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What’s Important for the Business (Notes from Sep. 16 – 22, 2019) November 5, 2019

Posted by Anthony in Automation, Digital, education, experience, finance, Founders, global, Hiring, Leadership, marketing, questions, social, training, Uncategorized.
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Ah, the art of learning. What can you absorb in the time that you allotted? Hopefully it was the good stuff, the one you can apply and remember. We’re not going to retain it all – far from it. Different surveys and studies will say between 10-30%, depending if you’re reading, listening, seeing things. Repetition, talking about details or applying what you’re picking up can improve those numbers – and it’s why there is still a ton of money being raised/made on improving it (Blinkist, Anki, Quizlet, rise of audio books and podcasts). This is all without bringing in the idea that the internet has allowed such a flood of ideas that opposing ones can exist simultaneously, persisting through its strong supporters. So, if you’re not doing research and coming to your own conclusion, it’s likely to be lead to whichever way you resonate with someone/something most (or first).

In reading through Constellation Software President’s letters to shareholders, you see a valiant attempt at conveying how he, executives and board members looked at the business health for the year – and refreshingly so, not explicitly through rose-tinted glasses. He critiques and suggests an option that reversion to the mean is possible based on a lower adjusted net income and cash flow from operating activities per share. Then he went through the shareholders returns on invested capital, average invested capital, and questioned the organic net revenue growth’s performance (as he notes that this is a primary core to the main metric for their performance: ROIC+OGr). Once he goes through the metric and it’s cash flow, he mentions that they’re looking to increase acquisitions, but the environment isn’t conducive to great values, so FCF may not be fully invested at attractive levels for the future. Then, he suggests a metric to cover this with a reasonable pattern, one less subject to shareholder alterations. Open to suggestions while he develops the reasoning for what another member has suggested for a good metric, he settles on FCF increase per share compared to average net income per share.

I loved his breakdown for the shareholders – mentioning half the shares trade for the year. He breaks it down simply as short-term, indexers, enterprising investors (including institutional, but also generally long-term, long-haul holders). He openly asks them to help find directors and members of the board and the difficulty that they saw initially after their IPO. The next paragraph was a big one, so I’ll include it:

Qualified and competent Directors are very rare, and not surprisingly, the track record of most boards is awful. According to the 2017 Hendrik Bessembinder study of approximately 26,000 stocks in the CRSP database, only 4% of the stocks generated all of the stock market’s return in excess of one-month T-Bills during the last 90 years. The other 96% of the stocks generated, in aggregate, the T-bill rate over that period. This means that 4% of boards oversaw all the long-term wealth creation by markets during that period. Even more disturbing, the boards for over 50% of public companies saw their businesses generate negative returns during their entire existence as public companies.

Wow. A) The recognition of wanting to be the best and provide a great board of directors for a long-time and B) genuine concern for the long-term view and suspicion of complacency arising. Both, I’d imagine, lead him to mention that vision / strategy are not necessarily courses of action – instead, perpetual objectives as the guiding point. Whether that’s seeking them out or maintaining what they had, he made sure it was top of mind. He sees that profitable VMS businesses may no longer come to be acquirable, and that he’s on the lookout for other opportunities – without them being attractive, though, he’d responsibly return FCF to investors.

Interestingly, he looked for Constellation to be devoid of “sycophants, mercenaries and spin-doctors” and wanted it to be a place where meritocratic results bring in “entrepreneurs and corporate refugees to invest their lives and and their capital and thrive”. Quite the statement for a business of such magnitude, when, especially from the outside, many succumb to the former (hell, take a look at Tech Twitter these days and a complaint I’ve had is that people seem to be comfortable bouncing between 2-3 companies a year for 5, 8. 10+ years). I’d love to build something that sustained a drive through many levels of employees.

“I find there is no magic to managing and leading. If you are smart, work harder… treat people fairly, do not ask them to do anything would not or have not done, share the credit, keep learning and keep teaching, then pretty soon you have followers. If you make sure that the team members are energetic, intelligent, and ethical people….”

Yup. That’s the way to build a company. Find that and hold on. And then he finalizes with the board requirements (which I’ll include at the bottom).

Hope you enjoy the notes this week.

  • Mike Strasser (@mstrasser), Motiv ring founder (Wharton XM)
    • Talking about the ring and how he knew the wearable would work
  • Lee Thompson (@flashpacklee), Flashpack founder, Marketing on a Budget (Wharton XM)
    flash_pack_logo_block-1

    • Photo journalist for 15 years
    • Talking about creating a brand through pictures, story-boarding, ethos of brand
    • If you can’t tell what your hook / pitch is, probably won’t sell
    • Went on first date with his now-wife from Match, wouldn’t tell him a great business idea
      • Post-wine glasses, she had a business idea for 30+ year olds wanting to travel – friends having too much of a family/kids
      • Adventure travel company for solo travelers in 30s and 40s, not tours via bus and such
      • Next few dates were researching travel industry, setting up a business
    • Book trip as solo traveler, then have others that you are meeting everyone else
      • Boutique hotels, price points established and like-minded – typically well off in careers, cash-rich
    • Launched with $15k each, savings and jumped in
      • Nobody would spend $1k+ on trips for a company that had no reviews
      • Generated a lot of PR, did a lot of viral videos by responding to twitter hashtags
      • Spent on Google ads and lost lots of money – built the website
    • Took a trip to Egypt on a budget, “come to Jesus moment”
      • If I can get on top of that and take a picture (Christ the Redeemer picture of a workman doing damage repair)
      • Wanted to take a picture on top and took a selfie
  • Mehrdad Baghai, Alchemy Growth cofounder & CEO (Mastering Innovation)
    the-alchemy-of-growth-full-1-638

    • Boutique strategy advisory firm advising large companies on innovation strategies
    • Designing organization architecture for growth, 5-10 years
    • Active investor in tech and p/e spaces with Macquarie Group
    • Former partner at McKinsey leading Growth Practice and then 3 years as Exec Dir at CSIR, Australia’s national science agency
      • Dozens of new tech companies
    • Also launched High Resolves with his wife, Roya, in 2005
  • Fred Destin (@fdestin), GP at Accel (20min VC 1/18/16)
    logos_master_accel

    • Former partner at Atlas Venture working with Zoopla (public), Secret Escapes, Integral Ad Science, Dailymotion (acq by Orange), PriceMinister (acq by Rakuten)
    • Studied life as derivatives at Goldman Sachs, first team on credit derivatives
      • Securitization of movie rights, derivatives in Pacific region for about 7 years
      • Opted out when it went from risk hedging to arbitrages
    • Moved to Speed Ventures for investing at really early seed
    • Spending a lot of time hiring the best 1-10 executives because you can’t spend time getting this wrong
      • Take a model that worked in 1 city (like Deliveroo), scaling it to 30+ and got there in under 3 years old
        • Fit consumer model and offering for the ones – brought new kinds of service to non-delivery food
      • Seed companies failing because you hire something you don’t understand – wrong team kills the team
        • Second mistake – overestimating the things you can do in the time – reality doesn’t match
    • Setting arbitrary goals for not being worthy of being funded – most companies run out of money or come close, being patient and empathetic with founders
    • Investors need mental plasticity for adjusting expectations on what to best deliver
    • Founders feeling screwed over because it was never possible for them to communicate the right decisions being made
      • Mix of market difficulty or overambitious of timing – how to improve intimacy and mutual trust
    • He likes to spend 3-6 months knowing founders – wants to do strategic sessions, whiteboard issues how you would solve it – discovery and disagree
      • Can work through disagreements, see how people work collaboratively
      • Engineer a situation of tension – hiring / decision made, create it to see pushback
      • Could we do an 8 hour wine test / road test – can we banter and have a pleasant time being together (Boston to Montreal, London to South of France)
    • Needs to ensure performance and milestones, sounding board, interest of company / employees / customers and investor with fiduciary standards
      • Had to tell guys at Real3D and say that they couldn’t invest – told them early, though
      • Mentioned Boston VC that said he’s said “No” so often that he just fizzles out – Fred said he tries to give constructive feedback but not always
        • He used to send very detailed No emails but would receive replies about not understanding opportunity and pushback – called stupid or not getting it
        • Now he responds with “Busy with other opportunities”, but sometimes he has things fall through the cracks
    • Favorite book: Mastering Margarita, missing and saying No to successful opportunities – doesn’t rue or look back like that because portfolio co’s do well enough
      • Success measure – how long it takes for knowing (16 years for him), took 10-12 for success as investor
    • Wasn’t super excited about returning to London but was pleasantly surprised about how vibrant it was – still US is more tolerant about money and quicker pace
      • Competitors share, acquisitions are faster – Accel moves fast so it’s advantageous but not overall
      • Boston wants to import the well of technical talent and ML – hubs working together in Europe will improve it
  • Thirteen Minutes to the Moon
    • Episode 7: Michael Collins: Third Man
      • Command module pilot for the mission
      • Test pilot before being selected as an astronaut – 90% luck he landed in that role
      • Someone wrote to Eisenhower that the best option for selection for astronauts would be experimental test pilots because apt to new scenarios and flight
        • Compared to deep sea divers or others
      • Collins had been turned down the first time to supplement that first 7 – after a year of more experience, selected in class of 1963 with Aldrin
        • First flight was 1966 on Gemini X, rendezvous and docking maneuvers
        • Once LEO Gemini flights were successful, Apollo XI was announced in January 1969
        • July 16, 1969 – launch sequence day – was responsible for launching lunar module to turn it around from Saturn V rocket
      • Was an English major and just did guidance verbs/nouns memorization to control it
      • As they neared moon, they were on far side and lost contact with Houston
        • Takes back everything bad he ever said about MIT – accuracy of system was ridiculous, 3000 ft/s and only had 0.1 ft/s in any one direction error
        • If something went wrong for landing lunar module, Michael couldn’t change his speed but it’d be up to him to figure out what to do
        • Mathematicians were responsible for coming up with a list of 18 variations for problems and what to do – some they hadn’t trained for
      • He felt alone, awareness of being on the other side of the moon, solo after Aldrin and Armstrong picked up speed on their way down

 

CSI Board Role Search Criteria
THE ROLE
Thought Partner – Thought partner for senior leadership.
Long-term Orientation – Unfazed by short term pressure. Focused on CSI’s long-term issues.
Timeframe – Able to serve on the board for 20+ years.
Investment in CSI – Willing to make a significant equity investment in CSI, above and beyond board comp.
THE CANDIDATE
High Quality Business – Understands what constitutes a high quality business.
Autonomy -Appreciates the motivational power of autonomy, decentralisation.
Cultural Fit – Respects and gets along with the current senior CSI management as well as the board.
Ownership – Believes in the motivational power of equity ownership.
High Impact / Low Ego – Will intervene when necessary, contribute meaningfully, but not dominate discourse.
Out of Kitchen – Can resist the urge to get into the kitchen when there’s a chef already in there.
EXPERIENCE
Builder – Helped build or maintain (as a director, manager or major shareholder) a large
organisation (>1000 employees) over an extended period, while providing a superior
return to owners (ideally including employee owners).
Decentralized – Experience with a decentralised company (nice, not necessary).
Capital Allocation – Experience in a capital allocation role (nice, not necessary).
LIKELY BACKGROUND
Family owned business operator or director.
CEO / #2 for exceptional business.
Entrepreneur
SEARCH PATHS
Multi-generational family owned businesses with high ROIC within reach of our
network and ideally local to CSI (increases involvement, eases reference checks, more
likely to know CSI, decreases absenteeism).
High quality businesses with strong shareholder alignment.
Great capital allocators in the corporate world.
CEOs with great shareholder letters and high quality businesses.

Inference Isn’t Just the Data (Notes from Sep 2 to Sep 8, 2019) October 14, 2019

Posted by Anthony in Automation, Blockchain, Digital, experience, finance, Founders, global, Leadership, marketing, questions, social, Strategy, Uncategorized.
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Internet has enabled more data, but that’s not necessarily a good thing for most. I’ve seen this for all ages – somehow, this deluge of information provides a glut where, instead of doing more research (because more is available), we seem to do less. There’s a laziness that has arisen, where the least amount of work is done because the sources are abundant. And this is problematic. And emblematic for what has transpired over the last decade with the web 2.0.

I say that it is problematic, but I do suspect it’s actually created a ton of wealth. The opportunity of doing a small amount of extra effort to sift through or provide a more nuanced/researched view in order to extract a ton more value from a wider audience is awesome. That’s never before been more evident or available to a wider group of individuals. Especially when, with nearly the world online, communities that would normally (past) not have had markets, all of a sudden have vast reach – internet enabled the connection across many neighborhoods/cities/regions/countries/continents. A magical thing for those that wish to do the research, put in a bit more work, and most importantly here, share the work for the public, at the cost of potential exposure to those that disagree or have reached a different conclusion.

