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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)
    wepay-1

    • Leading provider of integrated payments for software platforms, raised $75mil from SV Angel, Highland Capital, Ignition Partners, August Cap
      • Founders of YouTube and PayPal also in
    • Grew up in NJ, spent time in NY and father worked in Air Force and construction – taught himself computers in 80s
      • Received a scholarship to go to BC, met his co-founder for WePay waiting for the flight for the interview 6 years prior
      • Went to do IB at Jeffray’s – advising tech and software companies with clients, passionate and building for a year to quit
    • Installed a suit rack in his car because he wasn’t going home – long hours, brutal fundraising
    • Group payments that they saw repeatedly at the age of 22 – big market for payments, testing it out
      • Wouldn’t have less responsibilities than at that time – Rich deferred law school and Bill had worked on it full time
      • Tried to pitch Boston investors and failed – less receptive to early stage investing, applied to YC instead
        • Came out to the valley for an interview
    • Spent 1.5 year to invest and took money and sold furniture and drove to the west, taking turns
      • Product was conceptual, pitch deck was opinion and it was hard to prove a market need to investors – conceptual idea
      • In YC, built product by talking to fraternity treasurers at SJSU, ski club coordinators – got them using the product
        • Went to talk to investors by showing them the traction
      • Why would a treasurer to accept payments with different product? Host bbq and invite them over. Go to dorm room and watch product usage.
        • Responsive to requests – take feedback and be better than existing solutions. Gain knowledge in start by doing things not scaling.
    • Group payments were a big problem and needed a solution – weren’t willing to pay, or pay transaction fees
      • Venmo had raised money and had a bunch of momentum by giving away services for free
      • Competitors were taking advantage, 2 years after YC – pivoted but weren’t growing as fast
        • Built an events tool, donation, invoicing tool and an API for customer use – other companies were just doing those
      • Realized they could build an API making payments experience easy and simple and let competitors do whatever
        • Saw huge traction/benefit where they could be brought in via the API (since they had raised $30mln)
        • Needed the business to be grown but expectations were higher
    • 600 lb block of ice for marketing $500 in front of PayPal Dev Conf at Moscone Center – still highest market day
      • Since PayPal had a knack for freezing people’s accounts randomly
    • Pivoted to shut off 70% revenue stream from consumer product, gaining growth on API from other customers
      • GoFundMe used them as a payments processor from when they were 2 person company
    • Prior to acquisition by JPMC – 200 employees at that time, now fintech / bank
      • Asset purchase agreement day – tired – was negotiating final points of deal in person, had some drinks to celebrate
      • Bought a cabin in Mendocino County – deal was valued at $400mln
    • Part-time partner at YC now – helping companies in general – relevant to the next entrepreneurs and the scale
    • Angel investing on the side – much longer and harder and scarier than he ever would’ve imagined
      • Reinforces this to his younger self – startup doesn’t fail unless you give up
  • Evolving Narratives in the Crypto Space with Andreas M. Antonopoulos (FYI 3/12/19)
    • With Arjun Balaji, as well — and similar for me as host, his intro to Crypto space video YT
    • Conflict of Crypto Visions article by Arjun and host
      • Identified closely with unconstrained vision and doing talks on not playing zero-sum mentality
      • Ethereum as different than Bitcoin – evolving directed by design choices
    • Engineering consists of design tradeoffs – choices of optimizing and de-optimizing parts of systems
    • If you want to make something that is Bitcoin-ish, you run into problems for all the strengths that are already inherent to Bitcoin network
      • Differentiate enough to be a new thing from Bitcoin – can’t mingle or occupy that niche
      • Is privacy a big enough differentiator to separate from Bitcoin network?
        • Strong privacy in base layer – can end up with inflation bugs that can damage sound money policy of Bitcoin for the privacy
      • Sound money vs private money – not clear yet.
    • Hard money displaces other forms of money in long term but only if they’re maximalists and logical
    • Friction levels determining switching back and forth on a wallet between utility or store of value tokens / coins in the future
      • Automated backend where they are optimized
    • Interest in Ethereum – tradeoff worth making for smart contracts and applications that aren’t just money outside of Bitcoin
      • How the technology of VM blockchains work
      • Scaling is harder in Ethereum – proof of stake has different security model than proof of work
      • Sharding, beacon chain, polka dot – not sure if it will work or what the security constraints are – could have applicability to BTC
    • Bitcoin critics – make the case for it but then explain value proposition or store of value
      • He has an opinion, others have opinions – none will determine how the market develops
      • Arguing is a waste of time. If you understand the tool that’s best for a job, you’re a better user of tools.
        • Which is the correct tool and how to use it properly – perception is limiting in general
  • Sam Yagan, CEO of ShopRunner (Wharton XM)
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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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Fun Founder Stories (Notes from July 29 – Aug 4, 2019) August 21, 2019

