Utah
    • Had to live in his car in SV for a while and figured out how to schedule – during summer, would get hurt obviously
    • Raised $500k initially, couple months of cash left, due diligence – investor decided to not continuing Dec 23 (daughter was born soon after)
      • Never wanted to be in that position again – thought it would’ve been VC but it was more about a successful business
      • At YC, wasn’t focused on demo day – modeled 2 scenarios: 1 with VC money vs otherwise going wrong and seeing no VC money didn’t work
    • About the right time to raise: $1 today would be $3 or $4 later, still had much of their series A – getting dozens of VC emails and say no
      • No goal to raise B at that point, walked through the numbers with Jeff (one of investors) over dinner
      • So Good They Can’t Ignore You (Steve Martin quote, but Cal Newport book)
    • Looking at product-market fit – people would pay whatever to get to the job / signal
      • Incentives aligning, job and person – $1000 to start and pay after getting a job: Got into YC and thought no upfront deposit, etc…
      • List of 7k people, trying to refine and make sustainable
    • Training people online was tough, free upfront / no SITG – no Bay Area / NY, online engineering students
    • Iterating on all facets of business so quickly: had to do it, quickly and concurrently
      • Each 5 weeks do a project, roll people together and do an app – if they can’t, roll it back
      • “Insane” – but more people just can’t fathom DOING, the ACTION
      • Before running the experiment, they determined the metrics for success and failure (if it doesn’t happen, fail)
      • Career coaches / meetups / staff bonuses for people trying to get people hired – success of those 8 trials
    • Wright Brothers biography book and Les Miserables (humanity)
    • Changing SV – fundamental human problems, he wants them to build more, try more
    • 500k students in the year for 5 years goal

The Journey (Notes From Feb 25 – March 3, 2019) March 22, 2019

Posted by Anthony in cannabis, education, experience, Founders, global, Hiring, medicine, questions, social, training, Uncategorized, WomenInWork.
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I wanted to focus on the variety of journeys that these amazing people have been  on. All different, all learning. The commonality of assessing one’s place and moving strategically to take advantage of an opportunity that allowed each of them to do what it was, at that time, that they wanted to do or focus. I believe that is an innate skill.  Some have to build up to have the confidence to assess what they want. Others let it sit in the back of their mind until someone brings it out.

As a founder, I believe that becomes even more of an important skill. You have to not only know what you want to chase, but also where you want to go. Then, follow that up with being able to creatively attract others to do the same – whether they’re investors, customers, or potential employees/partners.

  • Julia Silge, co-author of Text Mining with R (Data Skeptic 2/22/19)
    cover

    • At StackOverflow now, phD in astrophysics, astronomy
      • Worked in academia and went to edtech start-up for academic development
      • Transitioned into data science – had needed to brush up on some of the skills and updated machine learning
      • Data scientist for 3-4 years
    • Did some public work for her portfolio, worked with state stuff on Drought, etc…
      • Thought about NLP for analyzing Jane Austen texts (public, projectgutenberg), and opened it up
        • Which parts of book have narrative more sad / joyous and sentiment analysis with heat maps
      • Started to develop TidyText package and R build with a friend – bridging text and R analysis
    • Using R as data science
      • Tidyverse database, messy real source & into the form she needs quickly
      • Mature community for statistical modeling in R
      • Text classification – regex as building blocks for effective results
    • At StackOverflow – texts every day and statistically analyze the numbers
      • Developers survey as one of the largest projects
    • Book for people who may have tried other approaches with text
      • 1st half lays out concepts, common tasks in text mining
      • 2nd half is beginning to end case study – eda, what’s in dataset, implementation of model
  • Brian Wong, Founder at Kiip (20min VC FF018)
    230px-kiip_logo_image

    • Started after university at Digg (laid off after 6 months) before starting Kiip, focused on mobile rewards network
    • People he truly knows are the ones he’s been with over 5 years
      • True Ventures, Relay Ventures, AMEX ventures, Hummer Winblad
    • Founder-friendly in his terms: creating services and ecosystem of the founders among the invested, not taking a massive chunk immediately
      • Services as you’re getting formed, early on
    • Quiet with his board – once every two, three months meet up, depending on financing
      • Sources for him if he needs others, find specific customer or advisor, analytically looking at problems
      • Trained by True Ventures initially about dealing with the board
    • Gamification tactics derived from Predictably Irrational book
    • “Nothing is ever as good as it seems and nothing is ever as bad as it is”
    • Jason’s Calacanis blog – seems to agree with a few
    • Inspired by a few founders: Elon, Elizabeth Holmes; moreso maybe less loud founders, Mike (one of his investors – NASA scientist)
    • Favorite apps: Tinder for dating, Evernote, Box app (storage – mobile app is awesome – faster than DropBox)
    • For Kiip, ad-blocking fever-pitch and being ones that can help – MasterCard as one of their big partners, usage / app data that they’re sitting on
  • Matt Lerner, Distro Partner with 500 Startups (20min VC 082)
    i9jumtd3_400x400

