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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)
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    • 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)
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    • 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)
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    • 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)
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    • 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)
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    • 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)
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    • 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)
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    • 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
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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)
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    • 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)
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    • 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)
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    • 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)
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    • 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)
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    • 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)
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    • 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

Different Ways to Create (Notes from June 10 – June 16, 2019) July 3, 2019

Posted by Anthony in Digital, experience, finance, Founders, global, Hiring, Leadership, NFL, questions, social, Strategy, training, Uncategorized, WomenInWork.
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3 fantastic sounding women to start. One in VC and finance, discussing the difference between NYC and SF for her. The second compared in-house marketing strategy and outside influence. What’s that look like? How much control is there? Last, but certainly not least, was an author who discusses something that I’ve seen with family and my sister – the challenge of raising a child while balancing some semblance of normalcy in work. What’s expected from yourself? What should be reasonably expected from work? What’s a balance?

Those women: Erin Glenn, Julie Scelzo and Lauren Smith Brody.

A few sportsmen discussed data and capital. Sixers Innovation Lab and former exec for And1 mentioned how they think about growth in Philadelphia and the brand, who can they support in the community that can also help with the team. John Urschel, former Baltimore Raven, is a published mathematician now who discussed the influx of data collection and analysis among all sports and teams. What they can do makes a great athlete experience, fan experience and overall performance improves.

A plethora of rising stars followed, from Kanyi of Collaborative Fund to Sofia Colucci of Coors and the co-founders for SHINE text. Hope you enjoy my notes and you check out the podcast episodes!

  • Erin Glenn (@leeeringlenn), CEO of Quire (20min VC FF025)
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    • Entrepreneur as kid – day business for summer camps, then management consulting, IB and took a company public (econ consulting firm)
    • Got bug to start own thing in 2010 – joined KIXEYE in SF for 4 years, video game company
    • Wanted to go to NYC (as kid in OK) – went to meet w Betaworks, fell in love with Quire
      • Mutual conv to join Quire – loved it – equity crowdfund co
      • Venture-back co’s enabling portion to raise for community & mission
        • Min. investment is $2500 – supporting larger investments as well, up to $250k
    • Likelihood for investors to get taken advantage of – Title III discussion (investors with <$100k income/net worth can invest up to $2k or 5% of income)
    • Mattermark study on investor bases that exist and why people do invest
      • Investor and diversity – minority, gender, big differences in those that follow Mattermark or others
    • Crowd won’t provide scaling / grow money (the $50mil+ rounds), but community can help participation at a lower level
    • Motivation to invest, other than financial incentive – supporting company’s mission + founders, spurring economic growth + innovation
      • Real commitment to realize dreams, grow economy
    • Benefits with crowd investing for company – moral and psychological
      • Supporters of the company can invest, which is reinforcing for doing it – customers that are owners of the business spend more, loyal, etc
    • SF vs NY startup ecosystems and CEO role
      • Had joined Quire with 2 suitcases, dog and air mattress after 2 days there
      • CEO role – really fun and exhilarating with challenges daily, gained confidence at eliciting feedback from ideas
        • Coming up with better solutions and getting them to help because we don’t have all answers
      • Intensity and vibrancy, competitive spirit in NY even though it’s smaller-feeling
        • Want to take on SV and not give up the competitiveness
        • More female founders in NY – fashion, finance, media in senior executives trying new things
    • Favorite book: Magic Mountain ahead of WWII in Europe, Switzerland
    • Favorite blog: Fred Wilson’s and Tim Cook as favorite innovator
    • Gimlet Media (first investment), Kano, Duel as others
  • Julie Scelzo, executive creative director at McGarryBowen (Wharton XM)
    mcgarrybowen-logo-2

    • Talking about marketing difference between in house and outside
      • Going from Creative MD for Pandora to take on MGB AMEX
    • Moving from agency to internal at Facebook – not even a salary bump, but just felt right
      • Worked helping clients was rewarding but she missed creating
  • Lauren Smith Brody, author of The Fifth Trimester (Wharton XM)
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    • Discussion of parental leave in the workplace – if uneven with your partner, mixing it up or staggering
    • First 6 months as crucial for development – how to best alleviate this
      • Every person is different and has different attitudes
      • Nobody can generally be told how something may feel for them
    • Having the partner available in the first 6-9 months provides evidence that they’re capable, and can understand some of processes
    • First day of work being scary – moreso as a parent – train whole life to be in workplace
      • Can be comforting back at work, not so much for first days as a parent
  • Dilip Goswami, Molekule Air Filters (Wharton XM)
    • Being his father’s son, a typical engineer
    • Developing and deciding what part of product to have in house vs outside
      • Hybrid model
    • Having customer support and knowing it worked – shipping and using that as validation
  • Seth Berger, founder and CEO of And1, Sixers Innovation Lab (Wharton XM)
    170718_innovationlab