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

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

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

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

  • Neuralink & Brain Interface (a16z 7/21/19, 16min on the News)
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    • With Vijay, Connie Chan, JPM
    • Announcement of neural lace – culture sci-fi by Ian Banks – processor & sewing machine
    • Non-invasive vs invasive (femoral artery all the way up to the brain)
      • LASIK as invasive / dangerous (still even, but now much better, accepted)
    • Announcing in rats and in monkeys now (surprising his president)
    • TikTok as 3rd most dl app behind WhatsApp and FB Messenger, 1.2bln MAUs – having huge influence at VidCon
      • Sponsored by YouTube but TikTok had a large presence, the ban in India
      • Short, 15sec videos – 1 hit piece can trigger enough people
    • How would they make money? – ecommerce, restaurants, retail – short videos for ads/commercials
    • FaceApp – probably nothing to worry about – unless high profiled public official, NatSec Space, leverage
      • Someone getting negative information or leakage – accusations of the country in general is silly
      • Countries consider privacy differently – in the US, convenience / UX will trump privacy for 15min of joy
        • Europeans, Germans, Italians for instance are more private
    • iHeartRadio announcing direct listing – before, emerging from bankruptcy or spinning off
      • Repurposed after Spotify / Pandora
  • Mobile malware and Bipartisan drug pricing (a16z 7/28/19, 16min on the News)
    • With Martin Casado, Jorge Conde, Jay Rughani
    • Monacle as mobile malware – March 2016 Android-based application
      • In security, netsec and endpoints – protecting desktops, for instance
      • Attacks phone with 2FA, even, and less secure
      • Can take calendar event, account info and app messages, reset PINs
    • Drug pricing – Medicare Modernization Act – why can’t Medicare use its purchasing power to negotiate medicine prices?
      • Part D – Medicare covering prescription prices, prevents HFS from negotiating any part of the value chain
      • Price of insulin where they get price hikes – new therapy gets $2mln for cures (R&D) differences, conflation
      • Price of successful drugs have to make money for drug and all of the failures
        • Counterargument – US subsidizes R&D for the world
        • Complex industry structure: manufacturers, distributors paid to move drugs through channel
          • Pharmacy benefit manager – who is eligible, who’s not – what are drugs for conditions and prescriptions
            • Helps insurers who gets the drugs – takes an economics layer
          • Insurers reduction drug spends, for $1 spent, manufacturer gets a small %
      • Dropping from $8k to $3100 out of pocket
        • Cap by tying to inflation (for growth) or annual price increases
        • May start higher prices because you can’t increase it much
    • Chain is not transparent, but also complex – tech can have an impact but needs help from policy to drive out some inefficiencies
      • Free market works if there’s transparency – what is a medicine and can you make it fair enough for everyone
      • Current system is not set up for the new medicines (extending life from 10 years to a cure)
  • Richard Hanson, CEO & cofounder of HiringScreen (20min VC FF028)
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    • Founded in Hong Kong in 2015
    • Studied law in Cambridge, did 11 years recruitment consultancy in London before moving to Hong Kong
      • Then created his own recruitment firm – had his own looking at 196 cv’s for an EA for someone
      • Score, sort and select candidates
    • Tech advances in recruiting industry – job boards and sourcing is at all-time highs
      • Barrier to application is all-time low but have too many to look for (especially manually)
      • Psychometric and phone facility stuff to find relevant candidates – get on with themselves
        • Go through rest of funnel to invest in the process in more efficient manner
    • Had always wanted to live in Asia – pretty exciting, bullish for Asia in general
      • Hong Kong, Singapore, Japan as hubs
    • If you have an idea, try to find someone or go ahead and do a view of what it may be executed on
      • He had the idea, went to his cofounder Luke (better at project management side)
      • Prototyping mockups and getting through the first steps efficiently – may hit a dead-end a few weeks in
        • Validating idea as soon as possible – customer or problems for people (heads of recruitment firms for his problem)
    • Making an effort to code or understand a bit of the UX (in his case, CSS and HTML to understand a bit)
      • Compared to languages in a foreign country
      • When his CTO introduces people, he wants to be confident about what the developer has been doing and understanding their past
      • His responsibility to show an effort/commitment in the job role
    • Looking to raise a round – HiringScreen did it in 8 weeks
      • Competitive slides, why you want to raise, how to convey mission statement, skill and productivity gaps
      • Understanding his potential investors, as well
    • Accelerators – choosing the right ones? He’s with the Blueprint Accelerator by Swire properties
      • B2B focus, no equity in startups – working space and Swire network of companies (conglomerate of different co’s in verticals)
      • Sponsored him and tried to help advance the company by talking to other HR talks
      • Mentions Brinc as hardware accelerator near the top
    • Idea of equity early on would depend on your assessment of what the startup needs?
      • Super low cost – accelerator with working space?
      • Product but proven use case – Blueprint to trial product and test it
      • Balance the need with the equity they’re taking
    • The Alliance book by Reid Hoffman for looking at employee and employer workplace, tour of duty principle
    • Brad Feld and Jason Calacanis’s blogs, Reid Hoffman as the most admirable founder – better people to take LinkedIn on
  • Jennifer Golbeck, College of Information Studies and Affiliate Professor at UMD
    • Talking about social media research, truth and justice
  • Carl Ericson, CEO & cofounder of Atomic Object (Wharton XM, Mind Your Business)
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    • Grand Rapids, Ann Arbor software product development company and why he chose there
    • Sails at Grand Rapids Yacht Club
  • Bianca Gates, Marisa Sharkey, Birdies co-founders (Wharton XM)
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    • Discussing how they started them and Feb 14 – when she landed an article with a SF Chronicle fashion correspondent at a dinner party
    • Driving up to the other in order to get all 2000 orders packaged and sent out