    • Runs the London office, specializing in conversion optimization, analytics engagement, retention
    • Helps them build and grow (Distro team) to growth engines and scale
    • Worked at PayPal in 2004, marketing director initially before later
    • Skype calls over 45 minutes, brainstorm over tests with a cycle time and see results in 48 hours – 2 days
      • Was told he could do this full-time and enjoyed it (Distro Dojo – growth to product-market fit)
        • Invest in post-seed, pre-series A typically – early stage / accelerator program for earlier
      • In London, he looks for live, functioning product with corpus of people out of beta
    • Talked about Mayvenn (connecting to the NEW episode about Series B) – Series A here
    • Where in the funnel do you need to focus on?
      • Understand the business, then brainstorm in the “dojo” – all kinds of ideas
        • 20% CTA button change occasionally – not always
    • Just invested in Fy, Founder Tom in Berlin – built entire business with growth in mind
    • Anna Kerenana “Happy families are all alike but each unhappy family is unhappy in its own special way.”
      • Companies don’t get their product out to customers in a way
      • Measuring / optimizing for wrong targets
      • Tactical things to ensure spend is done properly
      • Way to test quickly – 4 Hour Workweek – Bought 5 different ad-words and checked his titles for unpublished book
      • Paid acquisition in way that CAC is much lower than proven LTV of customer, can go quickly through advertising
        • Most businesses need organic acquisition channels over paid
    • Ultimate growth hacker – David McClure (his boss) – pirate metrics talk (viewing of video)
      • Sean Ellis (from DropBox, GrowthHackers.com owner) – mentored him at PayPal – attachment too big for email, send DropBox
      • Eddie Johns (Growth at Wealthfront, before at Quora and Facebook)
      • In London, Millen Paris?
    • Favorite growth hacking tools: MarTech talk, 500 Startups for best tools
      • Deck in show notes, Top 35 and Top 10
    • Books: The One Thing You Need to Know?
    • Tamatem – exciting startup in Dojo, Middle Eastern mobile games publisher
      • License other successful games, translate them, half the revenue and found money for developers
      • Don’t have to be good at making games – just need to have the database and quick adoption of other games
  • Chuck Smith, CEO / co-founder of Dixie Brands, Cannabusiness (Wharton XM)
    dixielogo