    • Discussing how coaching basketball to young adults was so helpful
    • Marrying And1 with his passion for basketball and teaching and being around it
    • Sixers Innovation Lab – knew Josh from the 90s working on a failed internet co originally
      • Helping with capital up to $1mn and seeing 10x returns so far
  • John Urschel (@johnCurschel), Former lineman with Ravens, MIT mathematician (Wharton XM)
    • Talking about the lifelong balance of math / football from his memoir
    • Thinking about where analytics may be super exciting in sports – real-time strategy if they’re allowed the computers / data on-field/court
      • Tracking data is so strong, it’d be interesting to see what coaches may do to get there
  • Nathan Furr, Curtis Lefrandt, Innovation Capital author (Wharton XM)
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    • Author discussing how innovation costs resources
    • Talking with Marc Benioff and others for the most innovative leaders

 

 

 

 

  • Sofia Colucci, VP Innovation of Miller Coors (Measured Thoughts, Wharton)
    • Introducing a new brand, Cape Line, into the world
      • Usually a 1.5 – 2 year process for a corp this size
      • Cut it down and released in 2019, dropped the other project (Project Sprint)
    • Had already done market research, wanted a more healthy, alternative to beer for women – cocktails in a can
      • Packaging and what that would look like after tasting
  • Jennifer Pryce (@jennpryce), President CEO of Calvert Impact Capital (Wharton XM)
    • Impact capital and how they grade different companies on the degrees for investment
    • Infrastructure, seeing them surpass $1bn
  • Marah Lidey (@marahml), Naomi Hirabayashi, co-founders of SHINE app (Wharton XM)
    246x0w

    • SHINE as a wellness app for meditation
      • Gaining ground with their superusers – seeking feedback
    • Self-care platform, weren’t sure how they attracted so many men – but it’s definitely catered to their experiecne
      • Reached out to one of the first superusers that was male to get his input and to have influencers help
    • Product-market fit and development was always based on how they wanted the app to be- what they were searching for
  • Kanyi Maqubela (@km), Partner @ Collaborative Fund (20min VC 094)
    deuobz-u8aarwgs

    • From South Africa originally, investments into CodeAcademy, Reddit, AngelList, AltSchool, TaskRabbit
    • Founding employee of Doostang, attended Stanford Uni & worked on Obama campaign in 2008, as well
      • Dropped out of Stanford, compelled by interest to see other part of world – did a startup, $20mil of VC funding for a couple startups
        • Being young, decision to leave was easy but once he’d left, it was tough
        • Making friendships and lasting connections easily in college – some communities outside, in pro world, was rough
      • Met his partner, Craig, while finishing school and doing work in design – convinced him to help him with CF
    • Investors are those that believe in collaborative economy – nodes, peer-to-peer and nodes for networking
      • Every consumer/employee/companies have obligation to align interests and value sets
      • Looking at companies to focus on impact and values – aspirational culture as outcome of collaboration
    • For the fund – stage specialization or theme?
      • Theme may be time-efficient-oriented. Reminder that many of most successful people have skipped on massive wins multiple times over.
        • Altman mentioned about having a point of view and heuristic to drive decisions (whether it’s stage or theme)
    • Being a partner at 30 – GPs with skin in the game
      • As young, have to have been very successful early or came from money to get into the fund
      • Needs to prove himself but as younger, may have been very risk adverse in the sense he wasn’t free-swinging
        • Facebook went public 7 years (quick for industry, but not necessarily quick for a fund) – feedback loop timeframes
      • Million ways to market as investor, drive value as portfolio, data, theme or stage specific
        • Blog as high leverage marketing for himself, writing is how he clarifies his ideas to himself and the public
    • Limits and is very prescriptive for the networking aspect of VC, conferences – wife in medical school so when she’s free, he makes himself free
    • Accelerator / demo days as good for investing – he likes being first institutional round, but thinks demo day to discover is not their best way
      • Sometimes the due diligence for demo days of seeing what’s out there
      • He uses them to talk to other VCs, see source and deal flow – coopetition – high leverage, high marketing channel
      • His best way in is likely the portfolio companies under them – he looks for connections for new places and vouch for them
    • Naming Fidelity markdown of a bunch of companies – saying that private companies are being treated like they’re public companies
      • Realtime prospects that are valued – can go up or down, financing or not
      • Private crowdfunding to create liquidity, getting to cash flows and thinking about dividends, debt, crowdfunding – IPO bar is so painful
    • Fav book: Brothers Karamazov – Dostoevsky as “fiction bible”
    • Union Square Ventures as the one he looks up to – Benchmark, also (Read ebooks)
    • Concept of Founder-friendly – agency from founders holding them responsible, but becomes messy / complicated
    • Most recent investment at that time: CircleUp was series C, crowdfunding platform for CPG – other forms of financing for orgs will be transformed