 

 

 

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

    • Doing a 6 month project after Seattle in Turkey – turned into 2 years as the only non-Turk
      • Grew an affinity for the people, culture, food and trends – girlfriend’s grandma at the time gifted him a pair of handmade shoes
    • Returned to NY and beat the crap out of the shoes – wanted another
      • Reached out to the maker (current partner) and bought another pair
      • Ended up getting 5-6 in different colors, customized without the flip – people said they were awesome
      • Ordered 300 – could get 150+ and did a party to showcase them with cocktails, enjoyed hosting
        • Got 30-40 orders on the first night, decided to do it for the rest of the summer “Sabah Saturday/Sundays”
    • Realized it could be a business after in the summer he was making more from shoe sales than his NY P/E job
    • Expanding from 3-4 employees to 40 and expanding from a home to a warehouse – border of Syria/Turkey
      • Has a few key employees that are Syrian refugees – part of the brand and they showcase it on the site
        • Not branding directly, but definitely part of the story
  • Goldie Chan (@goldiechan), digital marketing expert of LinkedIn and actor (Wharton XM)
    • Discussing quitting her job and making a fake company while unemployed
      • Turned into a marketing consulting gig – had a few clients, had to create a company
    • Now doing talks and discussions
  • Kurt Seidensticker, CEO of Vital Protein (Wharton XM)
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    • Collagen and explaining to people how it was – getting some in to Whole Foods through them asking
    • Didn’t hit him until he was in Italy and 2 random women at a café pulled their Vital out
    • Did about 10 companies, 2 succeeded enough to pay for kids college and allow him the freedom
      • Was doing Vital during another company until it surpassed the other
  • Fortnite, esports, Gaming (a16z, 16min on the News)
    • 2 million concurrent livestreaming – not as big as GoT, for instance
    • With Andrew Chen, Darcy Cooligan (investing team on consumer)
    • Bigger prize pool for Dota 2, $3mil for Bugha’s win was larger than Tiger’s Masters victory
    • 10 years for Riot and League – still grossing billion, WoW / Runescape
    • Billions of video consumption between Twitch, YT (and now Microsoft Mixer)
    • iPad can play Fortnite pretty well, for instance – massive multiplayer opportunities
      • Instagram and this generation for coming together as people – Minecraft/Fortnite
      • Gaming and cultural zeitgeist to hang out with friends
    • Sonal did a fight with editorial desk and had seen it for a profiling in 2013 – argued it was similar to sports
      • Big business and much of the same thing – management company, played 2+ years for 6-8 hours, sponsors, fans
      • Performance entertainment and personality-based
        • Comparative for game shows – other people answering trivia, reality tv
    • Strong incentives to keep games going – user-generated content
      • Established player leading way to user-generated thereafter
      • For Fortnite, building levels (similar to mods and mod community in Minecraft and Roblox)
    • Games stadia for esports and digital dualism (in real life compared to virtual – game is the bridge)
      • Malls building areas for this part
  • Chris Tsakalakis, CEO of Vivino (Bay Area Ventures, Wharton XM)
    aws_vivino_logo_600x400.cb594b3d79815eece9e8c685a7b8d043b7910b95

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

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

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

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

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

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

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

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

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

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

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

 

 

  • Sitar Teli (@sitar), MP at Connect Ventures (20min VC 12/30/15)
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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)
    open-graph-default

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

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

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

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

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

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

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

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

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

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

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

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

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

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

 

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

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

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

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

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

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

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

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

 