I, for one, am all for this abundance. A sharing of differing opinions and agreement or stories of anecdotes allow us to bring more data into the fold. That should enhance any inference/analysis on the information brought to the table – and real applications, at that. However, like many good things, there will be a small portion of people that are bad actors or looking to just ruin the good derived from a community or topic. Also, may see plagiarism or curation that doesn’t really add anything – worse, monetizing the curation of something where the value isn’t created. For 100 people, 1 bad actor would still be 99% good. 99.9% of 10,000 people is 10 bad. That’s a fair amount, but when you have a commonality among the people where some join for the purpose of providing poor information, or unproductive data or ruin the experience for the rest, it can tear it apart. And that’s becoming harder to gauge, I’d imagine (rise of community managers and insertion of social data and other related ‘checks’ inserted).

So that I can get off this little soapbox, I wanted to bring attention to those that have so far only consumed information – please share and try to bring some new insights. It will hopefully bring in a new person that sees it – refreshing eyes can be useful on new information- we all have different experiences. And for the bad actors, hopefully there’s an end goal that does provide some value – it’s tough early on but the right communities invite opposing views to allow others to draw conclusions. Inferences aren’t merely opinion on the shallowest, easiest data to gather, but rather a collection and reflection on a set that may agree or collectively provide information to allow a deeper understanding.

Enjoy the notes I had from this week! I do suggest going through the a16z podcast 16Min on the News (for news from the week).

  • Apple Card, BEC Scams Fed (16 Minutes on the News #7, 8/25/19)
    • Became available this week – partnering with Goldman Sachs and Mastercard coated white and titanium
      • Apple moving into financial services, no typical sign-up fees or late / overdraft fees
    • Apple as reinventing existing categories repeatedly, so even changing basic stuff like making the transparency feature
      • Reminder of SMS and Innovator’s Dilemma (making money in core with new business on horizon because you’d cannibalize yourself to enter)
      • B2b2c as incentivized to grow – GS not a big consumer lending (Marcus last 2 years), but can drive growth
    • Offering 3% cash back when purchasing from Apple, 2% with Apple Pay, 1% from card – incentivizing payment mechanism
      • Interchange fee is expensive but if they become default payment mechanism, they can pivot
      • Money as emotionally driven vs functional and product – making sense in rational isn’t the move
      • Nobody wants to budget just as nobody wants to diet – instead, automate small financial decisions to help achieve better outcome
        • Self-driving money: not having to make the decisions to optimize your financial life (too high friction or don’t know about them)
    • GS with $350 to acquire customers – traditionally, credit cards have been onerous
      • Future: everyone should have access to payments via unsecuritized debt without great credit
        • People that are creditworthy with great credit scores but those that never pay bills and have bad credit
        • Overly negative in a different sense (ones that are almost wealthy that end up getting in trouble)
    • BEC scams – business email compromise
      • More than doubling each year – big deal on security
      • Sending email messages to send money – better technical systems are now just asking individuals (social eng as most effective form)
  • Ben Lorica, Chief Data Scientist at O’Reilly (Big Data Beard 8/13/19)
    268x0w

    • Future of Big Data with O’Reilly’s
    • Would take handle “5g” or something related for the future
    • Collecting, aggregating and normalizing big data now – business intelligence reports, simple averages or trends
      • What else can we do? Improve or automate processes/workflows or extract higher revenue from systems
      • Natural evolution and what are the bottlenecks for the AI / ML processes (are you early stages, models in production?)
    • Quintessential marketing for hype but developing a use case for the application
      • Tools for labeling data, data programming
      • Mentioned how to do ML with demo and user cases for interacting by Product Managers – SF O’Reilly conf
    • RPA with proper use case and proper implementation (close to the task)
      • Successful organizations have figured out how to bridge that gap – technologists with communication/collab on business side
    • On open source side, TensorFlow and PyTorch as top 2
      • Data science side – announcements about internal data science platforms to work together (share pipelines / features / models) openly
      • DataBricks, one company that he advises, also works on delivering enterprise data science platforms – IBM, cloud vendors
    • MLFlow as DataBricks’ for managing and tracking ML development life cycle
      • Monitoring alerts for retrain model, feature drift, deploy model against live data (simulating on live data but not production)
      • Model governance as tools that excite him – highly regulated industries like banking, financial services – what models and metadata
        • When was it last touched, trained, on what data, etc…
    • Managed services based on open source – managed Spark, for instance – minimal log in
      • If I have a better model but worse data, the better data should win, and that’s what drives competitive advantage
    • Big push toward hardware space – training at the edge or even model training in general – specialized hardware for accelerating DL / ML
      • More researchers working on data cleaning and data repair
      • Snorkl from Stanford researchers – easier for more people to use the product
      • Reinforcement learning – he’s most interested in UC Berkeley’s RISE Club’s REY (sp?) – distributed computation platform in C++ low latency
        • Building on top of REY as Odin – can cover 80% of Pandas, faster and other libraries
  • Frank, Chief Business Officer at Edge Sports/Analytics (Wharton XM)
  • Becky Miller, co-founder & CEO at Tinyhood (Wharton XM)
    858aeebd82f3bb14afd339eda1db

    • Wanting to connect with other supermoms and doing a community
    • Deciding to do parenting classes online and helping subscriptions
  • Josh Phifer, co-founder at Barn Owl (Wharton XM)
    barn20owl2020rgb20_stagexchange_intro20pic

    • From Wyoming / Nebraska ranching, went to Wharton
    • Starting with water sensors but wasn’t quite working or gaining traction, thought drones would work initially
    • Pivoting as they were running out of money to find a product – camera with satellite / cell connection from China sourcing
      • Bootstrapping about $175k from friends and family
    • Camera use case – all kinds of agriculture applications for checking – can send picture via app, timed or on certain amount of times
      • Solar and battery powered
    • Obsession over the problem, not marriage to a solution – feed the need
    • Initial app created with Bubble.io, introduced at Wharton – low code solution with logic programming
      • Hired on an employee – electrical systems who could help with building out full app and logistics
  • Mark Nathan, CEO of Zapari (Mastering Innovation / Wharton XM)
    • Discussion of moving from engineering, building stuff to the medicine / insurance field
    • Not necessarily working on analytics, but collecting and informing consumers and other stakeholders
    • Doesn’t foresee regulation as a hindrance, since what they’re doing isn’t predicated on that
    • Primarily started with SoCal, Medicare and getting adoption from pharmacies – assisting nurses on customer service end with their call center, ex
      • Not set up to deal with pharmacists or customers, can alleviate this and help with people fulfilling prescriptions
  • JD Long, VP of Risk Management for Renaissance Reinsurance (Data Framed #37, 8/27/18)
    renaissancere-grey

    • Starting in R stackoverflow asking questions / answers and building the community with Mark Driscoll
    • Graduated in undergrad, starting masters and asked where PhD’s were going (of Agri Econ): answers AMEX
      • Due to SAS and mainframes, UNIX, R-Cran hadn’t started
      • AMEX was explicitly recruiting in 1996 for these economists because of modeling, coding messy data, crop insurance, regression (econometrics)
    • History of cultural agricultural yields, weather and prices from before 1996 – which was agricultural crop insurance start
    • Simulate and stochastically getting a bunch of results that give you an idea of the distribution of the model
      • He does very little predicting of what may happen next year – looks at shape of distribution for the following year
      • Looking at improbable 1 in 1000 results that may be possible in that distribution
      • Book: “How to Measure Anything” – what’s the highest and lowest estimate
    • Risk vs uncertainty: Risk is understanding underlying distribution but not sure what you’ll get; Uncertainty is not knowing the distribution
      • Flipping a coin has risk – can model the probabilities if you know the coin, but uncertainty would be not knowing if the coin is loaded / biased
      • Auto insurance is type of product that is mostly risk, less uncertainty – predictable patterns, historical distributions and tail events
        • Terror events – historical categorization of events but no reason to see world events as drawing from the distribution of that
          • Unstable random geopolitical events, component of risk vs higher uncertainty
    • Reinsurance with risks that can be correlated based on underlying physical relationships, such as homeowners insurance in NYC should be correlated
      • Hurricane Sandy would be something that hits everything there
      • P&C companies with casualty claim could be connected among multiple companies
      • Legal change in framework could cause claims to increase 15% – have to understand the correlation when aggregating data
      • 2 distributions can be added or correlations using copula – artifact of some other process
        • Model data should be containing it already but this is only way to insert
    • In 3000 BCE, Babylonians had disaster contingency – loans didn’t have to be repaid if losses happened for certain events
      • Edmond Haley (Haley’s comet) created modern-style mortality table in 1693
      • Lloyd’s coffee house emerged for shipping news and buy shipping insurance (turned into Lloyds of London – marketplace now)
      • 1992 – Hurricane Andrew recharged after ripping Florida and hit Alabama and Louisiana – big catastrophe for reinsurance companies
        • Hurricane reinsurance was a gentlemen’s game – big contraction of market after Andrew, filled by crop of reinsurers in Bermuda
        • Became a quantitative analysis market after this – turning point of reinsurance, reasonable proximity for US and capital-free
    • Heuristics that make certain assumptions for the modeling of both finance, insurance
      • More effective models for sharing and coming together with actuaries, risks and methodology
      • Data science examples, actuaries methodology that would be working together (GLM combined with understanding on actuary side)
    • If asker of question made it easier on the question answerer (on example for Stack Overflow)
      • Incomplete code or maybe not syntactically right so the answerer cannot answer it properly
      • Empathy for the receivers of your opinion or problem or otherwise
      • If doing analysis to equip underwriter for a deal – what information does the underwriter need to be well-equipped for negotiating their deal
        • Influences and drives thinking of how to serve that analysis / information
    • “Hacking empathy”: from Agile development method would be User Stories
      • Hugo is a data scientist who is trying to understand X. He needs this tool to do Y so he can understand X.
        • Forces the person to do this to think about that user or other person
        • Think about who is consuming it to give nudges or reminding someone – doesn’t think that way
      • At DataCamp, how active or users or learning profiles that they are aimed at
        • Designing for average, you design for no one from podcast “99% Invisible”
        • Give target audience a name to relate to them; multidimensional space for ‘tyranny of mean’
          • If you have 3 dimensions of human body (leg length, height, hand size, arm length, etc..)
            any 3 with a small margin of error will be merely 6% of pop
    • Where is market opportunity? Met with a headhunter in space.
      • Deep learning and AI for media and ink spill – interesting and have potential for revolutionary changes.
      • Former guest Jenny Bryan who talked of attempting to get people out of Excel – massive movement there, he believes
    • If we don’t ask “Does our analysis change the outcome?”, we can do infinite analysis since it’s all that we don’t know
      • Never drive organization. Leaders should have candid conversations about if the research is going to change the answer of the decisions.
        • If it’s a no – why put resources toward it?
        • What’s the next best simplest alternative? Not comparing to doing nothing.
          • Deploying a complicated model should be compared to old forecasting method or cheaper, faster one. Is the added complexity worth it?
      • Hugo tells them to deploy basic baseline model, do 20 min of EDA and try to make own prediction. Then test the models against that.
        • In public policy, effectiveness isn’t against doing nothing, it’s the next best. Benchmarks are too often done at base.
        • “Plot your damn data”
  • Matt Lieber, cofounder & President of Gimlet Media (20min VC FF030 1/15/16)
    gimlet-and-spotify