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)
    ah-logo-sm

    • Ali Yahya (@ali01), Frank Chen and looking at crypto
    • New paradigm has to improve upon one or two particular ways for new applications, but likely sucks in most others
      • Mobile phone as enabling behaviors for instagram/uber and such with camera and gps in the phones
      • Have to trust the company currently to do what they claim to be doing – trust Google with so much to have the interactions
      • Now, have a computational fabric that separates the control of human power, self-policing and security bottom-up
    • Difference in communication cost – bounded on the end by speed of light
    • Trying to make networking efficient – miners propogating to other miners – blockchain distribution network
      • 1 MB vs 2 MB blocks but can kill some small miners
      • Agreeing on blocks or updates are final
    • Non-probabilistic vs fast – need to be faster than 60 minutes, for instance
      • Improvements on networking layer with Ethereum 2.0, Cosmo and cost or Proof of Stake (vs Of Work)
        • Who gets to participate? PoW requires everyone to compute an expensive proof of work.
        • PoS – intrinsic token that you have to own in order to buy participation, 2% ownership says 2% of the say to say yay or nay
        • Cost of participating is less expensive because it’s intrinsic
      • Practical for everyday use, small interactions between people and machines or people
      • Trust as the bottleneck to scale
    • 3 Pillars of Computation/Scalability: Throughput, latency, cost per instruction
  • Daniel Waterhouse (@wanderingvc), GP at Balderton Capital (20min VC 091)
    cqis46nlxvy5bkvj15kh

    • 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)
    yawnofc__400x400

    • VP of BD at Upstream, enterprise sales in 40 countries in 4 continents
    • Wanted to start a company with type of what they wanted – centered around product, engineering company, starting in Greece
    • SMB over Fortune 500 because of product-focused and not corporation or enterprise-based
    • Applicant-tracking systems (ATS) – keep track of arcane process, don’t want to touch things – collaborative processes
      • Simple interface to solve problem of recruiting model (which is still 50+ years old)
    • Problems aren’t location-based – they’re conceptual – designing product, PMF
      • Opening with $500k or 50-200k if needing to get started, not necessarily $5-10mln like other markets or tech hubs, industry
      • Hiring engineers that are good in Greece isn’t so much a problem if you have a good company and network to attract talent
      • Moved to US not for VCs (doing good business, VCs pay attention) but they had 50% of customers without having anyone in US
        • Wanted to start customer support infrastructure, services and otherwise
        • Talked about marketing or accounting businesses on tech that taught companies that it may be worth it to update
    • Metrics that he pays attention to: month-to-month above $2mln annual revenue, ratio of new biz and lost biz
      • Not celebrating fundraising – few drinks but says it’s like having new shoes at the start of a marathon
      • Talking about investors and the relationships built to work together
  • Jaz Banga (@jsbanga), cofounder, CEO of Airspace Systems (Wharton XM)
    airspace-metal-crest2x

    • Meeting Earl, cofounder and maker, at Burning Man – drone being annoying above him while he did tai-chi
    • Prototyping the interceptor drones after seeing military request proposals – had an issue with drone over nuclear subs
    • Can place thermal scans and other security or rescue methods
  • The Transformer (Data Skeptic 5/3/19)
    • Encoder/decoder architecture of vector embeddings word2vec into a more contextual use case
    • Keyword lookup may not work (using ex of bank – river bank vs financial)
      • Humans at 95%+ accuracy, computer maybe around 50%
      • Encoding as correct interpretations and weights for context – emulating process
  • Laura Edell (@laura_e_edell) from MS Build 2019, NCAA predictions on Spark (Data Skeptic 5/9/19)
    build-2019-intro