    • Discussing CBD vs THC products and difference in integration / vertical distribution
      • THC requires state and full distribution
      • CBD can be sold online
    • Keeping the brand as a reputable one and making sure it sees plenty of time
    • Partnership with Latin American company for full integration / distribution channels, laying foundation for easy process
      • Ventures with other companies to engage quickly or acquisitions
  • Solomon’s Code authors (Wharton XM)
    • Olaf Groth, Mark Nitzberg
  • The Ultimate Side Hustle – Elana Varon (Wharton XM)
    • Different types of start-ups and trading compensation (time vs money)
  • Marvin Liao (@marvinliao), Partner at 500 Startups – SF accelerator (20min VC 083)
    • 10+ year vet at Yahoo!, came to Bay Area/Silicon Valley in 1999 tech boom, laid off  2001
    • Left Yahoo in 2012, did angel investing and speaking at conferences, mentoring
    • Learned investing game by angel investing, though, to his wife’s scolding, didn’t do well
      • Operator as investors – used to be in the same role – lots of services
      • Online marketing / sales experts in accelerator in the portfolio
      • Both models-Greylock, Accel vs 500 Startup & First Round,service-based)
    • Why 500 Startups? Strongly focused on sales and marketing – fit for him, especially being international (global)
      • First 2-3 meetings or intros are free, but after that – some value returned
    • Went from 1100 companies down to 36 for the accelerator
      • Seed fund – 12-30 cos a week, one inv ~2 weeks – not necessarily random
    • Average check size is $50-100k – doesn’t take board seats but gets board observer rights
      • Look at pre-launch phase, consumer mobile phase wants to see traction (10mil vs 1mil downloads)
      • Won’t look at enterprise SaaS pre-launch, wants to see $10-15k mRR in established space
      • Different industries requiring different attention
    • Industries that he’s looking at – marketplaces / platforms (SkillBridge), digital health
    • Challenge in his 2 years: cycles of learning (shocked that there are arrogant investors), still treats himself as a complete novice
      • Great investor and develop the instincts, thesis and to risk being wrong a majority of the time
    • Favorite book: Art of Worldly Wisdom, Dune (science fiction – key) – SingularityHub
    • Calend.ly and Evernote, Amy.X.AI (?)
    • Take on Yahoo: “They’re toast.” No disrespect to Marissa – trying M&A and most big companies aren’t good.
    • Challenge for 500S: scaling @ quality, going from 2 accelerators to 4 in Silicon Valley
      • Lucky and systematic difference to get to that point
    • Interested in the most recent batch: Neighborly (batch 10, fintech – hates Wall Street so disrupting this), AgFinder (agtech – not much attention but such a vital part of the global problem)
  • Ashley Whillans (@ashleywhillans), Asst Prof at HBS in Negotiations, Orgs and Markets (Wharton XM – Time Poverty)
    • Went through study in Canada with subjects that would receive $40
      • One group subjected to restriction that it has to be spent on “time saving”, other could be whatever
        • Measured happiness after each day (with a call)
      • Time saving could be fast food of some sort, hiring a neighborhood boy to shop, etc…
      • Happiness was higher with the $40 spent for time saving
    • Check the white paper for time saving and happiness
  • Elizabeth Hogan, Brand Dev at GCH, Cannabusiness (Wharton XM)
    • Discussing various levels of products – CBD vs THC and other treats
    • Company founded by Willie Nelson in 2015
      • Willie’s Reserve (flower, edibles, vape products at both med and rec dispensaries)
      • Willie’s Remedy – CBD oil-based products – talked about the neuroscience behind activation with cbd products
    • 8 oz cups of coffee with 5mg dose of CBD – often bring as product demos for concerts, festivals, events
    • Marketing is difficult because of federal regulations and the big marketing channels – Facebook, Instagram, Google, etc
      • Some influencers have been used but have to be careful – can lose their accounts if wrongly done
    • Plenty of organic marketing currently, but looking for paid channels has been a difficult task
  • Hooked author, Nir Eyal, (Wharton XM)
    • Habit building – playing on pains
      • 4 different ways to take market shares
        • Velocity, frequency (think)
      • Pains as psychological effects – pleasure as a result, and minimizing pain

Location, Location, Location? (Notes from Feb 18 – Feb 25, 2019) March 15, 2019

Posted by Anthony in education, experience, finance, Founders, global, questions, social, Uncategorized.
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As I sifted back through the notes I made from that week, a common theme appeared as location. I listened to a beer company COO, Principal at Bain, Jim Collins, and a managing director. Location was key for each of them, in some way. Whether it was funding by turning rocks over in the local area, expanding slowly to keep an authentic brand, focusing on location and presence, location was a motive for each of them.

So I ask how location matters to each of us? For me, I’ve stayed in Northern California basically my whole life, primarily the bay area. It built me, but I felt like I was missing some aspect of evolving into a better self until I left the country – visited places for the primary purpose of finding what made others tick – ones I had minimal commonalities. What was different, what was the same, what’s thought common that I don’t consider common? I’d been to other parts the United States and the answers to these didn’t click until leaving.

After reflecting on these episodes, I’m torn between becoming so entrenched and specific to a great place like the Bay Area. However, being elsewhere has shown me that I think there’s greatness lying outside of these bounds. So I’d lean toward focusing on those opportunities, personally. What do you think? Enjoy your community or go searching for new?

  • Yards Brewing, COO Trevor Pritchett (Work of Tomorrow, Wharton XM)
    yardslogo

    • Talked about some metrics for their capital intensive brewing
      • Barrels per sq ft typically
    • 50k barrels all in the production house / taproom to be able to see the production and create experience
    • In 4-5 states currently, looking at $300+ revenue per bbl, 50% margin
    • Seeing so much acquisition of new brands and crafts – “purchase authenticity” rather than innovate
      • Bigger companies are near 3mil barrels, so 60x
  • Weston Gaddy (@westongaddy), former Senior Principal at Bain Capital Ventures (20min VC 081)
    br4umccm_400x400