    • Produced radio shows Fair Game and On Point, worked as a management consultant at BCG
    • Radio producer was his lifelong dream after being a radio head growing up
    • Met Alex Bloomberg after his MBA and consulting, who is the cofounder – left to go learn business side
      • Distribution to big audience, too many gatekeepers, market-by-market he had to go to program directors to pick up the show
      • Exciting thing, creative, ambitious work was happening there
    • Constraint breeding creativity – raising a series A
      • Had launched 3 or 4 shows in first year, scaling to some audiences and had worked
      • Revenue from start, ads in the beginning – VCs didn’t want to hear about those
      • Believed they could self-fund through profits, growth with revenues – don’t need to dilute, maintain control
        • Would need to build up the company after building some shows
    • Keeping small culture – fairly strong but not explicitly communicating it yet
      • Behavior of leadership and design of signs – started Gimlet Guides around 25 employees for onboarding
        • Gimlet Guides are the mentors for establishing new employee onboarding – lunch once a month, questions
    • Wanted to get a partner for VC who was aligned with the vision, experience investing in media for different return timelines and dynamics
      • Sea of change of how a whole generation will consume radio and shows
      • Simplest, direct way for market – size of radio ($18bn+ in US in advertising alone) – digital for mobile media market
        • Consumption shifting to mobile – advertisement doesn’t work (Gimlet is ~80% mobile)
    • Deciding how to make new shows? Question from someone
      • Mentioned “Surprisingly Awesome” – people want to be entertained and learn something, recent ep was interest rates and economy
      • Teamed up with Adam McKay and Adam Davidson for it
      • Learning, listen and come away with some understanding, a host to connect with and is there a narrative
    • Mystery Show, Reply All, Startup and Surprisingly Awesome are all the biggest shows
    • Favorite book: Great Plains by Ian Frasier, didn’t have an emulator
    • Challenging aspect of creating it: scaling editorial where you create a system to grow and teach editorial material
    • Most excited about the next shows – this case, a podcast about podcasts called Sampler
    • Best advice: Be nice.
  • Abhinav Asthana, Founder of Postman (Wharton XM)
    postman

    • Talking about why he loved building more than what he had done previously
    • Community for that

Your Experience is Your Own, Only (Notes from Aug 19 to Aug 25, 2019) September 10, 2019

Posted by Anthony in Automation, Blockchain, Digital, experience, finance, Founders, global, gym, Hiring, Leadership, marketing, NLP, social, Strategy, training, Uncategorized, WomenInWork.
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I’ve been considering more and more about how my experiences are only mine. Especially when I feel like I don’t share them often. Working so much but not always discussing it with people outside of work (re: almost never). I was reminded of this while I met with a family member who I see roughly once a month or so. When she asks how work is or I mention I’m busy on days when she wants to meet, it often came with a “busy with a meeting at X but can do Y”. Never more. And almost always, I ask how her work is, and she divulges. So when we sat down for dinner and she point blank asked “I have 2 things: 1. Can you help me with something on my new phone? and 2. What is it actually that you do?” I chuckled because generally I don’t care to share that information – I really enjoy valuing start-ups and learning about the space / tech / finance / education changes, but other than high level stuff, rarely does anyone want to hear me talk extensively a la a podcast episode deep-dive or something. They don’t see the relevance, other than it being exciting for me. Same with when I was advising, same since launching the fund and all while working on project deployment in data science for others.

I strongly suggest reading through Colson Whitehead’s essay here about his version of New York City. How it’s interpreted. essay here

Another thing I read through today was Farnam Street’s blog post on asking seemingly simple questions that may be defined or determined by our experiences with those concepts. An example he uses: “What is a horse?” Try to think how we may answer this.
Power questions

 

  • AI in the Past, Present and Future (BDB 7/16/19)
    teradata-logo-social

    • Rod Bodkin, Tech Director at CTOs office in Google
      • Was with BigDataAnalytics, bought by Teradata and grew it from there
    • Grew Google after seeing the field advancing quickly, state of the art as evolving
    • First people to put Hadoop into production – Yahoo was too scared, single algorithm took weeks at the time
    • OpenAI put out state of art compute paper – 4 year paper, 300k X computation (double every 3.5 months)
    • For Google, evolution of cloud in the enterprise is a big deal – consumer side of Google as leading the way
      • Can just put data into BigQuery because of capacity and accessibility of data – increased production 4x on data science team
    • Big investments into Anthos – open source tech to enable cloud-native services in different clouds, GKE (Kubernetes)
      • Edge TPUs as 100x faster to compute a model vs traditional mobile CPU – TPU as accelerator chip for DL
      • CPU is completely general so less efficient
      • GPU has a boost over CPU but behind TPU accelerators (starting GPU chips, Tensor unit)
    • Kaggle Days and Google IO for cloud Pixel modeling and AutoML performing very well
    • Herrari’s book – 21 Problems for 21st Century
  • Tricia Han, CEO of Daily Burn (Wharton XM)
    51w1ctcdszl._sy355_

    • Community of like-minded fitness fanatics
    • Live 365 – 30min shows on working out, regulars
    • In survey, millenials said fitness #1 and health/wellness at #5
    • Fitness had about happiness equal to making $25k more

 

 

 

  • State and Future of Robotics, ML and Digital Celebs (Venture Stories, 8/8/19)
    ht6qfyjc_400x400

    • Michael Dempsey (@mhdempsey) – partner at Compound
    • Read, Listen, Write, Talk – Cunningham’s Law – share something with a strong opinion is likely to get responses
      • More value when shared publicly
    • Robotics, ML as cascading forward – robots broadly, initially – types, how to make them intelligent (2013)
      • Drones, hardware platform (DJI as leader), space and now as unsupervised or self-supervised learning
      • Deep dive on innovation for what he’s spent the last year or two – investments, as well
    • Women’s health as growing market for fertility and experience layer in healthcare system
      • Higher-end service around egg freezing (but was shattered by Tia founders), IVF or embryo screening
      • 2 investments for him already in the space, maybe more after
    • Strategic robot acquisition for Amazon, why now? Major companies in the space – he’s punted in that space, more investors.
      • Didn’t see meaningful differentiation in the space – didn’t see a company that had that from an investing side
      • Food was where he saw robotics as consistent – grew up in the industry
      • Really easy to get pilots but not for revenue – wants full-stack robotics company
      • Robots taking over entire industry – automated X / Y / Z (rebar, construction robotics)
      • Front of house and back of house retail (analytics, stocking)
    • Weird robot applications (in-home, manicures, old person help)
    • If company is built on algorithm being best, company probably won’t survive
      • Must talk to people doing operating – not just reading
      • Self-driving cars – spent time with Daniel Gruber, discussing local maximum and rules to write
        • If you can drive in NY, you can drive in SF, LA, etc…. 2007 DARPA challenge Waymo / Tesla / Cruise as result – path-planning
        • Intelligence approach – what are incentives / agents to accomplish in a car for end-to-end approach to scale
      • 1 model to move them all – enough compute that model can solve it (DL is direct function of this, for Google)
    • Investment in data labeling space – more people moving into production requires more people getting good data and filtering data
      • Larger data builds where it may cause $50-200mln per year to label but 50% is useless
      • Environmental impact and thinking about it – consolidating data but into better (CartaAI and SkillAI)
    • DeepGram end-to-end audio inscription – 80-85% can be good, but if you mess up some key words in certain industries, it’s more expensive
      • Voice side, horizontal players are pretty good – if x% of users will have same questions, simple workflow or algorithms
    • GANs and new generation of faces – Disney and animation nerd for a while – power of IP on agencies, CAA for example and Marvel
      • Stories through animated content, Robot Chicken, others – Robert Dillon – bringing in GANs
      • Watching live action is watching someone else’s story whereas an animated one brings you into the story
    • Trusting the people that have been given permissions – Reddit or being anonymous
  • John Roese, Global CTO of Dell EMC (Mastering Innovation, Wharton XM)
    dellemc2

    • Talking about the 20 year vision to be autonomous but incremental parts until then
      • Driving assist, improved AI in driving, maybe geofenced before autonomous
      • Autonomous vehicles as source of innovation – sensors / LiDar very useful for other industries but too expensive
        • Had talked to studios about virtual studios or conferences – expense should come down with auto
      • Vast problems with uncontrolled or unconstrained problems – already have fully autonomous warehouses or geofenced areas
    • Interested in bio feedback as input to AI or MI systems
      • Used example of video conferences with sensing stress levels – clearer audio, accent correction, more people = more stress
      • Cars already using bio feedback
      • People already wearing sensors via devices – can use that as more input
    • Attacking low hanging fruit because of data ethics or biased data inputs – easier to solve problems that are valuable in neatly constrained
  • Amri Kibbler, Katya Libin, Hey Mama co-founders (Wharton XM)
    • Collaborate and share and support their work for mothers as executives
  • 13 Minutes to the Moon
    • Ep. 06 – “Saving 1968”
      • Apollo II’s first landing – without Apollo VIII, Pathfinder and 250k mi to the moon, maybe gutsiest flight until then
      • Flying VIII before end of year – “We were not ready”
      • 2 deaths of MLK and Kennedy – April had hundreds of cities taking part in riots, thousands arrested
        • 1968 Apollo program was in shock and Saturn V rocket was malfunctioning – troubled test flights
        • Almost busted in all 3 phases the last time it had flown, and the lunar module had slowed down, as well
      • Taking lunar module away from Apollo VIII – former test pilot Jim Lovell said it as Lewis & Clark expedition
        • So many firsts, risks that were enormous on a 100x scale – reason Jim was there in the first place
        • Crews normally had 6 months but VIII only had 4 – mathematicians were responsible for all of the angles and engine durations
      • 1 chance in 3 for mission successful, 1 in 3 for non-crash but unsuccessful and 1 in 3 for not coming back – wife accepted this
      • Media as delivering “death pills” for dying painlessly – respondents would say oxygen would run out and it’d be fairly painless
      • Trans-Lunar Injection – don’t shoot at the duck, shoot out front – wanted to go to 60 mi ahead of where the moon would be
        • Spacecraft needed to get to the right moment, speed, angle and altitude for the moon
        • Human computer – Katherine Johnson – was responsible for the trajectory for launch time (Hidden Figures)
        • Took 3 days from launch to get to target – Lunar Orbit Insertion
      • Astronauts were late on radio contact from dark side of moon
        • Came back to light and could hide behind his thumb – 5 billion people and everything he ever knew
        • Finishing Apollo VIII with scripture and then Good Night, Good Luck and Merry Christmas
  • Bill Clerico, co-founder and CEO of WePay (DealMakers 8/13/19)
    wepay-1

    • Leading provider of integrated payments for software platforms, raised $75mil from SV Angel, Highland Capital, Ignition Partners, August Cap
      • Founders of YouTube and PayPal also in
    • Grew up in NJ, spent time in NY and father worked in Air Force and construction – taught himself computers in 80s
      • Received a scholarship to go to BC, met his co-founder for WePay waiting for the flight for the interview 6 years prior
      • Went to do IB at Jeffray’s – advising tech and software companies with clients, passionate and building for a year to quit
    • Installed a suit rack in his car because he wasn’t going home – long hours, brutal fundraising
    • Group payments that they saw repeatedly at the age of 22 – big market for payments, testing it out
      • Wouldn’t have less responsibilities than at that time – Rich deferred law school and Bill had worked on it full time
      • Tried to pitch Boston investors and failed – less receptive to early stage investing, applied to YC instead
        • Came out to the valley for an interview
    • Spent 1.5 year to invest and took money and sold furniture and drove to the west, taking turns
      • Product was conceptual, pitch deck was opinion and it was hard to prove a market need to investors – conceptual idea
      • In YC, built product by talking to fraternity treasurers at SJSU, ski club coordinators – got them using the product
        • Went to talk to investors by showing them the traction
      • Why would a treasurer to accept payments with different product? Host bbq and invite them over. Go to dorm room and watch product usage.
        • Responsive to requests – take feedback and be better than existing solutions. Gain knowledge in start by doing things not scaling.
    • Group payments were a big problem and needed a solution – weren’t willing to pay, or pay transaction fees
      • Venmo had raised money and had a bunch of momentum by giving away services for free
      • Competitors were taking advantage, 2 years after YC – pivoted but weren’t growing as fast
        • Built an events tool, donation, invoicing tool and an API for customer use – other companies were just doing those
      • Realized they could build an API making payments experience easy and simple and let competitors do whatever
        • Saw huge traction/benefit where they could be brought in via the API (since they had raised $30mln)
        • Needed the business to be grown but expectations were higher
    • 600 lb block of ice for marketing $500 in front of PayPal Dev Conf at Moscone Center – still highest market day
      • Since PayPal had a knack for freezing people’s accounts randomly
    • Pivoted to shut off 70% revenue stream from consumer product, gaining growth on API from other customers
      • GoFundMe used them as a payments processor from when they were 2 person company
    • Prior to acquisition by JPMC – 200 employees at that time, now fintech / bank
      • Asset purchase agreement day – tired – was negotiating final points of deal in person, had some drinks to celebrate
      • Bought a cabin in Mendocino County – deal was valued at $400mln
    • Part-time partner at YC now – helping companies in general – relevant to the next entrepreneurs and the scale
    • Angel investing on the side – much longer and harder and scarier than he ever would’ve imagined
      • Reinforces this to his younger self – startup doesn’t fail unless you give up
  • Evolving Narratives in the Crypto Space with Andreas M. Antonopoulos (FYI 3/12/19)
    • With Arjun Balaji, as well — and similar for me as host, his intro to Crypto space video YT
    • Conflict of Crypto Visions article by Arjun and host
      • Identified closely with unconstrained vision and doing talks on not playing zero-sum mentality
      • Ethereum as different than Bitcoin – evolving directed by design choices
    • Engineering consists of design tradeoffs – choices of optimizing and de-optimizing parts of systems
    • If you want to make something that is Bitcoin-ish, you run into problems for all the strengths that are already inherent to Bitcoin network
      • Differentiate enough to be a new thing from Bitcoin – can’t mingle or occupy that niche
      • Is privacy a big enough differentiator to separate from Bitcoin network?
        • Strong privacy in base layer – can end up with inflation bugs that can damage sound money policy of Bitcoin for the privacy
      • Sound money vs private money – not clear yet.
    • Hard money displaces other forms of money in long term but only if they’re maximalists and logical
    • Friction levels determining switching back and forth on a wallet between utility or store of value tokens / coins in the future
      • Automated backend where they are optimized
    • Interest in Ethereum – tradeoff worth making for smart contracts and applications that aren’t just money outside of Bitcoin
      • How the technology of VM blockchains work
      • Scaling is harder in Ethereum – proof of stake has different security model than proof of work
      • Sharding, beacon chain, polka dot – not sure if it will work or what the security constraints are – could have applicability to BTC
    • Bitcoin critics – make the case for it but then explain value proposition or store of value
      • He has an opinion, others have opinions – none will determine how the market develops
      • Arguing is a waste of time. If you understand the tool that’s best for a job, you’re a better user of tools.
        • Which is the correct tool and how to use it properly – perception is limiting in general
  • Sam Yagan, CEO of ShopRunner (Wharton XM)
    sr_stack_full