    • 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

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

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

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

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

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

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

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

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

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

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

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

    • Managing D/S Teams and how to organize development of algorithms and the processes
    • Corporate business organization of data science teams vs packaging and product building or open source work – known for more of that
    • Undergrad in Math, went to Wall Street after – got a lot of data analysis in the market, wasn’t a match for her ~15 years ago
      • Then went to strategy consulting – focused on pharmaceutical strategy, testing and experiment analysis
      • Went to marketing services industry – finally saw big data – (no longer any single machine work)
    • Talked about excelling as an individual contributor and moving to management as a different discipline in itself
      • First person she managed: quit the first day, had been a PhD graduate and assumed he was working with her, not for? (What a prick?)
    • Worked with teams in ops, finance, IT, engineering, R&D, etc…
      • Re-orgs for data science portion – always changing branches
      • If data science isn’t the product, within legacy/corporation, the team needs time to figure out the objective of the organization
        • Get past exploration and become experts
        • Her take on managers would be that they create space (o-line) for individual contributors to do their work as quarterbacks
    • As teams grow in size over time (using her experience as Manager and Director from ground up), potential vs low-hanging fruit
      • High visibility and high sophistication to give a leg up on what could be possible for the organization – low-hanging fruit is easy
      • Starting data science team have generalists but very good to mature into a better team, specialization
    • Humility for data scientists – avoiding the correlation factors that you build from gathering and going through data initially
      • What kind of questions should be answered?
    • Parts of data science that you can’t teach – how vs wanting to answer questions
      • Certain bootcamps are worthy of what they teach, organize – mentioned universities as not having programs until recently
        • Mentioned a team member trained initially as marine biologist – traveled and researched pods of dolphins
          • Modeling expertise for a fleet of robots as operating independently and together
    • Harder for C-suite to not be able to talk data in the strategy sessions for decision making
      • Common pitfalls of manager:
        • Data team doesn’t know how the data is gathered or where all it’s coming from
          • Have a data party or something to organize the data creation, designed, labeled, and stored
        • Not overpromising or underpromising
          • Lend credibility to actual outcome – being honest, transparent with other disciplines to interrogate situations
    • Her paper for HBR – Managing Data Science for AI
  • The Future of Decision-Making (a16z May 1, 2019)
    • Frank Chen and Jad Naous (via YT initially) of Enterprise Investing team
    • Digital transformation where industries are shifting to this design
      • Changing from manual to automated, digital processes and more agile
      • People’s roles will start to shift around – demand for new tools and dynamics for who wins in spaces
    • Product management – features or bugs would have been surveys manually or collecting data to figure out problem and sort them all
      • Now, the tools automate these from the product itself, often – now they can look at the dashboard of numbers
    • Marketing side: Growth hacking and market engineering – low cost to increase growth in certain parts of customer segments
      • Decision-making and creative work is the human part that can’t get automated
      • More people in the middle of the enterprise are becoming analysts – BI tools aren’t going to be enough
    • Types of tools should be operational tools that give answers to questions that they need immediately
      • Where is the bottleneck in the funnel? How to eliminate?
      • Competitor is having a flash sale – how much revenue is impacted or what segment should I target?
      • Generally, analysts would have to spend time and $ to get an answer (“$10mil to get a report that you didn’t need in the first place.”)
      • A/B test has to be continually monitored
    • Jad worked at AppDynamics – one of easiest things to sell is Performance Monitoring Tools – prevent systems from going down
      • Harder to prove ROI to other orgs – sales, marketing if they need continual results / ongoing
      • Want to have self-service tools vs full-service from someone else
      • Not analysts but instead the functional operational people – marketer, growth hacker, product manager, business people
    • AirBnB already open sourced SuperSet – ad-hoc access to data for results, used by 100s co’s – presentation layer product toward technical
      • Imply (one of his investments) for analytics and processing layer – store streaming data into database and do the analytics / presentations
      • DataBricks – processing layer
      • ETL layer is the one that has not gotten traction – domain specificity (healthcare vs ride-sharing or finance)
        • Currently too much integration issues and organizing
    • 3 categories – operational intelligence – sell tools for incumbents to enable intelligence
      • Target csm or sales or product manager (crowded currently, hardest to win)
      • Segment-focused vendors – sensors and analytics to oil & gas companies, for instance
        • Vertical solutions for industry
      • Vertically-integrated, operational intelligent company that competes against incumbents – Lyft / Uber, AirBnb, etc…
        • Biggest value but hardest
    • Non-IT buyers: Grocery, Construction, Oil & Gas – operationally efficient and commoditized as long-standing business
      • Minimal change in efficiency can be a huge value (Costco at $12.5bn ’17 on 11% margin)
      • Capital deploy for Exxon Mobile ($230bn capital invested, ROIC 9.5%)
    • Particularly excited by SuperSet, Imply – infrastructure tools – people seeing analytics and tools as necessary for business
      • Software vendors into large, existing industries – hardest would be economic profiles will be very different
      • Selling into stagnant markets (minimal margin) and not used to new tech – cycles will be long
        • Huge businesses to get in
      • Need to educate/prep investors – really bright light at end of tunnel
      • Need to become experts and trusted advisors in the domain
      • Help with software and services in the industries
  • Josh Wolfe (@joshwolfe), founder/MP at Lux Capital – Tech Imperative (Invest Like Best, 4/23/2019)
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    • Tackling massive scale problems – China as infrastructure power vs the states
      • State or story-sponsored role becomes more powerful with internet-enabling
    • Checklist of 5 main things (Xander of GoPro, now SurveyMonkey)
      1. Nail down the strategy of company – what are you going to do?
      2. Deliver capital to pursue strategy – clear, cohesive and sell