    • YC alum with Frog Metrics (handheld survey), cofounded in college from Founders Fund, YC, Alexis O
      • Created software, low-friction, to gather info for small stores while customers were in store
    • Strategy consultant for consumer product tech with Bain, initially
    • Grew up outside ALBQ in NM, went to college on east coast – how things go together
      • WisdomTree Funds – average investor to access the market
    • Start small, but work on a big focus (market) – small problems that tackle big issues
      • Thing he learned: asking the question “Why now?” – what has changed in the market/tech that created opp
      • Assuming everything else was bad in the past is a flawed framework
    • Investing-wise, he’s in NY and focuses solely on geographically sticking to the east coast (personally)
      • Time for valuations and investing has pushed early on the west coast
    • Product has the ability to win brand loyalty above all-else with technology and the media methods changing
      • Both digital and physical products (no longer single jingle commercial, or incentives in the channel to sell)
    • Passion projects and interests in the environment – does think that it’s important to specialize
      • His is enterprise technology as it pertains to selling to CMO – marketing tech
        • Largest spenders in the corporation over the next 5-10 years
      • Sector expertise becomes more important as you get further in the company investing stage
    • Favorite book as The Sixth Extinction, Pulitzer prize – human-created mass extinction
    • Most recent investment was a second tie-on for Jet.com
  • Jim Collins as guest (@level5leaders) (Tim Ferriss Show #361)
    • Socratic advisor: asked what Tim did his PhD thesis on – language acquisition and ideograms of different approaches / pros/cons of each method
      • Had to pick a language, selected Japanese (though he did Chinese in college), and acquiring concepts and thinking of different culture
        • Sounds, tonal language (Mandarin) vs writing and the etymology
      • How much of language constrains or enhances concepts we develop (whether math or otherwise, set the limits of our world)
      • Asked about Macphee (sp?) – been holding/carrying his notes from this class since 1999
        • Jim said he’d gone back to read one about fires
    • Example of choosing the right conceptual vessel
      • Started with Good to Great research – algebra with numerator and denominator canceling out
        • Only studying successes won’t be comparative enough (think – all successful companies have buildings, does that indicate?)
        • Births of industry, explosion of new entrants – look at twin companies – why does 1 become Intel vs other?
      • Hierarchy of levels with leadership
        • Individual capability -> good team skills -> manage -> leader -> ambition with humility/will
        • Look at every speech / interview and count how many times they take credit for themselves vs others
          • Vertical pronoun I vs others
    • Time management of old
      • As a 36-yr old teacher at Stanford, taught entrepreneurship – asked “Why don’t you go do something on your own?”
        • Initially, nobody knew who he was so he could go into cave and work as he pleased – invisible to visible would scare him
        • Decaying quality of happiness: 50% new, intellectual work, 30% of time in teaching and 20% in have-to-do’s
          • For each day, accounting the day – “got up early, creative hours, breakfast Joanne, workout, 5 hours creative, dinner”
          • Can’t be below 1000 creative hours in any 365 day period, and looking at monthly numbers drop
          • Pattern in his spreadsheet – emotion columns, +2 to -2 and rates – simplicity had a lot of +2
            • Arduous days were also +2
        • Creative days sometimes not what he thought might’ve been creative
          • “you’re a genius with 1000 helpers” – shell of a company
          • Even in writing with a friend, he’ll write down 3 things he wants to chat about – even if he doesn’t get to that
    • Asked to be a sleep student and reached out to a sleep center for himself – slept and had the electrodes on, as he figured out
      • 10 day cycle sleep – (El Capitan in a day – had to be up 36 hours in the day) – wanted 70 hours of sleep
      • Fun if you wake up to guess what time it is – 20min awake, need to get up
      • Gifted to be able to nap whenever, wherever
      • “Do the bug book” – on Jim, himself – how to deploy himself
        • What are you encoded for vs what you’re good at? Fund your goals and objectives with the economic engine.
        • Started a book at request of Rachell Meyers – observing the bug called Jim
    • Wanted to dwarf 1-60 with 60-90, as Drucker discussed in Effective Executive (Jim did foreword)
      • Owing the respect to mentor times – prepare ahead and codify/reflect notes after
      • What was his fav book – “the next one” (and he went to write 10 more)
        • Asked to know where Drucker was in writing at 65 – about 1/3 through what he did
      • Don’t do 100 decisions if one can do – say, events – is there a teaching moment?
      • “You’re asking the wrong question. How are you useful?”
    • Where does Flywheel start? Jim’s: What is he curious about?
      • Research correctly and he then can’t help having ideas and concepts from that.
    • 8 mi run for his “I’m thinking of upping my mileage” – 3 mi uphill
      • Joanne was one of original “Just do it” athletes – had won Ironman in 1985
      • Got married about 6 months later
    • Importance of empirical validation (vs pure analysis)
      • Fire bullets and it’s off by 30 degree, then another at 10 before hitting – then extend cannonball on calibration
  • Erik Moore, Base Ventures MD (Launch Pad, Wharton XM)
    x3rgt2at_400x400