    • Founding dating OkCupid and then going to Match and scaling to IPO
      • Going from running a team of 30 to 1000 in a month
    • Ecommerce ShopRunner as retailers combatting Amazon and Walmart – providing scale and guarantees with 2-day shipping for many retailers
      • Joining after Michael Rubin had founded it on premise of “Amazon for all others”
    • Making sure they have AMEX partnership to make it easy for customers
  • Travis Katz, VP of Product at Skyscanner (Wharton XM)
    image1-4

    • Had been cofounder of Trip.com and at Myspace prior
    • Social media giants Facebook and Myspace – selling to NewsCorp and getting revenue compared to funded Facebook acquiring users

Fun Founder Stories (Notes from July 29 – Aug 4, 2019) August 21, 2019

Posted by Anthony in Automation, Digital, education, experience, finance, Founders, global, Hiring, Leadership, marketing, medicine, questions, social, Strategy, TV, Uncategorized, WomenInWork.
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Starting with a discussion of Neuralink (Musk’s… brain-child of a company for neural lace) and how it reminds the a16z crew of invasive compared to non-invasive surgeries / medical tech. How did TikTok vary itself in the social space and explode in popularity? Harry Stebbings of 20min VC had been going on and on about HiringScreen and finally had the founder on which was fun to hear. Richard’s origination story for the company and his path that he took was fascinating.

Then I happened to listen to a few different shoe companies with founders on serendipitous and creative stories. One traveling to a new and different country and absorbing the culture to his story. The others, seeing a problem that seemed to arise and noticing there should / could be a solution. Then catching breaks for each of the 2 companies – including the bootstrapping and doing it on their own as something that was fun enough helping people solve those problems / be happy with their footwear. I strongly suggest looking at Sabah shoes for men’s drivers-ish and Birdies for women who go to parties where they may need slippers or comfortable everyday ones.

E-sports and digital discussion for a16z was fun in how society is adapting to digital experiences or how they meld entertainment. For those that don’t think esports may be viable, it’s easy to argue in the cases where they watch reality tv or even game shows (which have been around since tv). It’s just changed how we consume and perceive it as interactive live games vs recordings. Also, malls that are less successful or in areas have been able to take advantage of the space available.

Vivino’s CEO joined and talked about how he is trying to socialize and give people options in the wine space – which, let’s be honest, is always a good thing. Goldie Chan discussed filling the gap in an employment by consulting, by accident, nearly. She turned it into a full pivot consulting and has taken advantage of her great skills at marketing. Hope there’s something for everyone!

  • Neuralink & Brain Interface (a16z 7/21/19, 16min on the News)
    1200px-neuralink_logo.svg_

    • With Vijay, Connie Chan, JPM
    • Announcement of neural lace – culture sci-fi by Ian Banks – processor & sewing machine
    • Non-invasive vs invasive (femoral artery all the way up to the brain)
      • LASIK as invasive / dangerous (still even, but now much better, accepted)
    • Announcing in rats and in monkeys now (surprising his president)
    • TikTok as 3rd most dl app behind WhatsApp and FB Messenger, 1.2bln MAUs – having huge influence at VidCon
      • Sponsored by YouTube but TikTok had a large presence, the ban in India
      • Short, 15sec videos – 1 hit piece can trigger enough people
    • How would they make money? – ecommerce, restaurants, retail – short videos for ads/commercials
    • FaceApp – probably nothing to worry about – unless high profiled public official, NatSec Space, leverage
      • Someone getting negative information or leakage – accusations of the country in general is silly
      • Countries consider privacy differently – in the US, convenience / UX will trump privacy for 15min of joy
        • Europeans, Germans, Italians for instance are more private
    • iHeartRadio announcing direct listing – before, emerging from bankruptcy or spinning off
      • Repurposed after Spotify / Pandora
  • Mobile malware and Bipartisan drug pricing (a16z 7/28/19, 16min on the News)
    • With Martin Casado, Jorge Conde, Jay Rughani
    • Monacle as mobile malware – March 2016 Android-based application
      • In security, netsec and endpoints – protecting desktops, for instance
      • Attacks phone with 2FA, even, and less secure
      • Can take calendar event, account info and app messages, reset PINs
    • Drug pricing – Medicare Modernization Act – why can’t Medicare use its purchasing power to negotiate medicine prices?
      • Part D – Medicare covering prescription prices, prevents HFS from negotiating any part of the value chain
      • Price of insulin where they get price hikes – new therapy gets $2mln for cures (R&D) differences, conflation
      • Price of successful drugs have to make money for drug and all of the failures
        • Counterargument – US subsidizes R&D for the world
        • Complex industry structure: manufacturers, distributors paid to move drugs through channel
          • Pharmacy benefit manager – who is eligible, who’s not – what are drugs for conditions and prescriptions
            • Helps insurers who gets the drugs – takes an economics layer
          • Insurers reduction drug spends, for $1 spent, manufacturer gets a small %
      • Dropping from $8k to $3100 out of pocket
        • Cap by tying to inflation (for growth) or annual price increases
        • May start higher prices because you can’t increase it much
    • Chain is not transparent, but also complex – tech can have an impact but needs help from policy to drive out some inefficiencies
      • Free market works if there’s transparency – what is a medicine and can you make it fair enough for everyone
      • Current system is not set up for the new medicines (extending life from 10 years to a cure)
  • Richard Hanson, CEO & cofounder of HiringScreen (20min VC FF028)
    psrzsqo86j9gj71wrqli

    • Founded in Hong Kong in 2015
    • Studied law in Cambridge, did 11 years recruitment consultancy in London before moving to Hong Kong
      • Then created his own recruitment firm – had his own looking at 196 cv’s for an EA for someone
      • Score, sort and select candidates
    • Tech advances in recruiting industry – job boards and sourcing is at all-time highs
      • Barrier to application is all-time low but have too many to look for (especially manually)
      • Psychometric and phone facility stuff to find relevant candidates – get on with themselves
        • Go through rest of funnel to invest in the process in more efficient manner
    • Had always wanted to live in Asia – pretty exciting, bullish for Asia in general
      • Hong Kong, Singapore, Japan as hubs
    • If you have an idea, try to find someone or go ahead and do a view of what it may be executed on
      • He had the idea, went to his cofounder Luke (better at project management side)
      • Prototyping mockups and getting through the first steps efficiently – may hit a dead-end a few weeks in
        • Validating idea as soon as possible – customer or problems for people (heads of recruitment firms for his problem)
    • Making an effort to code or understand a bit of the UX (in his case, CSS and HTML to understand a bit)
      • Compared to languages in a foreign country
      • When his CTO introduces people, he wants to be confident about what the developer has been doing and understanding their past
      • His responsibility to show an effort/commitment in the job role
    • Looking to raise a round – HiringScreen did it in 8 weeks
      • Competitive slides, why you want to raise, how to convey mission statement, skill and productivity gaps
      • Understanding his potential investors, as well
    • Accelerators – choosing the right ones? He’s with the Blueprint Accelerator by Swire properties
      • B2B focus, no equity in startups – working space and Swire network of companies (conglomerate of different co’s in verticals)
      • Sponsored him and tried to help advance the company by talking to other HR talks
      • Mentions Brinc as hardware accelerator near the top
    • Idea of equity early on would depend on your assessment of what the startup needs?
      • Super low cost – accelerator with working space?
      • Product but proven use case – Blueprint to trial product and test it
      • Balance the need with the equity they’re taking
    • The Alliance book by Reid Hoffman for looking at employee and employer workplace, tour of duty principle
    • Brad Feld and Jason Calacanis’s blogs, Reid Hoffman as the most admirable founder – better people to take LinkedIn on
  • Jennifer Golbeck, College of Information Studies and Affiliate Professor at UMD
    • Talking about social media research, truth and justice
  • Carl Ericson, CEO & cofounder of Atomic Object (Wharton XM, Mind Your Business)
    atomic-object-wordmark-500x265

    • Grand Rapids, Ann Arbor software product development company and why he chose there
    • Sails at Grand Rapids Yacht Club
  • Bianca Gates, Marisa Sharkey, Birdies co-founders (Wharton XM)
    m_5a61f34b331627f3f88fe26b

    • Discussing how they started them and Feb 14 – when she landed an article with a SF Chronicle fashion correspondent at a dinner party
    • Driving up to the other in order to get all 2000 orders packaged and sent out

 

 

 

  • Mickey Ashmore, founder of Sabah Shoes (Wharton XM)
    sabahtwotone

    • Doing a 6 month project after Seattle in Turkey – turned into 2 years as the only non-Turk
      • Grew an affinity for the people, culture, food and trends – girlfriend’s grandma at the time gifted him a pair of handmade shoes
    • Returned to NY and beat the crap out of the shoes – wanted another
      • Reached out to the maker (current partner) and bought another pair
      • Ended up getting 5-6 in different colors, customized without the flip – people said they were awesome
      • Ordered 300 – could get 150+ and did a party to showcase them with cocktails, enjoyed hosting
        • Got 30-40 orders on the first night, decided to do it for the rest of the summer “Sabah Saturday/Sundays”
    • Realized it could be a business after in the summer he was making more from shoe sales than his NY P/E job
    • Expanding from 3-4 employees to 40 and expanding from a home to a warehouse – border of Syria/Turkey
      • Has a few key employees that are Syrian refugees – part of the brand and they showcase it on the site
        • Not branding directly, but definitely part of the story
  • Goldie Chan (@goldiechan), digital marketing expert of LinkedIn and actor (Wharton XM)
    • Discussing quitting her job and making a fake company while unemployed
      • Turned into a marketing consulting gig – had a few clients, had to create a company
    • Now doing talks and discussions
  • Kurt Seidensticker, CEO of Vital Protein (Wharton XM)
    ca400555-4bb7-4c66-a217-b5ac910cba73._cr5101107332_pt0_sx600__

    • Collagen and explaining to people how it was – getting some in to Whole Foods through them asking
    • Didn’t hit him until he was in Italy and 2 random women at a café pulled their Vital out
    • Did about 10 companies, 2 succeeded enough to pay for kids college and allow him the freedom
      • Was doing Vital during another company until it surpassed the other
  • Fortnite, esports, Gaming (a16z, 16min on the News)
    • 2 million concurrent livestreaming – not as big as GoT, for instance
    • With Andrew Chen, Darcy Cooligan (investing team on consumer)
    • Bigger prize pool for Dota 2, $3mil for Bugha’s win was larger than Tiger’s Masters victory
    • 10 years for Riot and League – still grossing billion, WoW / Runescape
    • Billions of video consumption between Twitch, YT (and now Microsoft Mixer)
    • iPad can play Fortnite pretty well, for instance – massive multiplayer opportunities
      • Instagram and this generation for coming together as people – Minecraft/Fortnite
      • Gaming and cultural zeitgeist to hang out with friends
    • Sonal did a fight with editorial desk and had seen it for a profiling in 2013 – argued it was similar to sports
      • Big business and much of the same thing – management company, played 2+ years for 6-8 hours, sponsors, fans
      • Performance entertainment and personality-based
        • Comparative for game shows – other people answering trivia, reality tv
    • Strong incentives to keep games going – user-generated content
      • Established player leading way to user-generated thereafter
      • For Fortnite, building levels (similar to mods and mod community in Minecraft and Roblox)
    • Games stadia for esports and digital dualism (in real life compared to virtual – game is the bridge)
      • Malls building areas for this part
  • Chris Tsakalakis, CEO of Vivino (Bay Area Ventures, Wharton XM)
    aws_vivino_logo_600x400.cb594b3d79815eece9e8c685a7b8d043b7910b95

    • Having users and getting customers – at least 1 employee in each region where they sell
    • Mostly in US, Europe – hq in Dublin
    • Bunch of users in Asia / South America (Brazil, specifically), but don’t sell there yet
    • Not taking VC until more recently

Idea Conversion to Algorithms (Notes from July 22 – 28, 2019) August 14, 2019

Posted by Anthony in Automation, Digital, education, experience, finance, Founders, global, Hiring, Leadership, marketing, medicine, questions, Strategy, Uncategorized, WomenInWork.
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There is quite a bit in this week’s notes to unpack. Most of the stories and experiences provided by the guests, though, premised around testing a hypothesis or quickly trying to solve a problem in a manner that, once validated, could become much more efficient. When trying to make the solution more efficient, whether data or AI-driven, then further questions have to be asked to ensure a proper, scaleable and ethical solution. Lauren deLisaColeman discussed the ML application ethics and what guides them. Karim Galil observed that patient history was stodgy and doctors weren’t in to new things that could save them time because of the catchup time. So he had to produce a solution that could be effective immediately and worth giving back doctors time – he chose oncology to do it in.