      3. Brilliant team to execute, drop others to start mission.
      4. Communicate the hell out of it – partners, competitors, media, press – keep consistent answer.
      5. Hold people accountable – if people aren’t and the goals aren’t clear, not effective organization.
    • Story – memorable, easy to repeat, conveys meaning in a clever way
      • Want to elicit an emotional reaction – putting meaning in a story for an individual
      • Portable ideas as superpowers – leaders being able to harness this, or the audience (maybe of the shared values)
        • How to aggregate the ideas
    • Abundance of liquidity to illiquidity or leverage (eg $200mln check in growth-equity round at $1bn (from $100mln) but if down-round, then the check has a big stake in it as creditors)
      • LPs and endowments are overextended – he’s telling people to look at secondaries, not venture
      • Sequoia was appealing to greed – sop it up and have to write bigger and bigger checks (get a big fund and put to work)
        • SoftBank as big problem pricing up rounds – either visionaries or producing paper assets as collateral against debt
        • Tesla as horrible balance sheet and illiquidity
    • Zoom doesn’t need to need a big business, but Uber/Lyft depends on strangers and investors to buy in to future
    • TurboChef (fast like a microwave but toasty like toaster) – Subway vs Quizno for $4k ovens
      • Sell to Subway – 20k places for purchase orders – but they got Coca Cola to buy the contracts for Subway in exchange for them to be in the stores
      • LatchAccess (one of his co’s) – remote by cloud from phone to consumer
        • New build and buildings (now 1 in 10) – did contract with WalMart / Jet
    • Some firms get lucky and parlay it into success – maybe wrong in process
      • What was process? Where did you get lucky? Where were you smart? How did you structure deal?
        • Benefit you, founders, investors
      • Price vs intrinsic value – public doesn’t do this, but path-dependent in portfolio (repeat entrepreneurs)
        • Team vs sole GPs – total equal partnerships and all mixes
        • Portfolio mix, super early stage, low probability of high financing risk
        • Others who are good at metrics / business, growth metrics
        • Subsector – fintech, crypto, etc… as experts
    • Tribes with a mantra
      • “Life sucks” – gangs, people homeless
      • “My life sucks” – 9-5 and get home and just crack a beer and grow for that
      • Like what they do – “I’m great, you’re not” – silo information, zero-sum and leave as free agents
      • Lux as “We’re great, they’re not” – robbers cave – how to get people to bond vs competitor / enemy
        • Sometimes it’s an entity – exogenous threat, devil – big oil, martians
      • Ultimate “Life is great” – mission driven, maybe Google / Facebook initially – cause/effect of money
        • Still climbing mountain, goal to reach – complacency maybe
    • Judgment: should we be disciplined about price?
      • Andreesen said only 10 good companies but you want to be in each one – but there are 1000s of decisions to be made
        • Pay any price for the ‘best’ or be discriminated – lead to FOMO and price action
        • Mentioned Cruz and setting up GM deal ($20mil at $60post vs $20mil at $80post, but GM came in and paid 11x)
      • In private markets, if you rejected them, you don’t get another chance.
    • Values: observable around morality (tech around morality and morality around tech)
      • Existence of an option is a good thing – military as a hot topic, tech as both sides affected
        • Had invested in Palantir offshoot for virtual wall for Homeland – has lots of immigrants who were deeply affected
      • Drone options or even autonomous driving (say, those who die as organ donors for the donor list)
      • Compares China’s pipeline from government to technology – decisive advantage will let them be ascendant
        • Moral discussions slow this down – barriers to experimentation
      • Real value of CRISPR isn’t the feature, but what it leads to in the platforms (ex: X-Men / Cerebro – Variant for rare populations)
        • 23andMe and Ancestry as targeting the ‘boring populations’ vs what they’re doing
          • 1000 individuals for rare conditions that have a metabolic rate that raises in the evening – what if this was monogenic / targetable?
    • Sci-fi vs Sci-fact as narrowing — ‘it will rot your brain’ as doing the next $10bn+ industry
      • Mentions engineers and Fred Moul (founder of Intuitive Surgical) starting Orace – just betting on him to recruit the right people
        • $8mn at $20mln valuation – for 5 years $90mln forecast and $450mln – then got a bunch of investment)
          • Exit for 63x for $6bn to J&J – completely flawed process on an order of magnitude
    • Directional arrows of progress if spotted increases probability of success on subsector
      • Lighting: burning flame -> bulb -> led; memory, energy density
      • Talked about Calliopa – he wanted to focus on gut-brain access – taste / sugar receptors (Charles as Chilean professor at Columbia)
        • Half-life of tech: 50 years ago, 25 years ago personal computer, 12.5 years ago laptop, 6.25 years phone, 3.5 iwatch, 1.5 airpods
          • More intimate over half-life and improved
        • Had to meet “Rearden” – “I can get rid of that” – Bill Gates’ right hand guy, polymath, PhD neuroscience after undergrad as Classics/Latin
          • Put on wrist strap that could detect 15k neurons that innervate the 15 muscles in your hand – perfectly model this
            • Can control it just by thinking of turning on whatever you’re speaking of
          • We don’t have input problem – we have output problem — too linear
            • Series A and Google/Amazon invested $30mln – want to sell after maximum value
      • Do you find companies that touch near the directional arrows?
        • Don’t need to implant in brain, can read the neurons – 5 years ago you didn’t have everything that was required – power, IoT
    • Moral imperative to invent technology, instruments to invent genius – encounter the technology that eventually inspires others
      • Losing touch with humanity – where is the song after sung? Find way to reduce human suffering.
    • Are there enough entrepreneurs in real technology frontiers? Is vs ought (jokes about competition)?
    • If you can spot “What sucks?” – can you discover something “Wait, what?”
      • 100mln mice – can’t you put sensors/automation for this?
      • Document storage (Mushroom vs atomic storage, not REIT for storing docs) – banker data, scan them – IronMountain can’t do it
      • Entropy information – he gets more optionality by giving information, but death of privacy is coming with convenience
        • Mentioned graphic novelist “Why the Last Man?”, side one called “The Private Eye” about everyone being surveilled – wearing masks
        • Socially and personal privacy is a losing battle but industrial side makes sense
        • Mentions blockchain for voracity – Banksy for private store (analog), authenticity
    • Special operations spending time for 2 weeks – Asia: Philippines, Thailand, Malaysia, Singapore, Japan
      • Coalitions forces, training, sniper, subsea, Seals, cutting edge tech – able to look at things for laser targeting
        • He was there for “What sucks?” – humbled by voracity, proud by the intelligence and what he could do and who he was with
      • Optical signals for those that get through program are the opposite of the big guys – stunning, talented, quietness “stoic intensity”
  • Ayan Mitra, Founder, CEO at CODE Investing (formerly Crowdbnk) (20min VC 089)
    webp.net-resizeimage-16-640x321