    • Why he chose Berkeley – 5 min from his house
      • Likelihood to run into his mentor more in same building
    • Investing in Olly which was former founder of Method seeing opportunity in packaging branding of med
      • DSC and similar with just quirky videos for brand
    • Different investment in Mayvenn – weave/hair extension distribution landing their funding: $23M from a16z
      • Changing dynamics of economics for hair stylists and making it better experience for customers
      • Good, Better, Best price discrimination tiers

Shifting Mindsets (Notes from Feb 11 – Feb 17, 2019) March 7, 2019

Posted by Anthony in Automation, education, experience, Founders, global, medicine, questions, Uncategorized.
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I think many [most] of us fall prey to the day-to-day grind of life, work and whatever we have going on. We lose out on collection and reflection, maybe some have the time to gather their thoughts. Rarely, unless asked, do we get the chance to work through introspection. The luckiest may then analyze this and work to change their mental processes. If I had to guess, I’d imagine people work through this in conversations or in reading (or both).

What do my thought processes look like? Are they consistent? Do I work through problems in the same manner, or are they dependent on material or background? Have I worked to improve?

What did I hear/read about that may influence these? How does what I worked on or read about influence my opinion/knowledge of other situations/circumstances? Is there a fundamental shift that’s occurred over time? Should there be? Can I anticipate that? Can I preempt the movement?

As the week’s theme, each of the partners or founders on the episodes assessed the known framework for which they were improving upon or changing. At Brightseed, they’re focused on asking questions on how we can transition from infectious diseases to chronic disease prevention, all in a system that has been built and established on the former (big pharma). The diagnoses, the business model, research and development and actions thereafter all influenced by how it has always been done.

  • Sofia Elizondo, co-founder and COO at Brightseed (Bay Area Ventures, WhartonXM)
    brightseed_logo_standard

    • Talked about how 150 years ago, as pharma started to ramp up, system developed to treat infectious diseases
      • Now, infectious diseases are not the primary, and chronic diseases have taken over
      • Not prepared industry-wide to deal with this (pharma designed for diagnosis and then action)
    • Food industry is health and wellness, some biotech companies trying to make names
    • Mentioned performance of big package food companies – lack of growth, especially only 2% for the biggest names
  • Joel Marcus, Exec Chairman of Alexandria Real Estate (Wharton XM)
    logo

    • Talked about only wanting to buy real estate, commercial lab buildings in bunches and lease or sell to biotechs
      • Have dealt with commercial labs
    • Google had approached and they didn’t like it initially but agreed on the premise of what they’d do
      • Uber is another, soon to be 1million acre real estate for them
  • Patricia Nakache (@pnakache), GP at Trinity Ventures (20min VC 080)
    trinity_logo_light_300dpi-447137163032

    • Father was one of first engineers at Computer Sciences Corporation, spent summers in labs and telecom intern
      • Physics and chemistry major in college – went to McKinsey after graduating
      • In 1999 went to a start-up but was also a freelance writer, later had Forbes articles, etc…
    • Interviewed Trinity Ventures in 1999 and they continuously said they were very busy
      • Joined as a contractor, then 3 months later converted 3 months later
    • 2 P’s related to gender difference in venture:
      • Pipeline: nurtured through apprenticeship model, or brought on as a senior role initially
      • Pattern recognition: notion that decisions are based on what attributes have contributed to past successes
    • Care.com has vetted profiles of caregivers for hiring – more concerning for Patricia, so she is attracted by that pain point solution
      • Ruby Ribbon partnership and learning
    • Pedal to the metal for spending and burn rate – product-market fit with a broadly-defined market
      • E-club, meal services to companies (eg 100 ee’s, goes on the app and pick what you want – variety and meals delivered at once)
    • Measuring success – returns to LPs and helping founders
      • Jim Collins “From Good to Great” – ew, but makes sense if she grew up with initial tech boom
    • Fan of on-demand economy / services, but believes there will be a shakeout of it, as well
      • Scheduled cleaning, for instance (not on a whim)
        • Cites Homejoy – wasn’t sure what the app strategy was solving, immediacy
  • Cal Newport, Digital Minimalism book (Wharton XM)
    • Talking about reducing technology overall
    • Habit forming – diets, if you’re not committed to making a lifestyle change, can’t gain the benefits
  • Yogov founder, Ryder Pearce (Wharton XM)
    0y-5xkd4_400x400

    • Mission-driven looking for making gov administrative jobs easier, less painful
      • Hard to fix this as there are so many different 3rd party / privatization attempts
    • Spent 3 months in 2016 dealing with California DMV for what he felt should’ve been minutes

  • Phil Libin (@plibin), co-founder CEO of All-Turtles (Launch Pad, Wharton XM)
    220px-at-logo-red-label-stacked-opaque-2048

    • Attempting to cover real-world problems with AI
    • Focused on Evernote until starting this – have grown broadly to eventually include biotech
    • Talked about doing 72 hour fasts after being very skeptical (as a fad, etc….)