Alyssa Dineen discussed profiles as well, but of the dating variety. There were more ways to screw up than attracting attention. At first, she could do it manually before realizing she could improve the work she did and make it better for both business and clients. Khartoon at Spotify talked about how they started at Spotify with freemium model and the streaming aspect before connecting that with all of the data to their corporate and enterprise partners. In turn, the two-way data sharing enabled them to pivot nicely to provide more value and eventually into a paid model that helped the business. Lastly, Max Bruner talked about his hell of a journey where he eventually landed at Metromile, but not before building Mavrx in the best form of dirty solutions – cameras from planes. Then realizing what could be attached and automated to be a full provider to farmers in much of farmland US and improving it. Quite the product path.

Curious about this concept for much of college / graduates.
Idea possibly worth pursuing – saw post on similar idea. Fake VC – take seed or series A opportunities, combine with data plan (via other post). Have various students make their opinions on what to seek, whether funding was good. How to think of next steps? Make action plan, but templated and maybe try to get an argument. Podcast/videos presenting either side. Try to talk to startup that received. Good sourcing examples, data (limited) problems, industry seeking.

Hope you enjoy the week’s notes and check everyone out!

  • Lauren deLisa Coleman (@ultra_Lauren), Digi-cultural Trend Analyst (Wharton XM)
    • Forbes contributor, discussing AI and ethics of ML applications
    • Who makes the rules – is the data guided?
  • Karim Galil, Founder of Mendel.ai (Wharton XM)
    mendel-logo

    • Working in Egypt initially, wasn’t in Cairo but started in Sinai – beach and did surf/kitesurfing lessons deal
      • Talent was not as abundant, but did a project with Pfizer, Dubai government and others
      • Egypt had free healthcare but hospitals couldn’t pay for procedures that may have been experimental – trials would allow it
        • Wouldn’t hear about trials until it was too late in his oncology rotation
    • Observed that you could have a dating record online and perfect match, but not catch up on papers in context in industry
      • Had to start somewhere – landed on oncology – wasn’t a junior vs senior thing – few doctors had the time
    • Losing patients to cancer and messy medical records – trying to improve the healthcare industry
    • Can get a bunch of oncologists to drop everything and work as data scientists
      • Cheaper in Egypt and feasible – fair salaries to do this
      • In the US, very unlikely to happen as oncologists are far above data scientist salary
    • Medical matching service – AI-powered to do trials for language content
    • Paying ~30 employees, where 15 of them are oncologists
  • Alyssa Dineen, Style my Profile founder (Wharton XM)
    style-my-profile

    • Personal stylists online and in NYC
    • Wanting to expand – mentioned Forbes article and expanded 3x
      • Mostly from out of the NYC area
      • Would love to open LA, SF, Chicago, most urban areas
  • Daniel Korschun, assoc prof of Marketing at LeBow Drexel (Wharton XM)
    • Marketing and branding for Kaepernick’s Betsy Ross argument
      • Nike blew opportunity to turn the flag into a very big positive – “Unity” or 13 civil rights activists
    • Owning the branding, making sure to keep it different
    • Making statements or seeing both sides can attribute your opinion without actually doing so
      • Being “informed” by museum after making case for both sides
  • Chandra Devam, CEO of Aris MD (Wharton XM)
    arismdlogo-tealrevised

    • Discussion of iTech NASA competition with Star Trek-surgery
    • A/R and V/R applications – board with the tech
  • Rachel Glaser, CFO of Etsy (Mastering Innovation)
    sell-jewelry-on-etsy

    • Search algorithms to increase sales
    • Etsy as vintage space – defined as 20 years, or handmade materials or put together
    • Have to stay ahead of counterfeit and trends

 

 

  • Sitar Teli (@sitar), MP at Connect Ventures (20min VC 12/30/15)
    ll9ofnkowyknor16pe7t

    • Doughty Hanson Tech Ventures, series A round in SoundCloud
    • Dual degree in MechE and Econ from Duke
    • Taught English in South Korea for a year, 3 years in IB in US – Broadview (M&A, tech focused)
      • Enjoyed working with the companies but not the banking side – best part was to hear how companies started and early days
      • Hadn’t considered London in 2005 when headhunter had reached out
    • Gaming, fintech, music & content, adtech where Europe is producing big, growing companies now (2015)
      • More cross-pollination of entrepreneurs going back and forth or partnering with others
      • IB moving into VC – different perspectives for her 2 other partners
    • Starting a new fund – “one of worst startups you can think of” – competitive against established funds
      • Build brand, reputation, product and designing it (not just money but experience) – how to work with the founders
      • First year – founders aren’t necessarily eager – want a seriousness that came with business cards
      • Allocating $100 – she’d do $90 to the portfolio and investments, $10 to rejections and focus
        • For No’s, make it quick and even in the meeting or cut short
    • Looking for companies
      • Founders that really understand the market they’re building for – how passionate, how much time to understand, experience
        • CityMapper founder – public transport and how they move through the city and how it can help
        • Stockholm-based Oxy – music creation app (prior at SoundCloud) – digital music tech, digital to greater number of people
      • Founders on a mission (other than $)
      • UX-focused and at the center of what they do
      • As an aside, whole lot of $ (maybe at seed) but it’s not the only bucket – ecommerce, adtech, depending on what founders are
        • Thesis: investors can dictate the entrepreneurs and align them
    • Crowdfunding alongside VC – many biz don’t need venture capital but do need capital
    • Amazing Adventures of Kavalier as book
  • Khartoon Weiss, Global Head of Verticals at Spotify (Wharton XM)
    open-graph-default

    • Starting with the streaming service as free and eventually getting into freemium / subscribers
      • Providing value to users and charging for it
    • Analyzing usage data from subscribers and free users to personalize the experience for listeners and serving brand partners
    • Core value of giving creative artists the opportunity to live off their art
    • Advertisers will see data in events that drive music playing
      • For example, an eclipse occurring will produce more song plays with eclipse themes – can drive user advertising for it, connect brands
  • Max Bruner (@maximusbruner), VP CorpDev at Metromile (Wharton XM)
    metromile

    • Talked about Mavrx, geospatial and agtech company
      • Flying drones and then planes over farmland to assess and improve efficiency
      • Didn’t have the initial equipment when they went to South Africa (needed data during US’s winter)
        • Had pilots take their cameras, IR and others
    • Most of clients were in the midwest – eventually sold to various parts of the vertical
    • Attended UW-Madison in econ and Arabic – did a year abroad between Egypt and Qatar (at the time, nice and hadn’t been through revolutions yet)
      • Felt like something was missing so returned to DC where he worked in the DoE under Reinvestment and Recovery Act

Universal Laws: Parkinson’s Law (Notes from July 15 – 21, 2019) August 6, 2019

Posted by Anthony in Automation, Digital, experience, finance, Founders, global, Hiring, Leadership, marketing, medicine, questions, Real estate, Uncategorized, WomenInWork.
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I included in my thrice-weekly newsletter the blog post by Morgan Housel espousing some of the most common universal laws of our world today. Once you know of them, it’s tough to not consider them in your everyday life. I’ll be honest and say that I hadn’t heard / didn’t know the name or origination of a few, including Parkinson’s. However, I wanted to comment on it because of its commonplace position on my timeline (and in the way I generally price much of my consulting work).

Parkinson’s Law: Work expands to fill the time available for its completion.

ML and apps – attention. Phones and apps have stolen hours of attention over the last 3-4 years (Wharton XM blog) — 3 hours to 4+ hours for the average, now

How do they squeeze in more DAILY? Work efficiency, likely. Most probably don’t have 8 hours of real work – ask anyone. What do we think the % is? I understand there are roles that probably see a full day a few times a week or in certain weeks (looking at you, auditors/accountants/finance/strategy/consultants) where projects line up or during busy times. Even retail / seasonal / cyclical has busy seasons – boosts that require full focus. But generally, not.

Work time vs value – if you can finish a project in 24 hours, charge more because the allowable time outside of that is higher or do you take the full time or project out for time in case of a problem / feedback / there? See: consultants working with a client, maybe a new client? Value = price but want to keep them. Can’t do too low. Can’t go outside of the range. Sweet spot of pricing and expand the time. Expensing to look like the time is filling. I can’t knock any firms taking advantage of this, especially when most have derived the business model from value creation, but it does seem that as time goes on, keeping that price premium and time valued becomes less of an advantage used for good and merely an indicator of what they should bring.

Time will tell for those that hang on the longest. Hope you enjoy the notes.

  • Cynthia Muller, Dir. of Mission Investment at WK Kellogg Fdn (Wharton XM, Dollars & Change)
    • Discussing consulting and the people or culture parts (@cynmull)
      • Merger where everything, paper and number-wise, looked like a perfect match
      • Failed miserably – many of the top producers were unhappy and the merger allowed them to leave easily
    • Satya Nadella at Microsoft reimagining the purpose – got to everyone PC-front but had to overhaul
    • Measuring people – upper quintile in survey of 500k employees (~500 companies) – middle management ratings of purpose
      • 7% YoY performance over others – not lower or upper – middle management was determining factor
  • Scott Kupor (@skupor), MP at Andreesen Horowitz (Wharton XM)
    • Discussion of becoming full-shop, including investments and RIA
    • Value add other than capital is very important to him
    • Tries to make decisions and No comes with why?
      • Sometimes they are wrong, see founders again and some have come back with addressing the reasons “no”
    • IPO extensions to 10+ years vs 6-8 – private and liquidity-driven
      • Discussed employee needs as a big reason for why it will stay 10-12 and not increase
      • Can’t compete with Google or others if you aren’t liquid
      • Early on, private companies aren’t worried about that with the people that can take the risks
    • Secrets of Sand Hill Road book, going through that
  • Brian Kelly, co-founder of The Points Guy (Wharton XM)
    tpg-primarylogo-color-28129

    • Selling to Red Ventures – taken private recently, also
    • Partnering with hotels and airlines to build an app in Austin – connect accounts, personalized, direct to airlines/hotels
      • Make it easier and hopefully change it for the better consumer experience
      • Turning it into a tech company moreso than a media one
    • Blogging initially, leaving Morgan Stanley – consumer-focused and not driven by partnerships
    • Only takes credit card partnerships instead of airlines or others
  • Benito Cachinero, Senior Advisor at Egon Zehnder (Wharton XM)
    egonzehnder_logo

    • Former CHRO at DuPont, ADP and leading succession processes
      • VP of HR for JnJ Medical, Corporate HR VP for MA Divestitures at Lucent Tech
    • Born in Spain, knew he wanted out at an early age
  • Eric Hippeau (@erichippeau), MP at Lerer Hippeau Ventures (20min VC 12/21/15)
    lerer_hippeau_ventures_logo