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

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

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

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

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

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

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

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

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

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

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

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

    • Discussion of getting data from Netflix / Amazon / Hulu / tv to better match brands and advertising
      • Dirty data via a wharton grad who set up a survey style
      • Cohorts and demographics, along with psychographics
    • After getting data, attempting to approach Youtubers / social media influencers, tv spots and channels or shows to get their brands in front of the right people
      • More pointed, depending on what interests are for their cohorts
      • Creative storytelling as the change of cultural mind shift has increased
  • Understanding the Space Economy, Sinead O’Sullivan (@sineados1), entrepreneur fellow at HBS (HBR IdeaCast #684, 5/28/19)
    • Facebook, Amazon (3000), SpaceX (12,000) and other funding like Blue Origin / SpaceX / asteroid mining or travel
    • Global space economy as $1tn by 20 years – currently $325bn so it would need to 3x
      • Breaking apart space resources and otherwise – earth-focused (delivering or existing in space that helps earth)
        • Exploration or creating interplanetary existence
    • Running out of space in space for satellites – comparing to airplane docking / loading
      • $2500 per kg now to launch, used to be $50k / kg
    • Reliance had been on unilateral agreement for space policy – one tech startup launched a satellite that didn’t have permission (but no fall-out)
      • Food / grocery stores, wifi, phone, insurance pricing due to satellite data – reliance on services are increasing as the market increases
      • Thinks that we’re close to seeing the cheapest cost of launching – cites SpaceX, but won’t allow everyone to participate
    • Ultrahigh accuracy will require higher powered satellites – GPS, nonmilitary grade is ~0.5 m – thinks it will prevent autonomous vehicles solution
    • Ton of money going into asteroid mining but thinks it’s better for testing missions to Mars and figuring out the problems for future
      • Looking at Uber at start and say “people won’t get into a stranger’s car” or other cases as how we see the future – going to Mars, etc
    • Earth-focused space technology – 100+ launched satellite start-ups, micronano satellites, relay companies, downstream analytics
      • More touchpoints for everything in this manner
      • SpaceX will increase public and government intervention and within 50 years, maybe see a human launched there
  • Investing w Twitter Sentiment, Andy Swan (@andyswan), LikeFolio (Standard Deviations, 4/25/19)
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    • 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)
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    • Sits on boards of Top10, ROLI, Lovecrafts, TrademarkNow etc
    • Was 5 year partner at Wellington Partners and invested in EyeEm, Hailo, Yplan, Bookatable, SumAll, Readmill (sold to DropBox), Qype (sold to Yelp)
    • Break in 1999 by getting to work at Yahoo for about 10 years before getting to venture in 2009
    • Applied Math background, tend to understand complexity and appreciate solutions in technology – different experience for entrepreneurs
      • Learned to be less metric-driven – less obsessed on numbers and data, more human and market dynamics
    • Operators vs career VCs – successful firms take different skills at different times
      • Exit, raising money, portfolio management, scaling, growing
      • Important to watch how a business is grown, whether it’s yourself or by being a long-term investor or close in other ways
    • Entrepreneurship as a career path and as a global one, investors in similar mindset and ambition – can take it anywhere
    • More seed capital and smaller checks than later in Europe at this time – maybe more competition higher stages
    • Thinks that consumerizing SMB software has a lot of room to grow, as well as enterprise software – easier UI-driven tools
      • Developments in AI – mentioned real-time trademark analysis (TradeMarkNow, for instance)
      • How to give great recommendations after gathering consumer tastes and insights
    • Book: The Brain That Teaches Itself – neuroplasticity of the brain
    • The first $10mln you invest, you’ll lose – advice
    • Overhyped sector: food delivery; Neglected: apply behavioral science to tech problems
    • Favorite blog/sector: longs for better content – thought TC was great when it was started, but now it’s Medium individual articles
    • Most recent investment Curious.ai – made it in 40 minutes & ambition was compelling
  • Geoffrey Batt (@geoffreysbatt), Nature of Transformational Returns (Invest Like the Best, 4/9/19)
    • Iraqi equities – history of asset classes
      • Time and patience is a big factor for how you look for results
      • Japan, for instance, 1950 – 1989 100,000% return but now is still below the peak from 1980
    • May have a decade of not working out return-wise
      • Nothing to show for investments, incredibly challenging, especially now – hedge funds or LPs
        • Reporting weekly or daily basis, long-term investment could be 6 months or 1 year – pulling money after first issue
      • Hard to ride out periods of long-term returns (think: re-rating countries)
    • LPs as agreeing to the time horizon – maybe investment committees that are making decisions on career-risk for institutions
      • Iraq to one of those – not likely to get paid off if it works, but if it doesn’t, get demoted or fired
      • Had to approach HNW and family funds – adjacency risk for people next to seed investors (if one is weighted on fund)