Builders for the Future (Notes from Dec. 24 – 30) January 16, 2019

Posted by Anthony in experience, finance, Founders, global, Hiring, questions, social, Uncategorized, WomenInWork.
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Hello all! It’s good to write again this week! And this one sent us back to the week of Christmas. Appropriate for this edition because I spent mine with my mom. She’s one of the strongest women that I know – the stars of their respective companies/start-ups/brands. They’re asking questions that affect all of us, whether it’s when we literally stay a Marriott Hotel, question our job choices going forward, try to learn a new skill, or even debating on jumping into a start-up with an idea.

I know I took quite a bit of the segments to heart and reflected on them. They covered a wide range of topics but ultimately, how do we move forward in whatever we’re doing.

  • Nicola Corzine (@ncTheCenter), Exec Director at NASDAQ Entrepreneurial Center (Bay Area Ventures, Wharton XM)
    nasdaq_ec_horizontal_rgb-144-jpeg

    • Enabled 7500 entrepreneurs in 65+ countries
    • 49% female and 65% minority as a big driver for her
    • KPIs as solving the business problems that are presented
  • Stephanie Linnartz, Chief Commercial Officer at Marriott International (Wharton XM)
    • Discussed how they had positioned to make sure certain hotels stand by their values
    • After buying Ritz Carlton, keeping it as Ritz Carlton brand
    • Host / rent-type brand and partnership to ensure that they can offer amenities in line with brand
    • Structure of Marriott being franchised, primarily
  • Ellen Ruppel Shell (@EllenRuppelShel), author of The Job (In the Workplace, Wharton XM)
    • What’s it mean to be satisfied at work? Depends on the person.
    • How gig economy changed mindsets – if it did? Many people want consistent paycheck
      • Control isn’t super valuable if they don’t know when the next paycheck is coming
    • Disagreements of people seeking ‘satisfaction’ or ‘meaningful’ employment – falsehood that has been pushed onto workers
  • Alice Bentink (@Alicebentinck), co-founder of Entrepreneurs First (20min VC FF 014)
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    • Founded Girls Code
    • Was originally at McKinsey when she started – best way to think about next steps when they were graduating from university
    • Ingredients for EF: exceptional technical talent, co-founders that matter
      • Idea is important but not perfect idea (doesn’t exist)
        • Idea that resonates with you to attack and start to build
      • Non-technical people need to bring domain expertise (used a translation expert with contacts/work in the space)
        • People with years of expertise don’t want to work in their domain – EF is pained by this
      • Selection based on the talent over ideas (changing them 2-3 times)
    • What makes a great founder?
      • Thousands of applications – built companies or startups or products
        • How to keep an open mindset and willingness to learn
    • First time founders worry too much about the startup than the product being built
      • Don’t worry about accounting, incorporation, logo
      • Do built a test product, get feedback, iterate quickly
    • Friends and family rounds can be dangerous if there isn’t an understanding of the risk or time horizon (say, pensions or house deposit)
    • Scaling from 15 companies a year to 40 companies
  • Kirsty Nathoo, CFO of Y Combinator (20min VC 075)
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    • Went from PWC in Cambridge, UK to YC and do-everything at a start-up accelerator
      • Got involved with YC through her husband, funded in 2008
      • Had a 3 week window to close up Cambridge and get to SV – was doing a ton of responsibilities
        • Control of all bank accounts, access to everything and it made her more trustworthy
    • First batch she was involved with was Winter 2010 and had 26 companies
    • 10 minute interviews questions – how big does it get? How does it adjust with getting customers what they want?
      • Have the founders thought about enough to make each other learn something new?
    • Obvious business expenses vs controllable business expenses – spending investor money and when to vary / change these
      • Founders can break up and it often isn’t ideal – YC makes sure to pay founders and payroll
    • Founders should be able to report a lot
      • How much $ in bank? Runway? Growth rate? Burn rate?
    • Most impressive interviews are ones that founders can articulate answers to questions because they’re so matched with their mission

Experimentation & Testing (Notes from March 25 – March 31, 2019) April 17, 2019

Posted by Anthony in Digital, experience, finance, global, Hiring, questions, social, training, TV, Uncategorized.
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I know, I know. It’s a bit of a cop out to use a Game of Thrones image on the back of the Season 8 premiere from Sunday. Sue me [please don’t]. And I’ll give credit to the image creator: Instagram @chartrdaily for the fun visualization. However, after listening to Pinnacle Sports’ Marco Blume, I couldn’t help after hearing deployment strategies for their prop bets on popular TV shows, such as who will be left on the Iron Throne or the ever popular “Who dies first?” props. They experiment, hypothesize, post a line with a limit (hedge risk) and let the market decide from there. And boom – we have the theme of the week!