    • Enterprise architect and tech mgr, worked with M&S, Orange, and First Direct
    • Software eng by trade, started in mid 1990s and built internet framing for Bank Offers Direct
    • Was in NY when Kickstarter kicked off in 2010, and saw the regulation was ready for this type of investing
      • Made the concept popular, regulated funding, or Kiva-type – early stage investing is a lot more popular in Europe/UK
      • JOBS Act as regulation freedom for positive step for alternative financing
    • Wave of changes where technology is being brought on the systems and the benefit goes to the investors and markets
      • Quick and transparent – believes it would’ve happened regardless
    • Crowdbnk – reactively do due diligence, price and valuations – invest alongside with investors on their platform
      • Look to raise growth capital for equity and debt – not a pure platform/marketplace
      • Minimum / maximum – equity looking for $500k – 2mln pounds, debt – secured/asset-backed $1ml – $5mil
        • Investors – $10k pounds a year to be diversified and properly investing
    • Valuation class by Ashwin (NYC) – intrinsic valuation (creating, discounted by time and risk) or momentum valuations (price willing to pay)
      • VC could benefit from diversifying investment base – early round by Index recently
    • In crowdfunding, consumer brands may have an easier time going down crowdfunding pick
      • Harder for others to understand some of other sectors / SaaS, for instance
    • Debt funding is #168bn and growing, but small compared to financial services
    • Drawing attention as a focus over time, consumer behavior changes
      • By being more efficient, they can return value to investors and people on the platform
    • Book mentioned: Intelligent Investor – Ben Graham
      • Seth Godin’s blog
    • War chest vs planned capital injections – not a binary answer (eg: compete against Uber – good luck without war chest; tech-enabled services)
    • Funded a company called Breezy – simplifies user interface for older generation, potentially – team/value and invested by US VC’s
  • Andrew Hohns, President, CEO of Mariner Infrastructure Investment Management (Wharton XM)
    • Conceptualized and founded IIFC Strategy as part of his dissertation at Penn
      • Funding gap in project finance to address world’s infrastructure needs
        • Talked about growing projects in Africa, India and others
    • Started a fund as he finished school – raised $500mln for capital projects
      • Including a $1bn transaction with African Development Bank completed with multilateral bank and private investors
        • Provided approx $650mln in additional lending capacity
      • Credit Agricole in 2017 that was “biggest impact investing deal yet” by Financial Times to allow an extra $2bn of funding toward green projects
    • Managing the originations networks for funds with relationships with many global financial institutions

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

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

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

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

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

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

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

Hope you enjoy!