    • Chairman of RebelMouse, co-founder of NowThis Media
    • CEO in 90s of Ziff Davis initially as media company, the publisher of PC mags as well as conferences
      • Being in tech business moreso than media – sold to p/e firm before they sold to SoftBank
      • Before selling, they were about to be 2nd institutional investor in Yahoo but SoftBank made bid for 1/3 of Yahoo before IPO
      • He went to Yahoo Japan which allowed them to get a lot of source just due to the company
    • Sold business in late 90s, joined SoftBank as investor and opened firm in NY with them before his own
    • Backing company or business requires some business experience and growth/hiring and strategizing are all important
      • All partners at LHV have operating background – biggest difference is probably the time horizon (need really long view as VC)
      • Had just closed 5th fund, very satisfied with the work life instead of operating – running as a startup
      • $8.5 mln initially – no full-time employees initially, until the 2nd fund
    • First investments are at seed level, have always kept money in reserve for follow-on
      • 70% of co’s are in NY
    • Value add for LHV, generally – 2 levels of support
      • Product that is a technology platform that they plug everyone into
        • Recruiting and marketing database, best practices, current series A/B investors and what they’re seeking, Comms layer
      • Each company assigned to one partner and associate – bespoke plan and a to/do list for each company
        • Intros, branding, pricing, organizational structure and growth
    • Biggest problems for portfolio co’s – dependent on sector
      • Ex: SaaS: correctly size marketing opportunity for going after the right, big companies – largest/most important get a premium on the valuation
    • First check is typically $750k – $1mln – characterize this as collaboration between other funds
      • As long as terms are acceptable, let others lead or whatever is best when the companies are the best
    • Best pitch: what they’re looking for is the Big Idea – original, large market, tech-enabled, timing
    • Drone Racing League as public, recent investment: fantastic idea as drones are becoming more popular, variety of them, popularity of video games
  • Sumeet Shah (@PE_Feeds), Investor at Brand Foundry Ventures (20min VC 12/23/15)
    • Investments include Warby Parker, Birchbox, Contently
    • Grad from Columbia in 2008, biomedical and went to p/e through Gotham Consulting Partners (engineers at firm, diff industries)
      • P/E as two party system – deal team of firm and the client portfolio company
      • Lots of outside the box thinking, project work for 2 and B/D for 3 years
      • Met Andrew Mitchell who is the boss at Brand Foundry
    • July 2013 moved into start-up with friends with Gist Digital – help with bizdev
      • 6 months in, help with capital – Andrew reconnected – was offered a full-time job into vc
      • March 2014 was when he went full-time and after the first year is active – seed rounds, pre-seed occasionally
    • Paul and Sarah Lacey – series A crunch with tech/software/app-focused
      • Invested into Cotopaxi for $3mln seed round
      • Working alongside Indiegogo and Kickstarter and have invested in crowdfunding
    • Marketer, operator and technician and his due diligence takes between 2-4 weeks, typically
      • Take on doubles/triples compared to unicorn returns that are worth it – Eilene’s opinion to do unicorns
    • Believes over time that building reputation with doubles and triples, will stumble on a unicorn – those are the ones that can make the fund
    • Most value from investors – sign of weakness is not reaching out to investors
    • Different mindsets of East vs West coast
      • NY looks at building sustainable businesses, SV/SF is a $1 to a dream mentality (need this, still)
        • Want to look at revenue streams, traction, etc… but loonshots are ‘safer’ in SV
      • Founders as female-led – 7 of 13 of their investments have female founders and 3 of them are 2 co-founders female-led
    • No general people in the startups that may catastrophically fail in SV, so it’s okay for the funding to be gone
      • Bullish on TechStars Boulder, looking at ventures or accelerators that are growing in that region
    • Things A Little Bird Told Me as favorite book and most recent investment with LOLA – women’s biodegradable tampons
  • Carolyn Witte (@carolynwitte), co-founder & CEO of Tia Clinic (Wharton XM)
    z6kdoir2_200x200

    • Going from a tech AI program / chat – making women be comfortable with talking to a message
    • Before doctor appointments to after, and then having them bring her in with the doctors
    • How to interact – realized that they needed to complete the offering with their own clinic

 

  • Jessica Bennett, gender editor at NYT, “In Her Words” (Wharton XM)
    • Sympathetic attitudes and gender
  • Boris Wertz (@bwertz), founding partner of Version One (20min VC 12/28/15)
    4z_wfx6c_200x200

    • Top early-stage tech investor, board partner at Andreesen Horowitz, COO of Abebooks.com that sold to Amazon in ’08
    • 2005 named Pacific EY Entrepreneur of the Year
    • Internet 1.0 in 1999 – wanted to be apart of it – started JustBooks with some friends
      • Built it to Europe’s market leader and then sold to competitor AbeBooks before Amazon
    • Took proceeds and put into 35 internet and mobile companies – early wins, early exits and decided to do it professionally
      • First fund was $18mln
    • Power of bringing together customers across the world and finding the book – buyers/sellers in small marketplace with hard-to-find
      • Years and years of book fairs or local inventories that they were limited to
      • Passionate customer stories and being part of the company – personal way to see how marketplaces are important
    • Transportation vertical with Uber as unlocked in marketplaces
      • Mobile first, others – and their investments
      • “A Guide to Marketplaces” book by VersionOne
        • Precision for a thought that may have been in your head when you write – clarity
        • As supportive as possible to the startup ecosystem and how to impact entrepreneurs in portfolio or outside
        • What does VersionOne get excited about and how do they contribute or help?
        • 50 page guide put together for a framework and concise – depth but not overly so
    • Attractiveness of marketplaces
      • Fragmentation of supply/demand – more people on either side of marketplace, buyers/sellers
        • Buyers/suppliers sometimes want a monogamous relationship – doctors, cleaning personnel – don’t want to get someone new
        • Cab driver / uber – doesn’t matter who drives A to B as long as it’s safe
        • Transactional relationships vs monogamous
      • Size of underlying market, ebay grew from collectibles to all sort of products
      • Specific niche market – what is the kind of market you can address – specially-crafted goods
        • When he looks – lens of VC that needs a return, so needs to see a return on capital in 5-7 years
        • Operators can be great in this case because it can be very profitable, bootstrapped or friends/family money to get and grow
    • Demand or supply first? Any marketplace chicken and egg.
      • Depends on marketplace but once you have network effects, it takes off
      • Uber paying drivers to be idle just to have people in the area and have the supply
      • Addressing supply – how much to have? Hotspots.
        • Which transactions work really well?
        • Price point? Vertical? Certain buyer/supplier? AirBnb doubled down in NYC higher value rentals. Just needed that initially.
    • Trust and safety becomes more important after some attention – supply side with hobby sellers with a little bit of their inventory
      • Power starters are the ones that are stronger. Professional sellers.
    • Mobile first marketplaces and on-demand marketplaces excite VersionOne the most.
      • Services / products as on-demand (Fueling of cars, for instance)
      • Fascinated by decentralized marketplaces built by blockchain – will they ever make money but can’t generate money on own?
    • Measuring as VC: how happy are entrepreneurs, were ones that they met with taking away stuff, serving/help them and get feedback
    • Favorite book: Hard Things, Blog/newsletter – Fred Wilson’s
    • Overhyped: on-demand, Uber for X thing – underlying drivers for Uber’s success, for instance
    • Underhyped: quicker hype cycles – blockchain, VR/AR, drones and anything new is all over it in few months
    • Marketplace Key Metrics: gross merchandise sales and take rate (revenues compared to the gross sales)
    • Recent investment: HeadOut mobile first marketplace for travel experiences (NY, LA, Chi, SF, LA, Vegas)
      • Upcoming experiences in next 24 hours in that city

Refresh the Old and Tired (Notes from July 8 to 14, 2019) July 30, 2019

Posted by Anthony in Automation, Digital, experience, finance, Founders, Leadership, marketing, questions, social, Uncategorized, WomenInWork.
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For the abundant discussion on big tech, rise of tech and the valley’s obsession with all of it, there are quite a few industries that have had much longer staying power. They’ve proved their worth, decades and decades in. There are still railways. There are still cars. Manufacturing persists. CPG and everything that that entails last. Walmart, as much as people love (or don’t) Amazon, it’s still a lion’s share of commerce. Tech has improved and allowed them to have this staying power. Additionally, enabling improved efficiencies can allow new players in the industries to fundamentally change how they’re viewed.

Industries include tv – nonpartisan and bipartisan news with Carrie Sheffield. a16z gets into online from offline forms of services, restaurants to tech-enabled deliveries, as well as the rise of CAA and the agency fights. Then we have traffic and building with a consultant in that space. The next industry was making the legal space a little more transparent – provide a marketplace where information becomes symmetrical. I believe these are ways that simple pain points that can be improved through a technological lens give access to a value that wasn’t there before.

Hope you enjoy the shorter posting and the notes as more detailed. Check each of the wonderful people out!

  • Carrie Sheffield (@carriesheffield), co-founder of Bold TV (Wharton XM)
    slack-for-ios-upload-1

    • Discussing bipartisan vs nonpartisan
    • Growing up in very conservative areas and then going to the coast – seeing both sides, especially media
      • How it was to be in media
    • Fake news as non-fact-checked as well as actually fake – ~70%+ considering bias
    • Intellectual diversity along with everything else – thinking differently vs looking diverse
      • Used example of Google AI conference canceling on a colleague who was a conservative, black woman
  • Chia Chin Lee, CEO of BigBox VR (Wharton XM)
    ravlfjtl_400x400
  • Initially trying VR and finding it sickening – didn’t work (Oculus)
    • Tried HTC Vive and fell in love – had a room set up and felt enthralled
    • Hardware and platform may get cheaper with tech
      • Opportunity lies in the software side – connecting to others and industries

 

  • Entrepreneurs, Then and Now (a16z 6/29/19)
    • With Marc, Ben, Stewart Butterfield (@stewart)
    • 10 year anniversary for a16z in late June – how has the environment changed?
    • Class of 2009 entrepreneurs were some of the most special: Todd McKinnon, Martin, Brian Czesky
      • To get to that point, needed to earn your stripes
    • O2O – online to offline (AirBNB, Uber, DoorDash, Postmates, etc….)
      • Founders that may be more operationally-focused since those require that
        • Maybe more similar to semiconductor founders from the 1970s, start of 80s
    • Dual discipline people as they got more involved in healthcare or bio-related
      • 10 years ago, Bio PhD wouldn’t know much on computers but now, dual PhD’s
    • Economics + CS – discussion of field of economics with empirical / quantitative economics compared to physics or formulas
      • New inventions by economists with machine learning and data
    • New ideas – thought venture firms had lost way, founders/operators that built businesses who would help out on boards
      • GPs started to get more abstract ideas, professionalized
      • Institution and ecosystem, network and fundamental staffing model – pay at a16z is different than other VC’s
    • If priority was to find best founders at the best opportunities, shouldn’t matter which stage they’re at – miss things, maybe
      • Skype deal early, multiple entry points – working with entrepreneur and being stage-agnostic
      • Tech bubble bursting – “can’t possibly start fund” – 2009 was Khosla and them
        • Mentioned ‘crusty’ or ‘grouchy’ VC’s
    • Much of the tech was at an inflection point – Salesforce as only SaaS, iPhone not quite there yet, Uber, Airbnb
      • Maybe the main response should be “No, this thing is stupid” as more accurate
      • Never thought it was a bubble – prices of companies are always incorrect (future performance, which nobody knows)
      • East coast vs West coast – not obvious, find what each argue about
    • How high is up? Online pet delivery, all actually happening
      • What are the exploratory bets? Are markets ready? Are people ready? Regulators?
        • Sometimes it’s the pioneer, sometimes it’s the last – time and effort for founders, personality, other
    • No individual company gets 25 years to prove something – maybe 5 years for a hypothesis
      • Morale issue losing faith or architecture issue – prior architecture (ex: mobile dev in 2002, system on archaic and aging-in-place)
      • VC’s will do the same thing – kid doesn’t know about failed experiments – VC freeze themselves out (ones who don’t know will often invest)
        • Can you learn lessons from failure – maybe you should learn nothing – “That doesn’t work.”
        • Edison as trying 3000 combinations before the filament, Wright brothers trying many
    • Copying the model from CAA – Michael Lovitz and describing the whole thing – not a collection of individuals
      • Operating platform, system and infrastructure with professionals across the network
      • Compounding advantage year over year – but why can’t they copy? They were paying themselves all the money
        • Nobody wanted to take pay cuts – 80% to hire everyone at such a scale
    • Top end venture investment – need something working (product-market fit, product)
      • Do they know what they’re doing? Can they do their job scaling?
      • Second-time or later founders – can do what they want and figure stuff out?
        • Problem may be with the good idea – investments on that idea or otherwise (fragmented idea with nothing)
      • Idea maze to find out what the ideas are – haven’t gone through that
    • VCs can’t invest more than 20% of funds that aren’t primary equity investments – crypto, for instance (vs RIA)
    • Deadwood as creation of city or state – horrifying obstacles
      • Why History is Always Wrong? (Taleb’s narrative fallacy, for instance – often more complex)
        • Don’t even know body, climate still (too complex) – can converge on science to Newton’s laws, others
      • Can’t Hurt Me by David Goggins
  • Scott Kuznicki, Pres and Managing Engineer at Modern Traffic Consultants (Wharton XM)
    logo-text