Antoine Nussenbaum, of Felix Capital at the time, mentioned going from private equity to start-ups and venture funding where they had to decide between backing people or belief in the company. He got first-hand experience by starting a company with his wife, successfully gaining funding, and then exiting – only to fail with a different company that wasn’t scaling. How did he go through frameworks to decide on startups to fund or help?

Mark Suster gave his take on how he comes to investment funding – sales, technical skills and being aware of each. How did his entrepreneurship experience influence his framework for funding new start ups? Why is it that there is a sweet spot for amounts based on run rate? Experimenting, failing and adjusting.

Then I had listened to 2 data scientist / researchers in their discussions of NLP parts – what to test, what they assumed to be true, how to approach new methodology and testing this methodology. Is there a limit to the progression that can be made with NLP? Why might it be relevant to decide on testing state-of-the-art further? Then, ultimately, what’s the applications for how we can use that optimization to improve the current status quo?

I hope everyone checks out what may interest them – this was a fascinating and fun week. So much so, that I suggested to a few different students for them to check out different parts (granted, I do this often, but I was quite excited to share these ones).

Cheers!

  • Antoine Nussenbaum (@Nussenbaum), Principal and cofounder of Felix Capital (20min VC 084)
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    • Partner at Atlas Global prior, p/e fund that was part of GLG Partners
      • Working on digital early-stage, venture fund and helped startups bootstrap after missing the tech side
      • Miraki, Jellynote, Pave, Reedsy, and 31Dover as some of his best investments
      • Helped start Huckletree with his wife
        • Looked for investment of $80mln but got $120mln
    • Backing someone vs backing the company initially in early stage funds
    • Raised in Paris in international environment, lived in UK as well
    • Launched 2004 software-on-demand business with 2 friends “that was not scalable at all”
    • Did M&A in the UK after leaving software
    • Felix Capital at intersection of creativity + technology, lifestyle brands: ecommerce and media, enabling tech
      • Stages – flexible capital, but have made investments from $200k – $6mln, focus on Series A + B
      • Geographic – agnostic, as long as backing entrepreneurs
      • Advisory services and focused on helping their investment companies
    • More entrepreneurs that know the playbook and how they can build, grow and scale
      • Looking for more companies that can scale globally or expanding outside with proper funding
    • Using Triangle as an example – bathing suits on Instagram strategy and launching millions of product via digital
    • ProductHunt as a blog he gets lost in – 15 min of destruction
    • Lifestyle-related excitement: food side, better life, marketplaces
    • Hard Thing about Hard Things and Capital in the 21st Century – relationship of wealth and economic wealth
  • Mark Suster (@msuster), MP @ Upfront Ventures (20min VC 085)
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    • Was VP of PM at Salesforce.com before Upfront
    • Late 80s – had an interest in development as a student in college in the UK
      • Worked initially as a programmer at Anderson (Accenture) for 8 years
      • Entrepreneurship isn’t for everyone – better to start earlier, need to have a fundamental understanding of systems (coding)
        • Python, PHP, Ruby, JavaScript – not trying to become best developer – just knowing the systems
        • Sales experience would be second – telesales or customer support – ask CEO to do an hour a week of calls
    • Started 2 software companies – one in England and then Silicon Valley, selling both – backer brought him in to VC
      • Fred Wilson wasn’t an entrepreneur, but does give you the insight
    • Don’t get the sense of urgency with too long a time – 3 months vs 12 months
      • Too much capital creates laziness and shortcuts that lead to mistakes
      • 18 month run rate for capital – takes 3-4 months to raise (start with 6 months plus)
    • Wants to see early stage companies once a month, roughly.
    • $240mln fund – invest half into companies and reserve the other half for follow-ons
      • 3 year timeframe, $40mln with 5 partners – $8mln per partner
        • Series A, B rounds where each partner is doing 2-3 deals per year when avg is $3-5mln investment
    • On his blog, has the “11 Attributes of Entrepreneurs”
      • Best known post would be “Invest in Lines, not Dots” – x-axis as time, y-axis is performance (any given day, your dot)
        • Interactions create a line that matches a pattern and he can decide if he wants to do business
      • Not a big fan of deal days or investor days where you hype up a company because of this
    • 50 coffee meetings a year – once a week, if you meet 50 entrepreneurs a year, maybe you’ll become close with 5-10 of them