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

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

 

 

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

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

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

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

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

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

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

  • Validating D/S with QuantHub, Matt Cowell CEO (BDB 4/2/19)
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    • Also with Nathan Black, Chief Data Scientist at QuantHub
    • Talking data science – math with business and IT skillsets
    • Companies are manually doing tech assessments with candidates / roles – programmer-based primarily
      • QuantHub looks at a comprehensive, scientific approach to assessment of the stack of what may be necessary
    • NLP of resume, and then Bayes’ updating for input and results from that
      • Assessment platform at the core and using it for hiring (natural use-case) but then benchmarking organizational skills
      • Aggregators of content and matchmaking (say, data analyst up to data engineer – wrangling, SQL improvement)
    • Assessments done and individuals won’t be charged – overall value in helping talent
      • Building the training side in the next quarter
    • How do companies engage QuantHub? 5min to get running – align the incentives for using it / relationship.
      • What are the challenges? What are the skillsets? What do you mean that you want them to do?
      • Requirements changing by different statistical methods (along with computing power, designing algorithms vs latest research)
      • Knowing/vetting data scientists as having to do the role / job – can you mirror the actual job requirements? (try vs buy, potentially)
    • Cloud computing or hardware innovation as ‘cool’ in a world of software – highly critical, depending on certain organizations
      • Some orgs NEED the data improvement there (Kubernetes, Docker, Cloud, Spark vs Power Excel user)
    • Matt as a product strategy guy – book “Monetizing Innovation”
      • How do you determine what the market and customers want
    • Nathan’s book – “Make It Stick” on how you can improve learning methods
  • James Martin (Staffing Lead, D/S at Google) – Getting Noticed in D/S (Kaggle CareerCon 4/17/2019)
    google-cloud-platform-for-data-science-teams-4-638

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

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

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

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

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

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

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

Hope you enjoy! Leave a comment or follow along!