    • Traffic control tech – California high speed rail vs autobahn style
      • Autonomous lanes?
    • Designated autonomous – level V vs others, depends on density and adoption
    • Thinks parking structures with flat tops could be converted or pay for cost
      • Multipurpose, solar, green or plants etc…
  • Risk, Incentive and Opportunity in Starting Co (FF 027, 20min VC)
    logo-062fd8699c93a47b2e8278975e71b84870194fd30c288607f1e06c92a4e831a0

    • Daniel van Binsbergen, CEO and co-founder of Lexoo
      • Online marketplace connecting businesses and lawyers
    • Founded it in 2014, got an investment for $1.7mil
    • Friends always asking for referrals – kept a short list of them
      • Seemed great, “quoted $X – is that good?” – perception of complexities
      • Could put make a marketplace together for transparency
    • Kept 100% of his income boosts – got used to his training salary so it wasn’t as big a risk
      • No kids meant it may have been easier – really disappointed if you didn’t give it a go – decision already made
    • Legal space’s lack of progression in tech – incentives in wrong place
      • Hourly model still for law – if you spend less time on work, you would make less money
      • Risk-spotting for lawyers
      • Senior partners have heaviest voice – not exactly lining up for retirement in the near term vs long term
    • Highest goal may not be senior partner – fixed fee, sharing risk, more open to innovating with own practice
    • Lexoo initially – didn’t have tech skills for it, had a vision in his head but didn’t know best way
      • Didn’t build full-scale solution, did a forum for $15 website, form to fill in
      • Arrived in his email – he would then contact lawyers and fill in Word template – get their responses and quotes
      • Attached the lawyers’ quote and response to a doc and pdf and send back to clients
      • Automated only when he couldn’t handle the workload – hit limit on evenings and quit
        • Lawyers paid 10% commission on the quotes
    • Focus on business ideas – tech isn’t the big solution – market innovation (access to litigators)
    • Investors at Forward Investors – introduced through a friend who knew them through squash partner
      • Difference between FOMO on being convinced vs other investors who have a sense of opportunity
    • Fav book: The Mob Test – how to ask questions to get useful feedback, asking questions to customers in the wrong way
      • Would you use the product if it does X, Y, Z – most definitely? Instead of asking what the customer problems are.
    • A lot of work in Trello, for goals, and Sunrise app – Microsoft’s indispensable for calendar meetings
  • Facebook Bargaining Bots Invented a Language (Data Skeptic 6/21/19)
    • Auction theory and econometrics – equilibrium strategy
    • Neither agent is incentivized to change strategy if the other stays the same
    • Plateau of events in real life – baby, marriage, life changes, job, lease ends in time
    • Discount is a single floating-point decimal, ex 0.99 ^ t
      • Everything known – can calculate based on common knowledge and discounts
    • Gaussian distribution, mean 100k, 10k – ignore tail in negative and renormalize
      • Rubenstein one-sided incomplete
    • Game: don’t know private value now, but can have probability distribution
      • Update with Bayesian with behavior
      • Classic ML: corpus of examples of negotiation, mark up conveniently, objective function to maximize reward (post-agree)
      • Opportunity for RL – patterns for language utterances, insult or compliment or neither – recognizing strategy
        • Character level or nothing to ask it
        • Conversations for language you don’t understand and the reward – can you do this optimally?
    • RL + Roll-out with 8.3 to agent and 4.3 to other algorithms (94.4% agreement)
      • Roll-out was 7.3 and then RL – 7.1 and last place was 5.4 for likelihood model
    • Training data was in English, negotiating over 3 items – shortcut its job, RL wants the short path to reward
      • His example – loses points if you went to pits but to reward – chance at falling
      • Wasn’t worth it to move, so he had to do a penalty for not moving
      • Penalty for Facebook example was agents continued to communicate in English
      • Put a time constraint, maybe
  • Transfer Learning with Sebastian Ruder (@seb_ruder), D/S at DeepMind (Data Skeptic 7/8/19)
    deepmind-1

    • Generally, TL is leveraging knowledge from different tasks or domains to do better on another task
    • Not a lot of training data, may want to pretrain – models to train on imagenet, for instance
      • Language modeling to train on large corpora and use that on a bunch of other tasks
      • Source vs target data: task stays the same but can adapt between source and target, say sentiment of reviews
    • Classic benchmarking, may have ImageNet moments over last year – features of pretrained models applied on more powerful NLP
    • Google XLNet’s most current, BERT and ELMo as others – pace of improvement has been great
    • Difficulty of target tasks – can be good for 100 samples in target source on binary tasks, maybe, 50 even?
      • 200 examples per label, question-answering or reasoning, examples must be increased
      • If we can express target task as a conditional language modeling, can do fewer or even inference
    • Pretraining is costly due to large clusters on your own, but now can be public pretraining where you can finetune quickly
    • Area of common sense reasoning – infer what a question means or expressed depends on what may not be said
      • Grass is green, entity facts (son of a son), inquiries for language model – incorporate to modeling

Innovative Investing (Notes from June 3 – June 9, 2019) June 25, 2019

Posted by Anthony in Automation, cannabis, Digital, education, experience, finance, Founders, global, Leadership, medicine, NFL, questions, social, Strategy, training, Uncategorized, WomenInWork.
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The primary theme of the week seemed to be how data can get pooled together to determine a signal and how to learn to seek the best way we, as individuals or teams, can discern valuable content to motivate actions on that information. Data is plenty – it’s a matter of gathering, curation, analysis and testing before putting it into action. This is done by any number and types of companies nowadays – this is a source of advantage seeking that forward-thinking ones make, in my opinion.

Since my notes were more detailed, I’ll try to keep this brief. The wonder people below hailed from banks (First Republic Bank), funds like Emerson Collective and Womens VCFund, marketing company like BEN or LikeFolio and then David Epstein’s Range, Sinead O’Sullivan’s work on space or the data Rohan Kumar collects with Azure Data.

Create a hypothesis. Test the hypothesis. Put into action, or iterate. Rinse, repeat. Good luck!

  • Samir Kaji, (@samirkaji) MD @ First Republic Bank (20min VC 093)
    first20republic20logo_gkg

    • Leading private bank and wealth management, before at SVB
    • 1999 – “anyone with a pulse could get a job” but he was working selling vacuum cleaners at dept store
      • Was told by family to get a real job – applied to first business SVB, got resume in and interview immediately before starting
      • First couple years were tough – learned a lot, but was 2004 until companies had scaled and were getting bigger
    • First 10 years were tech companies, series A and B and venture debt – post 2009 Lehman / Bear, went to venture group at SVB for 4 years
      • Made the move with a few others from SVB to First Republic, now leading team in micro-VC and early-stage tech co’s
    • Says the micro-VC is more entrepreneurial & collegial compared to extended stage VC’s
      • First fund is that you can get traction for a second or third one, fees as pressure – most likely why many people come from some wealth
        • Writing large checks as GP, as well
      • 2-2.5% management fees initially vs 1 / 25 or 1/30 model
      • 1999 – 2002 distribution was 0.9x and you’d get 10x return (whoops) – very difficult for funds to get 2-3x for LPs
    • Barriers to entry much smaller for $20-25million as compared to $500mln – institutional, etc — he can go to family friends and high net worth
    • Seed over next 5 years: contraction in space (wrong), but said there isn’t enough returns for funds to max it
      • 1100 in the 2000 year and burst
      • Continued prominence of Angelist platforms, maybe an integral part of the ecosystem
      • Starting to see use of data (Mattermark, CBInsights, SignalFire) to more efficiently identify and action at this level
    • Favorite book is Phil Jackson’s – behavioral psychology, Give and Take is another one
    • Really respects the pioneers of the industry and first-time fund-raisers
      • Mike Maples, Michael Deering, Steve Anderson, Jeff Clavier when it wasn’t a thought
    • Habit – reading book or blog post for 20min in the morning before email
      • Disconnect from audio / video devices and reflect for an hour
      • 2 hours a day for family/friends and disconnecting, as well
    • Thomas Redpoint, Mark Suster, Brad Feld, Strictly VC, Ezra at Chicago Ventures
    • Knows awesome fundraisers but terrible at returning capital – didn’t mention any
  • Collectively Driving Change, Laurene Powell Jobs and Ben Horowitz (a16z 5/27/2019)
    emerson-full-logo

    • LPJ – founder, president of Emerson Collective
    • Grew up in NJ – father passed away in a plane accident when she was 3 – 3 children.
      • Mom remarried so there were 6 of them. Wooded area of NJ.
      • Core values and dedication to education to get out of the area.
      • She went to Upenn – first student from her high school that went to Ivy League – ~20% went on to more schools
    • Addressing East Palo Alto school as a volunteer to help – 1st talk, 0 had taken SATs
      • What happens when you’re first to graduate high school? What’s it mean to the information from family?
      • What happens to be first to want to go to college, thrive&complete it?
        • To have the aspiration, can be a leader in the family – translator, get sucked into all problems
      • Started with 25 freshmen – would have to come with friends for responsibility mechanisms – for College Track
        • 3000 high school students, 1000 college, 550 grads
    • Collective of leaders, innovators – education inequities, access and need for enhanced/robust curriculum
    • 10 year time horizons – getting them together is scheduled with Monday all-staff meetings (3×3 matrix of videos)
      • 5 cities, sometimes philanthropic speakers or reports
      • Discussion of reading as you fall behind through third grade before switching to reading to learn – already behind
    • XQ as SuperSchool dream – 17 of 19 will open in August
    • Caring about impact and solving problems, not wealth increasing – wants access to policy or money and not taxes
      • Judged Giving Pledge for not wanting to be more philanthropic
      • Environmental, edtech portfolio, cancer / oncology investments, immigration incubator, new thinking to old problems
    • How do you know when you’re succeeding? Collecting data on everything they do.
      • Example: XQ – schools and districts, state of RI as switching to statewide competition
      • Chicago has good data for fatal/nonfatal deaths (I disagree)
    • Imperiled or important institutions like journalism and media need to be sustained, how many join?
      • Concentrating and following where IQ is migrating (hahaha – what a joke)
  • Data Infrastructure in the Cloud, Rohan Kumar at BUILD conference (Data Skeptic, 5/18/19)
    microsoft-azure-new-logo-2017