    • As student, he majored in psychology – said there was a guy in the seminar class that was a unique thinker
      • Daniel Cloud had co-founded a fund that invested in post-Soviet Russia before doing his PhD
      • Asked Geoffrey to come work for him and learn investment markets
    • Wanted to find next big thing – first, find a place that everyone thinks is awful (in 2007, this was Iraq)
      • Does perception meet reality? How many people dying in the war every month?
      • Country portrayed as “failed state” but oil production was increasing, CPI was 75% yoy but now 5-10%
        • Currency appreciating against the dollar, now civilian casualties in a month were down to 200 from thousands
      • Normal visit to Baghdad – mundane here, out-of-place was that 2 guys in middle of afternoon were playing tennis, kicking soccer
        • Visit companies, stock exchange, meet executives, go to a restaurant, how easy is it to hail a cab, get around
        • Critical process of infrastructure – are people paying in dollars (bad sign), local currency?
    • News media is just not set up to convey complexity to the audience – alienate, progressivism as a service, readers change
      • Experts don’t have skin in the game so they don’t face career risk – betrayal if you suggest otherwise, likely (even if true)
    • On his first trip, mentions Berkshire – early 1990s where they first invest in Wells Fargo
      • Managerial skill in banking is paramount if levered 20:1 (make 5% mistake and you’re done)
        • Don’t want average banks at great prices – want great banks at slightly unreasonable prices (thought about this in Iraq)
      • CEO of first Iraqi bank – unsecured loans to taxi drivers – less likely to take out the loan “between you and I, I’m a cowboy”
      • Entrepreneurs in these areas have to be better than others because of the instability vs the stable environments
    • Stock and capital guys – stock may trade at 3x earnings or 4-5x FCF, top and bottom lines growing at 20% per year
      • Usually just put there as knowing someone powerful but they can be bigger as allocators
      • 2008-09: Size at the time of the stock market – traded 3 days a week, 10am – 12pm on a white board
        • 60-70 companies, maybe 20 suspended from trading because 0 annual report – $1.8bn, smaller than Palestinian market
      • High growth company that is super opaque – can’t meet or won’t meet with CEO, maybe some others that state-owned enterprises that just want to keep paying salaries; maybe 5-10 companies that are investable
        • Equally weighted these companies initially and was still learning – now, he developed relationships and is on the board for companies
      • Does he want to put more $ and concentrate on the companies that he trusts and follows the guide
    • Largest holding for them – Baghdad Soft Drinks (Pepsi bottling and distribution for Iraq) – mixed-owned enterprise in the 90s
      • Local businessman (Pepsi is the dominating market share – ~70-80% vs elsewhere where it’s split) saw it was mismanaged
      • By 2008-09, were going to default on the loan they were floated – businessman bought the loan, fired management & 2000 ee’s, switched equity
        • Within a year, it was fully certified, tripled production, profitable business
    • Now, 5 days a week but still 2 days a week – had foreign investor interest, $5-6bn, couple IPOs successful
      • One telecom has about $1bn market cap, 14.5% dividend yield, 33%+ FCF yield
      • Foreign money came in 2013 but ISIS scared them off – coming back and interest from banks – arranging trips and momentum
      • Key problem – no 3rd party custodians (compared to Jamaica or even in Africa, HSBC or anything) – working on getting one
        • Makes it difficult to bring in foreign money – exchange is the custodian (which is actually safe)
        • No margins, cash-based market and settlements of t+0, no short selling (can’t sell unless have stock)
      • Oil collapse depressed prices and ISIS issue but has been up over last 2-3+ years, cheaper today than when he invested 11 years ago
    • Multiple expansion is the question for returns – 1x to 25x or 4x to 15x – depends on what they are as compounding
    • Kobe Bryant complimented him after a junior year game in high school – already looked at as a superstar – saw Geoffrey was dejected
  • Nikos Moraitakis (@moraitakis), Founder, CEO Workable (20min VC FF024)
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    • VP of BD at Upstream, enterprise sales in 40 countries in 4 continents
    • Wanted to start a company with type of what they wanted – centered around product, engineering company, starting in Greece
    • SMB over Fortune 500 because of product-focused and not corporation or enterprise-based
    • Applicant-tracking systems (ATS) – keep track of arcane process, don’t want to touch things – collaborative processes
      • Simple interface to solve problem of recruiting model (which is still 50+ years old)
    • Problems aren’t location-based – they’re conceptual – designing product, PMF
      • Opening with $500k or 50-200k if needing to get started, not necessarily $5-10mln like other markets or tech hubs, industry
      • Hiring engineers that are good in Greece isn’t so much a problem if you have a good company and network to attract talent
      • Moved to US not for VCs (doing good business, VCs pay attention) but they had 50% of customers without having anyone in US
        • Wanted to start customer support infrastructure, services and otherwise
        • Talked about marketing or accounting businesses on tech that taught companies that it may be worth it to update
    • Metrics that he pays attention to: month-to-month above $2mln annual revenue, ratio of new biz and lost biz
      • Not celebrating fundraising – few drinks but says it’s like having new shoes at the start of a marathon
      • Talking about investors and the relationships built to work together
  • Jaz Banga (@jsbanga), cofounder, CEO of Airspace Systems (Wharton XM)
    airspace-metal-crest2x