      • Single best introduction is from a portfolio company CEO for an investor
    • He knows and built software company – SaaS-space since he knows how to be helpful
      • Data and video tech industry (has 11 personal investments and 5 are video)
      • AgTech as an underappreciated industry so far – stays quiet until a few investments before hyping
    • Too much company, too much money and entrepreneurs clouding the market for everyone else
    • Book “Accidental Superpower”, how demographics and topology will drive the future and how areas grow
  • Marco Blume, Trading Director at Pinnacle Sports (DataFramed #54 2/18/19)
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    • Got into data science by “sheer force”, building quant team out from Excel going to R
      • Efficiency was by orders of magnitude since R was better than Excel
      • Could do anything with risk management, trading, sports
    • Pricing GoT, hot dog eating contest, pope election and making the lines
      • Use pricing and market analytics to let the people set prices
    • Risk management in general – maximize probability and hedging risk
      • Does the bottom line change? Does it affect anything? Regulations.
    • NBA where all teams have played each other – have a good idea of strength of teams
      • Soccer or world cup – not as much certainty with teams not always playing each other
      • Start of season has a lot more volatility and responsiveness to bets because of uncertainty
        • By end of season, bookmarkers have the price and knowledge, so they’re likely to increase risk
      • Bayesian updating
    • Goals to improve models, open new betting options to clients
      • Low margin, high volume bookmaker – little bit with a lot of options
      • Book of Superforecasting – group of people who are better at forecasting
        • Pays them already at Pinnacle – consultants, betting and paying the price
    • Much bigger R shop than Python at Pinnacle, active in the R community
      • R becoming more of an interfacing language and production language (vs C# or other), can use R-keras or plumbr
      • Teaching dplyr, rmarkdown and ggplot cover 95% of their work outside of specialists
    • GoT as one of his favorite bets
  • Matthew Peters (@mattthemathman), Research Scientist at AI2 – ElMo (Data Skeptic 3/29/2019)
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    • Research for the common good, Seattle, WA research
    • Language understanding tasks – ELMo (embeddings from Language Models)
    • PhD in Applied Math at UW, climate modeling and large scale data analysis
      • Went to mortgage modeling, tech industry with ML and Prod dev in Seattle
    • Trying to solve with very little human-annotated data, technical articles or peer-reviewed
      • Very difficult, very expensive to annotate – can you do NLP to help?
    • Word2vec as method for text to run ML on text, context meanings of say, bank
    • ELMo as training on lots of unlabeled data
      • Given a partial language fragment, language modeling predicts what can come next
      • Forward direction or backward direction (end of context), neural network architecture
    • Research community may want to use ELMo, commercial use to improve models already in prod
      • Pre-trained models available and open source
    • In the paper, evaluated NLP models on 6 tasks – sentiment, Q&A, info extraction, co-reference resolution, NL inference
      • Got significant improvements on results from the prior state-of-the-art models
      • Character-based vs word approach
        • Single system should process as much text as possible (morphology of the word, for instance)
    • Paper over a year old now but Bert was put up on ArXiv to improve upon ELMo (transformer architecture for efficiency)
      • Scaled the model that could be trained by many X’s, quality is tied to the size / capacity
      • Language modeling loss changed, as well (word removed from middle of sentence and predict before/after)
      • Large Bert models have computational restrictions – how far can you get by scaling the model
  • Kyle and early Data Science Hiring Processes (Data Skeptic 12/28/18)
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    • Success isn’t correlated with ability to give good advice
    • Conversion funnel for businesses: website that sells t-shirts, for instance
      • Tons of ways to bring people into the door / website (ads, social media campaign, ad clicks)
      • Register an account or put into cart (what %, track it, a/b test and improve)
      • Cart to checkout process (how many ppl? Credit card entered, goes through, etc…)
    • Do any sites convert faster than others? Keep track, find out why / focus on continuing it
    • Steps for job hire: video chat / task / phone screens / on-site next / offer
    • Resume should be pdf (doc may not open nicely on Mac or otherwise) – include GitHub
    • SVM – should have margins or kernel trick on resume (otherwise, don’t include it)
      •  Ex: ARIMA (auto-regressive integrated moving average) – time series data
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