    • Corp VP of Eng of Azure Data Team at Microsoft – SQL and data services, open source, analytics, etc
    • Trends in data engineering in the cloud, serverless and hyperscale
      • ML and AI and enabling applications – shifting to edge vs cloud – analysts predict 70% will be on edge devices
      • Solutions and private edges – training in the cloud and deploy them on the edge applications
        • Data platform needs to be the right foundation
    • Highlight for him from conference: work they’ve done on relational databases in the cloud – as volumes grow, scalability challenges
      • Hyperscale for Azure and PostgreSQL, as well as MS SQL soon enough – system scales with needs (they’ve tested <= 100TB)
    • Acquired Citus Data, support scaling out the compute layer – strong team, great product, matches in Azure and open-source
    • Releasing serverless option for Azure database – costs designed to stay low and optimized
    • Analytics side: customers wanted to do real-time operational analytics – didn’t want to move them outside of their core product
      • How is data distributed and having compute be co-located with the data to gain Spark efficiency being nearest to node
      • Support Jupyter notebooks across all APIs to modernize to do more predictive analytics
      • Attempting to build out pipelines requires too much scripts, instead have Data Flows in Azure Data Factory – no-code and UI
      • Wrangling data visually and seeing if something can be recognized or learned to repeat across other columns/tables
    • Latency won’t be ideal if compute nodes occur nonlocal to the data changes – can’t do 50,000 nodes all at once
    • Excited for the future: Horizon 1 (next 8-12 months), Horizon 2 (~3 years), Horizon 3 (moonshots)
      • H2: Hardware trends, what do customers want? Pushing boundaries of AI and ML, healthcare, gaming, financial services, retail
  • Wide or Deep? David Epstein, author of Range (Invest like the Best, 5/28/19, ep. 133)
    • First book’s research lead him to get into specialization and finding kernel for next
      • Some countries: turning around national sports teams – why don’t we try other sports? Contrary to 10,000 hour rule.
      • SSAC – debating Gladwell – athletes have a sampling period instead of first gene – delay specialization
        • Used Tiger vs Roger – Roger had tried a ton of sports vs Tiger who was born and was playing golf
    • He was not good at predicting what people/public would attach themselves on to – 10,000 hour rule – race/gender as most talked (but weren’t)
      • 10,000 hour rule were based on 30 violinists in world famous music academy (restriction of range)
      • Height in American population vs points scored in NBA (positive correlation) but if you restrict height to NBA players, negative
    • Finnish cross country skier who has genetic mutation similar to Lance’s boosted
      • Sensitivity to pain and modification to your environment – also sudden cardiac arrest in athletes (what pushed his interests)
      • Book as opposition to Outliers and Talent Code – interpreted a lack of evidence as evidence of absence (genetics matter)
        • First year he read 10 journal articles a day and not writing – they were making conclusions they could not make based on their data
      • Differential responses to training – best talent were missed because we don’t know about training responses
    • Collection and exploration phase – competitive advantage for expansive search function to connect sources or topics
      • Has a statistician on retainer, essentially, to check models or surveys
      • Wanted to know what he was missing – “how come I broke the 800m women’s world record after 2 years of practice? – genetic difference”
        • Racing whippets – 40% had a genetic defect that gave them more muscle and oxygen
    • All of sports as a limited analogy (problem after Sports Gene; now, more tempered)
      • Robin Hogarth addressed “When do people get better with experience?” Don’t know rules, can try to deduce them but can’t know for sure.
      • Kind learning environment: feedback immediate, steps clear, information, goal ahead
      • Wicked learning environment: can’t see all information, don’t wait for others, feedback delayed/inaccurate
    • Study at Air Force on “Impact of Teacher Quality on Cadets”
      • Have to take 3 maths – calc I, II, III (20 kids randomized) – professors best at causing kids to do well (overperforming) systematically undermined their performance thereafter
        • 6th in performance and 7th in student evaluations was dead last in deep learning
        • Narrow curricula were better at the test that they had at the end would be negatively correlated with going forward in performance
      • Teachers that ignored what was on the test taught a broader curriculum (making connections vs procedures)
    • Learning hacks: Testing (wonderful – primed to test ahead of learning), Spacing (deliberate not-practicing, Spanish ex spread 4 hour twice, 8 hours), Mixed practice
      • Ease is bad – known time horizon for when you have forgotten again – interleaving and spacing mixed
    • Passion vs Grit (“Trouble with Too Much Grit” – Angela Duckworth’s research)
      • Duckworth did a study at West Point for East Barracks cadets – candidates score (test + leadership + athletic) was not good prediction of doing this (overall it was good)
        • Grit was a better predictor for making it through East Barracks – she questioned whether it had an independent aspect
        • Variance for grit was probably 1-6%, especially after “flattening” groups – looking at people that had a narrowly defined goal for short periods (cadets or spellers)
      • Cadets were scoring lower on grit at late 20s vs earlier – tried some things, learned others about what they want – grit is poorly constructed
        • Look holistically – if, then signatures (giant rave – introvert, small team – extroverts) right fit looks like grit – developmental trajectory as explosion matching spot
    • Choosing a match for a future them who they don’t know in a world they can’t comprehend – people that find good fits (in practice, not theory)
      • Paul Graham’s “Commencement Speech” that he wrote “Most will tell you to predict what you want in 20 years and march toward it.” (premature optimization)
        • Everything you know is constrained by our previous experiences – limited as a teenager – just expanding and learning as you go forward
    • Gameboy example – with so much specialized information that can be disseminated easier – can take from all types of domains and recombine them
      • System of parallel trenches – can be broader much easier now – hired people for Japanese and German translations
      • Japanese man profiled in his book – technology was changing faster than sun melts ice – didn’t get Tokyo interviews
        • When he got to Kyoto company making playing cards, he was a tinkerer who was maintaining machines – started to mess with them (arms)
        • Turned them into a toy, and it was Nintendo – cartoon-branded noodles (failed), and had toy development
          • Lateral thinking with withered technology – stuff that’s cheap, easily available – takes into other areas
            • Remote control, more features – wanted to democratize this and strips it down – LeftyRX only left-turns
        • Sees calculator from Sharp and Casio and thinks he can do a screen and handheld game – small games
          • Had issues with Newton’s rings so he found other small tech (credit cards embossed) to fix small pieces
      • What it lacked in color, graphics and durability (could dry it out, batteries would be fine, split it up, “app” developers because it was super easy to understand)
      • In areas that next steps were clear, specialists were much better – less clear, generalists were more impactful – depends on the specificity of the problem
        • 3M had a lot of areas for this, “Periodic Table of Technology” – post-it note came from reusable adhesive that had no use for
        • Only Chinese national woman to win Nobel – “Three No’s” (No post-grad, foreign research, membership in academy)
          • Interest in science, history – Chinese medicine for treatments of malaria – world’s most effective treatment from ancient text
  • Greg Isaacs, BEN (Branded Entertainment Network) (Wharton XM, Marketing)
    Print

    • Discussion of getting data from Netflix / Amazon / Hulu / tv to better match brands and advertising
      • Dirty data via a wharton grad who set up a survey style
      • Cohorts and demographics, along with psychographics
    • After getting data, attempting to approach Youtubers / social media influencers, tv spots and channels or shows to get their brands in front of the right people
      • More pointed, depending on what interests are for their cohorts
      • Creative storytelling as the change of cultural mind shift has increased
  • Understanding the Space Economy, Sinead O’Sullivan (@sineados1), entrepreneur fellow at HBS (HBR IdeaCast #684, 5/28/19)
    • Facebook, Amazon (3000), SpaceX (12,000) and other funding like Blue Origin / SpaceX / asteroid mining or travel
    • Global space economy as $1tn by 20 years – currently $325bn so it would need to 3x
      • Breaking apart space resources and otherwise – earth-focused (delivering or existing in space that helps earth)
        • Exploration or creating interplanetary existence
    • Running out of space in space for satellites – comparing to airplane docking / loading
      • $2500 per kg now to launch, used to be $50k / kg
    • Reliance had been on unilateral agreement for space policy – one tech startup launched a satellite that didn’t have permission (but no fall-out)
      • Food / grocery stores, wifi, phone, insurance pricing due to satellite data – reliance on services are increasing as the market increases
      • Thinks that we’re close to seeing the cheapest cost of launching – cites SpaceX, but won’t allow everyone to participate
    • Ultrahigh accuracy will require higher powered satellites – GPS, nonmilitary grade is ~0.5 m – thinks it will prevent autonomous vehicles solution
    • Ton of money going into asteroid mining but thinks it’s better for testing missions to Mars and figuring out the problems for future
      • Looking at Uber at start and say “people won’t get into a stranger’s car” or other cases as how we see the future – going to Mars, etc
    • Earth-focused space technology – 100+ launched satellite start-ups, micronano satellites, relay companies, downstream analytics
      • More touchpoints for everything in this manner
      • SpaceX will increase public and government intervention and within 50 years, maybe see a human launched there
  • Investing w Twitter Sentiment, Andy Swan (@andyswan), LikeFolio (Standard Deviations, 4/25/19)
    logo402x

    • 1700+ tweets examined per minute in LikeFolio – discovering consumer behavior shifts before news
      • Direct partnership with Twitter to create massive database and how they’re talked about to look for mentions
      • Purchase intent, sentiment mentions – trends across product categories or brands
    • Example – Delta (as host is a loyalist) – making adjustments
      • Expectations are the relative part – comparison to the baselines (metrics compared to itself as baseline)
    • Put out a comprehensive report on Apple day after keynote event – September 14, 2018
      • Consumers were unimpressed with iPhone lineup – more price sensitive than maybe they’d considered
      • Apple Watch was the silver lining – stock / sales may struggle over 3-9 months (upgrade cycles)
    • WTW version of keynotes – NYE resolutions – subscribing early to drive revenues the rest of the way
      • Purchasing mentions were only up 30-40% compared to 5 or 7x weekly mentions (big difference)
    • Shelf-life and how to consider the sentiment data – lead time may be binary corp event (same store sales or year)
      • Couple months with Apple, for instance, but with Crocs – resurgence that persisted to current time
    • Set up keyword structure and brand database – “I’m eating an apple” as opposed to an Apple mention – human eyes to ‘label’
      • “Closed my 3 rings” – apple watch but sarcasm / spam that wasn’t caught (estimates at 2-3% of data)
      • If spam / sarcasm are consistent portions of the data, doesn’t really have an effect
    • Twitter Mood Predicts Stock Market – Bollen, Mao, Zeng (88% and 5-6% predictions) – fund closed up shortly
    • Advantage being better than analysts or pricing and codifying sentiment behavior compared to past quarters, data
      • Some consumer trends analyzed as true tipping point or actual movements
      • Public prediction before productizing their modeling – made 40 and were 38-2 (confidence as highest)
      • Investing as very specific, concentrated and holding ammo compared to trading with option spreads and has risk profile built
    • https://arxiv.org/pdf/1010.3003.pdf
    • Diversification as 20-25 stocks, doing it over time and with conviction can be done
    • Starting in Louisville for his fintech company, host in Alabama, for instance
      • Talent can be more difficult to seek out but the world is globally flattening via the internet
      • 70% lower overhead cost than being in SF, for instance – developers would anyhow be in Slack channels / not a big deal
      • Reduction in cost maintains greater control of company since they don’t have to take reduction of equity to gather more
    • Network effects don’t matter if you don’t have a great product or product-market-fit
    • Free association game
      • grapenuts: best cereal (Co’s been around for 100+ years, branding and $ spent and they can’t figure it out)
      • Fintech Future: individualization and customization
      • Victory: most important thing in life, achieved what you set out to do – setting goals and achieving these
      • Bourbon: pappie von winkle – collecting for dust on shelf 10 years ago and now going for $3000
  • Jonathan Abrams, co-founder Nuzzel news (Launch Pad)
    nuzzel

    • Landing hedgehog as the mascot – animal as cute, 99designs and surveying 50 friends – 25 men/women
    • Discussing how VC’s don’t have great advice, especially when general – too hard to be an expert in such a wide range
      • Finds it easier to be very context-driven and providing solutions or action-oriented questions to founders
      • Investing now easier with YC and Angelist, etc…
    • Timing and other mistakes he made – out of control, losing equity part early (but depends on where you are / what you need)
  • Etan Green, professor at Wharton (Wharton Moneyball)
    • Discussion on paper of how sharp money comes in at horse racing tracks
      • Difference between sites – fairground action compared to tracks, and specific to region (New Orleans, Minnesota, for instance)
      • Big sharp money comes in very late, pushing the underdog prices to higher values
        • More expensive to bet while at the track than the APIs enabling higher volume bets
        • Books at the track are incentivized to bring in as much $ as possible, so $0.20 on $1 vs $0.15 rebate on $0.20 for volume
    • Value and differences in how people will bet
  • Edith Dorsen, Women’s VCFund founder, MD (Wharton XM)
    wvcfii_logo

    • Talking about their focus on first fund, approach
    • Opportunity for finding diverse founders, 25% of their fund had a woman founder
    • Starting a second fund
    • Had consumer tech, enterprise and not so much b2b, but trying to increase
      • Hard to say or give advice if one of their partners don’t have expertise in the domain
  • Sophie Lanfear, Silverback Films producer on Netflix “Our Planet” (Wharton XM)
    • Species that are dying, going extinct
    • What we can do about it
  • Aliza Sherman, Ellementa co-founder, CEO (Wharton XM)
    logo

    • Discussion of client talks when she made them aware of her cannabis endeavors
    • How friendly the community is
      • Then knocked the idea that ~30% was female to start before diving off a cliff
    • CBD to mask opioids – does it really do anything from a pain/treatment perspective, though?
      • Anti-chemo because of CBD – really?
    • Sounded too rehearsed – made it sound fake, not genuine
      • Passion/motivation/mission and kept repeating as the best advice she could give – painful

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)
    logo-4-300x187

    • 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)
    icryo-cryotherapy-logo-uai-720x433-5-3-300x181

    • 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)
    43260847

    • 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)
    mb_vroom_id_1
  • 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)
    page_55dc6912ed2231e37c556b6d_bsv_logo_v15.3

    • 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

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)
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    • 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)
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    • 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
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