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

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

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

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

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

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

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

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

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

  • Eric Topol (@EricTopol), author of Deep Medicine, cardiologist (Wharton XM)
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  • Deep AI and medicine – asking how to properly apply deep learning or AI to medicine
  • Yet to have a drug made by AI
  • Bayesian principles failing in medicine community
    • 12% of women get breast cancer, yet we ask them to do 1-2 years mammograms
      • 10 year span, 60% will get a false positive (yikes!)
    • Adopters of Apple Watch and its cardiogram / heart information are treated unilaterally
      • Young, healthy and curious people may not have any arrythmia so any abnormality (totally normal) may be misdiagnosed
        • Signal from heart rhythm from one person may be different from another
    • Says it’s a sign of being behind (3rd industrial vs 4th for the rest) when Stethoscope (invented 200+ years ago) is still sign of industry
      • Analog, no option of recording and is still very subjective to the doctor listening
      • This, despite advances in imaging and scans otherwise
  • Mir Imran, Chairman & CEO of Rani Therapeutics (Bay Area Ventures)
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    • Talked about the big equity stake his fund took in the company, wanting it to be a big hit
    • Drug development, say, an insulin pill compared to injections (spent 150+ years on solving this)
      • Stomach / pill form in past is only 0.5% or < 1 % efficacy – can’t intake that many pills or cost-effective
    • Designing pill that is pH-shell-dependent to identify intestine pain receptors are limited (can inject no problem)
      • Pill recognizes pH change by dissolving outer shell (to pass thru stomach), then sugar-needle injects
      • 1000s of animal trials and just passing pills
      • Started trials ~18 months ago in Australia for pill traveling (using x-rays every 30 min to track)
        • Then progressed into drug for giantism, along with looking at other biologic diseases / solutions
    • Don’t want to limit biologics to a single buyer (Novartis, Genentech, etc…) – keep it open
      • Investments from Novartis and Google initially ~$150mln worth now
  • Mapping Dialects with Twitter Data (Data Skeptic 4/26/2019)
    • With Bruno Goncalves about work studying language – now @ Morgan Stanley
    • Started with research in CS and Physics, moved eventually to Apache weblogs, email, big router logs, Twitter conversations to study human behavior
      • Turned into looking at Twitter check-ins with the log using longitude and latitude
      • Language used: order maps, areas dominated by specific language (drawing boundary between French and Belgian in Belgium, for instance)
        • Intermingling cities that attract many languages
    • Spanish changing from one areas to another – everyday words, phrases
      • Can use the location data to determine the area of dialects – splitting Brazil, for instance (South, North and then central American)
      • Dividing grid cells into km x km – maybe not determinate of gradients of English vs Spanish since they were testing dialects of Spanish
    • Each row corresponds to each cell and words, but the matrix essentially loses the meaning
      • Ran PCA analysis and K-means on the clustering
    • He’s gathered 10 tb of data from Twitter, corpora and looking at millions of tweets – too few data to look over time
      • Measuring language changes over time was difficult for Spanish, but easier with English
      • Used Google books, for each language and counting bi-grams in how many books – popularity of words
        • Corpus of books published in UK vs US for Google books (1800 – 2010) for reliable data, but further back was less popular
        • Could normalize for words on how American vs British words were (and the mixture)
    • Recently, looking at demographic splits of language now with more digital / online presence
    • Training spellcheck based on dialect or demographic splits
    • Doing this stuff part-time now – how to train a model to detect use of language in this sense
      • Using word embeddings to detect automatic meanings of slang to determine the different meanings
  • Matt Turck with Plaid co-founder, Podcast
  • CSR in Gaming Industry,  (Wharton XM)
    • Discussing how it can be weird to gain competitive advantage and share
    • CSR as large subjective to the person looking in
    • Gaming companies chip in to gambling addiction hotlines / help / etc
    • Particularly in Las Vegas / NV, CSR survey determined that direct opportunities in water conservation, energy and green energy
      • How can they more efficiently run such large operations
    • Survey had about 80% of the gaming industry represented, from servicers to manufacturers to casinos themselves
  • Suranga Chandratillake, GP at Balderton Capital (20min VC 090)
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    • Founded blinkx, intelligent search engine for video/audio content in Cambridge in 2004
      • Lead company for 8 years as CEO through journey to SF, building a profitable biz and going public in London
    • Early ee at Autonomy Corp, engineer in R&D and then CTO when he went to SF
    • Belief in technical person shouldn’t be CEO – idea has been replaced in US (probably split)
      • Jack Dorsey, Zuckerberg (went into Dorsey and the idea 2 companies that aren’t really overlapping)
    • Been impressed by both Gates and Zuckerberg
      • Knowing the code and going through to understand everything
      • Zuckerberg’s acquisition of Instagram (ridiculed for $12bn and how right he was – surpassed Twitter’s users)
    • 2 Things for Tech going forward – changing infrastructure
      • VR and including drones or autonomous vehicles – hardware progression and mobile phones for what CAN be done
      • Societal difference – how we change to gig economy or how we work and spend time each day
    • Changing investments – just announced investment in Curious.ai
      • Shorter term where society is changing (eg: Nutmeg for planning pension plans, cheaper and available)
    • Structure for Balderton as equal partner VC – not sure it’s the most efficient model, but thinks it’s the best
      • Equal partnership where the partners have all the same number of votes, compensation and identical split
        • Removes the politics and impacts the partners and the stakeholders (founders that may be affected)
      • No single group that can carry the vote – hiring becomes very difficult because you need an equal
    • Knowing when to stick and when to switch
      • Deciding when to say “We don’t do that” and pivoting and being right
    • Exciting company for investment that he’s done recently – Cloud9
      • All development being done in the cloud now
  • Nick Leschly, CEO Bluebird Bio (Wharton XM)
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    • Purpose Built, 5/14/19

 

 