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

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

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

    • Sustainability at Feast (prior company)
    • Using Feast as test-tasters for EveryTable menu / offerings
    • EveryTable as sustainable, healthy food for people in an affordable way
      • Restaurants with partnerships of cities/areas that match the pricing (Santa Monica different than Watt or Compton)
      • Can order on app and go pick up meal for < $8 – able to do this with scale – try to ensure this early
    • Rolling out BlueApron-style weekly meals at the same price as in store
    • Corporate offerings where they have EveryTable coolers / fridges that take a credit card / payment and can pull out your order
  • Brian Linton, United by Blue founder CEO (Wharton XM)
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    • Originally moved from Singapore / Asia, went to college in Michigan – boredom satisfaction with sales
      • Started with ‘guady’ jewelry that was travel-related (tourist-style jades, emeralds, etc…) that he would source from home
      • Travel down to Florida / other areas and sell to region
    • Believed in doing good, so he would donate ~5% of all proceeds to ocean conservation – realized this wasn’t sustainable
      • Random donations of $1000s or %’s
    • Finally started United by Blue to develop the sustainable business model and what he believed in
  • Right Way to Get Your First 1000 Customers with Thales Teixeira (@thalesHBS), associate professor at HBS (HBR IdeaCast #676, 4/2/2019)
    • Startups failing because they try to emulate successful disruptive biz and scale instead of learning about initial customers
    • First customers are more than the money, word-of-mouth, R&D and free feedback
    • Etsy, Amazon, Netflix, Uber had no new technology (just finally had the map to see if there were cars coming)
      • Etsy went to craft fairs to recruit sellers, who then attracted more buyers
      • Pinterest tried to create a culture initially to set the tone for quality
      • AirBnb was awful initially in NY, so the founders wanted to find out – places were great but pictures were awful
        • Rented a nice camera and offered to take the pictures to improve the ones on the listings
    • What is the primary driver of value to the customers to deliver? How does technology play a role in this?
      • AirBnb had 1 engineer (founder) for a long time – increase the utilization of an expensive asset
        • Hid the options initially – didn’t have much inventory so they would email / find out and then get back to customers
        • Show availability – needed to stay in a house in the places
    • Technology is the enzyme / enabler of the start-up or experience and acquire the customers to purchase the product
      • People that like smaller companies, try new things, explore products and tell them
    • Unlocking the Customer Value Chain (Thales’ book)
  • Critical Thinking in D/S – Debbie Berebichez (@debbieberebichez) (DataFramed #58, 3/25/19)
    • Debbie is a physicist, TB host and CDS at Metis in NY (first Mexican woman to get a PhD in Physics from Stanford)
      • Promoting women in STEM, especially hispanic women
    • Metis is a data science teaching company as an arm of Kaplan in NY, San Francisco, Seattle
    • Did 2 postdocs around Columbia before going to Wall Street to work as a quant – but money wasn’t the only motivator, so she left
    • At Cambridge, she remembered speaking about Astronomy 101 as her first intro to physics class – was on 2 years of scholarship
      • She took a walk with her friend Rupesh and said that she was crying – “I just don’t want to die without trying physics.”
      • Passion drew attention and professors – offered her for a 2 year physics degree (skip first 2 if she could pass a test with complicated derivatives)
        • Had 2+ months to learn calculus, basics to mechanics and more – passed her test (9am to 9pm)
    • Mentioned going into high school to discuss data science – class was doing coding/SQL/data look on animals
      • Had 1 group that was looking over turtles – couldn’t answer the units for weight (triple digits) – not lbs, but grams
      • How this made sense – how to piece together reasoning / bias – how needed this skill was
      • Not bothering to check outliers or some data was exhibiting – why do we do it all?
      • Danish astronomer built and designed 1000 stars, which wasn’t much, but Newton and Kepler, Copernicus all derived theories from
    • Large datasets vs small datasets – insight more important vs size (big data as sometimes unnecessary)
    • Feynman quote about fooling ourselves – bias that we create.
    • History of Statistics – Stiegler, normal distribution and derivation of central limit theorem by Gauss and Laplace (1809 with Jupiter’s motion around sun)
    • With her bootcamp – she wants to attack the question of using the right algorithm and how to analyze the problems at hand
      • How to choose a data project in what you’re interested in – madewithmetis on Metis site
    •  Singular value decomposition (SVD) and reducing dimensionality, worked with Genentech founder – healthy DNA vs patient’s DNA and cancer
      • Reducing dimensions to the ones that were most relevant – NLP also
    • Think deeply, be bold, help others – Grace Hopper celebration talk
  • Dean Oliver (@deano_lytics), Data Analytics (Wharton Moneyball)
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    • Talking about how far behind NFL is behind NBA in tracking
    • There are people doing video for football, but not much – not widespread
      • Position groups will gain entirely different/new insights into how they’re playing
  • Cordasco Financial Network Planning + Sri Thiruvadanthal (Behind the Markets, Jeremy Schwarz)
    • Discussion of hedging dollar vs not – if hedging, probably wise to diversify with global
      • If not hedging, then europe may not be as great
    • Current markets say that liquidity isn’t as high with central banks, stocks start to couple and lose diversification / value
      • Decoupling early on in cycles
    • Relative value may be fine but not absolute for the dollar compared to other currencies
  • Jeppe Zink, GP at Northzone (20min VC 087)
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    • Invested in Spotify, Bloglovin, TrustPilot with focus on SaaS, fintech, mobile
    • Worked at Deutsche Bank as analyst in corporate finance, tech banker – left with 90% of team
      • Convince bank by buying principal investments before IPO in late 1990s – worked out
    • European cycles of tech – 100mln to 3bn people online, digital increase and telecom infrastructure
      • First VC firms in existence were doing integrated buyout model, which failed initially – too transaction focus
      • VCs have the talent that’s aligned with the founders now – 90% of VC firms that existed in 2000 had died in 2002
    • 10 year cycles where the great companies withstand, others don’t
    • Stage agnostic for them, series A to D rounds
      • Nordic companies of unicorns for what he has had success with
      • Europe as dropping trade barriers initially and in the 90s, broadband and smart phone starts (Nokia, Ericsson)
    • Has offices in the north for Northzone but he makes it up every other week or so
    • Try to emulate the start-up and have hunger/ambition always
      • Not trying to stagnate – venture capital vs patient (he thinks impatient is better – learn through failure and testing)
      • How fast can you learn to level up and deliver the best product? Continuous measurements, KPIs.
      • For Jeppe – momentum in product development
    • Most intrigued by fintech investing – Peter Thiel as one of his favorites
      • Most recent company was CrossLend – consumer lending with European bank lending
      • Book: Startup Growth Engines as collection of random founders and interviews
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