  • A Guide to Making Decisions (a16z 5/12/19)
    • Emily Oster, Hanne Tidnam discussion
    • Health and personal spaces – “right” thing isn’t a difference for some
      • Cocoa crispies, eggs and the preference for why you eat them, not just health-related
    • Impact of breast feeding vs not and looking against obesity
      • Less likely to be obese if breast feeding
      • Random trial for this is from Belarus in the 1990s where they requested parents to some breast fed
      • Siblings in choice to breast feed – almost no correlation if they did or did not
    • Constrained optimization – money or time
      • As parent, making choices based on preferences but have constraints
      • Recency bias for current literature – only data will be problematic and that it can only support a relation
    • Regression discontinuity as drawing a line and then seeing interventions and how they may change or affect outcomes
    • Screen time as having evidence that’s just very poor
      • Not easy to run randomized trial on – iPad or lifestyle would have to change for trial
      • Apps in short term doesn’t have the info available for results / outcomes
      • Studies currently don’t have any conclusions on phones/tablets – little
      • Bayesian updating on uncertainty – if 2 year old spends 50% of waking hours, that’s bad. Where’s the limit?
    • Last time went to doctor – very concretely made a recommendation of “2 hours max in a day”
      • How does the information get to that level of the system?
      • Conversations occur with doctors where they essentially make the lines that stick, pass on
    • Recommendations go out as arbitrary but now are made as truth – terrible without the decision
      • People that take the recommendations are likely more different or health-conscious
        • Added another layer (vitamin E, for instance) – those that adopted did many other things
    • Just be wrong with confidence – can be right, lots of good options
  • Innovating in Bets (a16z 5/8/19)
    • Annie Duke (@AnnieDuke), Pmarca, smc90
      annie-duke-thinking-in-bets-199x300
    • Every organization and individual makes countless decisions every day, under conditions of uncertainty.
    • Thought experiment: posing Seahawks run the ball and are stopped vs passing and throwing pick
      • Once there’s a result, it’s very difficult to work backwards to assess the decision quality was.
        • Outcome was so bad – it was in the skill bucket. Or – oh, there was uncertainty.
      • Very slow in NFL, for instance, to adopt analytics according to the numbers
      • 4th and 1 – should unanimously go for it since numbers say the other team will get 3, expected
    • As a decision maker, we likely choose the route that keeps us from getting yelled at
      • We allow uncertainty to bubble to surface – conflicted interest – long run vs short term
      • 2×2 matrix of consensus vs nonconsensus and right vs wrong
        • CR fine, NC-R genius, CW – fine (all agreed), NC – W really bad
        • Outcomes are right and wrong – hard to swipe the outcomes away
      • Thinking or allowing uncertainty in human driving and killing a human vs autonomous vehicle
        • Black box not understanding autonomous vs THINKING understanding another human (“Didn’t MEAN to do it”)
    • Anybody in business – process, process, process
      • R/E business – everybody in room after appraisal is 10% lower vs 10% higher (similar analysis)
      • Outcomes come from good process – try to align that an individual’s risk matches with corporation’s
        • Rightest risk – model could be correct (tail result) or risk decision (and deploying resources)
    • Most companies don’t have SITG – how to drive accountability in a process-driven environment so results matter?
      • How do you create balance so outcome caring about is the quality of forecast?
        • State the model of the world, places and what you think. How close are you to the forecast?
      • When you have a bad outcome and you’re in the room?
        • How many times do people say “Should we have lost more? Should we have lost less?”
      • Learning loss – negative direction on how to figure out? Poker example of betting X, X-C or X+C
        • Bet X and get a quick call. Should’ve done X+C (learn, regardless of opposite getting a card)
    • How much can you move an individual to train thinking? Naturally, thinking in forecasts more. Will have reaction and lessons quicker.
      • Getting through facts quickly and having the negative feedback – not robots
      • Improved 2% which is amazing – what are YOU doing to not make it worse?
      • “Results-oriented” as one of worst for intellectual work – need a process.
    • Story of “something is happening” is not a good story. Hard to read journalism now for him because they’re both “non-consensus”.
      • She’s optimistic that people can be equipped to parse narrative to be more rational. Pessimistic of framing and storytelling currently.
      • “Not making a decision” is making a decision but we don’t think of it that way.
        • Really unhappy in a position. Did the time frame thing “Will you be annoyed in 1 year?” – Yes. Nondecision didn’t feel like decision.
      • Mentions not having kids – time, energy, heart and decisions associated with indecision.
    • On individual decision, you have: clear misses, near-misses, clear hits
      • Bias toward missing – don’t want to stick neck out. Have to see it in aggregate. Forecasts.
        • Anti-portfolio / shadow book is really about when you include clear misses vs near-misses.
        • Fear is that the ones that hit are less volatile or less risky will be returning less than shadow portfolio.
      • Says it’s difficult to do with 99:1 turndown vs investments. Sampling.
        • Time traveling with portfolio – bounce out and see if “if this is 1 of 20 in portfolio” vs “invest vs not”
    • Conveying confidence vs certainty
      • I’ve done my analysis. This is my forecast. Is there some piece of information I can find out that would change my forecast?
        • Bake it into the decision. Not modulating the forecast – 60% to 57%. Costs and time differences.
      • Putting confidence interval on earliest dates and another probability, inclusive, on latest day
        • “I can have it to you on Friday 67% of time and by Monday 95% of time.”
        • Terrible to ask “Am I sure?” or others “Are you sure” compared to “How sure are you?”
    • Pre-mortems in decision analysis
      • Positive fantasizing vs negative fantasizing (30% closer to success by thinking of hurdles in front of you)
      • What happens if you ask out crush and they say yes? What happens if you ask out crush and they say no? – No’s more likely.
      • Teams get seen as naysayers – individually write a narrative on pre-mortem – good team-player is how do you fail?
      • After outcome, we overweight regret – don’t need to improve this much. Look out a year and see if it affects you.
  • Mary Gray, author of Ghost Work: How to Stop SV from Building a New Global Underclass (Wharton XM)
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    • Research with coauthor at Microsoft Institute
    • Discussion of the b2b services that run in the background – used Uber’s partner as example for driver verification ID
      • Beard vs no beard on default, for instance – need close to 100% accuracy and algorithms/vision can’t pick that up just yet
    • Humans that run mechanical turk or various tasks on classifications
    • Technology used to be cat vs dog and now it’s species of dog – likely tech will enable more specific classifications but not remove intervention
      • Running with surveys, captioning, translations, transcription, verifying location, beta testing are all tasks
    • Used another example for “chick flick” meaning or 2012 debate with Romney’s “binder full of women” comment
      • Needed humans to assign relevance and to provide or connect proper context (Twitter, for instance)
    • Facebook and its content moderators, most social media companies do this
  • Trends in Blockchain Computing, (A16z, 5/18/19)
    • Get paid for AI data or encrypted data – can still train a model on it but nobody would know exactly what the data is
      • Long term accrual – depends, also, on if it’s on AWS vs open-source
    • Gold farming and ex-protocol websites in WoW, for instance – virtual worlds

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

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

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

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

I hope you all enjoy!

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

 

 

  • Benito Cachinero, Egon Zehnder Leadership Solutions (Wharton XM)
    • Previous President HR at DuPont HR
    • Discussing potential ~4 factors for leadership (direct contradiction to measurements in being accurate)
    • Aligning resources for growth strategy in new markets – China vs Midwest, for instance
  • Pulse Check on Consumer Tech Trends (a16z 1/17/2019)
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    • With Benedict Evans, Steven Sinofsky
    • Trends at CES – no consumer product themselves, just a lot of all parts of supply chains / manufacturing
      • Batteries in 10k – 100k, so know what you want
    • TVs were not curved (nobody bought curved) – had 3 ft bar and tv came out above it in 10-15sec
      • Or edgeless Samsung blocks “The Wall” where you could make them as large as you wanted – LCD in any shape/size
      • Sizes could be anything now, amortized supply chain and manufacturing plants vs idling
    • Media content providers and apps
      • Pausing / syncing and Samsung apps with Apple video – clunky or AirPlay hardware
      • NorCal vs SoCal or California vs other states (think Apple phone vs the rest)
    • Easiest product to get alarm from 12+ companies for an hour to plug stuff in and it’s done
      • Proprietary electrical wires until they got low energy Bluetooth and now it’s everywhere
      • Lock or other nuts and bolts having SKU proliferation or new homes
        • Have to know gen contractors, Home Depot, developers and fragmented
    • FirstAlert smoke alarms – mesh wifi since they’re hardwired or battery
      • Put wifi in the alarm (to go to phone, etc…) – lots to do it with insurance or risks
    • Alexa chip supplier to connect everything
      • Apple tried to do Home Kit but eliminated everyone because almost nothing was implemented – wasn’t easy
      • Amazon has leverage for hardware but it has to benefit them for Alexa and being useful
      • If all makers saw HomeKit, could join war for Alexa vs Assistant (now that everything has their discovery appliances / connect)
      • Compare electric toaster to holding bread in front of fire and similar progressions
    • Show about running experiments is CES vs show about finding business value
    • Cultural part of CES – Japanese hand clapper
      • The founder of Ukrainian and employee and others that were hustling
  • Caryn Seidman-Becker, CEO of Clear (Wharton XM)
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    • Biometrics and buying Clear
    • Talking about the reactions for people getting to skip lines – make it more efficient
    • Allow TSA agents to work on what is actually important
    • Trying to describe to change customer behavior in the privacy aspect of what they keep
    • Biometric data but encrypted and secure

 

  • Brett Hurt (@databrett), co-founder and CEO of data.world (Wharton XM)
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    • Discussion of Edgecase (Compare Metrics), BazaarVoice (running backend for a lot of ads)
    • How they stumbled onto the data.world site – calling his co-founders of next big idea
  • Rod Hochman, MD and President, CEO of Providence St Joseph Health Leadership (Leadership in Action, WhartonXM)
    • Talking about being a junior member of the board of physicians when he started out
    • Leadership and how he went into the administration side as a young physician
    • Administration for many physicians is beyond the time / scope of many – hard to think of it without doing MBA or taking time
      • Important to combine the two for the expertise and management
    • With this business, how much is going on between clinics or hospitals and the network
  • Sentiment Analysis, Ellen Loeshelle, Dir. of Prod Management at Clarabridge (Data Skeptic 4/20/19)
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    • How positive or negative a customer may be expressing a review or otherwise, polarity
    • Academically, may have entire text as positive vs negative
    • Clarabridge – helping their clients understand their own customers
      • With hotel experience: could be multiple levels – service, cleanliness, check-in, overall
      • Using a clause, individual tokens, lemmas, parts of speech and how they’re related
    • Dealing with 21 languages natively, and having computational linguists on staff to understand the grammatical syntax or individual contractors
      • Vocabulary can change, but not necessarily syntax (think: sick)
    • Sentiment is rules-based engine as BI tool for her end users – full control / transparency for analysis
      • ML in place with w2vec for tuning rules in the engine since those change based on context/industry
    • Flipping sentiment or negating and modifiers as using the extreme ends of sentiment analysis (-5 to 5 scale)
      • Structured stays similar, but lexicon changes contextually and sarcasm / transcriptions as more difficult unless obvious or explicit
    • Sentiment goes along with their emotion or effort analysis for customers
      • Enterprise tool and APIs for engine on enriching internal systems
      • Considering sentiment analysis as table stakes now – different than when they first started when they were ahead
    • Client in small kitchen appliances used Clarabridge to treat sentiment on competitors, specifically for pressure cookers
      • Eventually saw that the sentiment split for pressure cookers and that pushed them into doing Instapot
  • Stephanie Cohen, Evolution of M&A and Corporate Strategy (a16z 5/7/2019)
    A Goldman Sachs sign is displayed inside the company's post on the floor of the NYSE in New York

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