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

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

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

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

 

  • AI in the Past, Present and Future (BDB 7/16/19)
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    • Rod Bodkin, Tech Director at CTOs office in Google
      • Was with BigDataAnalytics, bought by Teradata and grew it from there
    • Grew Google after seeing the field advancing quickly, state of the art as evolving
    • First people to put Hadoop into production – Yahoo was too scared, single algorithm took weeks at the time
    • OpenAI put out state of art compute paper – 4 year paper, 300k X computation (double every 3.5 months)
    • For Google, evolution of cloud in the enterprise is a big deal – consumer side of Google as leading the way
      • Can just put data into BigQuery because of capacity and accessibility of data – increased production 4x on data science team
    • Big investments into Anthos – open source tech to enable cloud-native services in different clouds, GKE (Kubernetes)
      • Edge TPUs as 100x faster to compute a model vs traditional mobile CPU – TPU as accelerator chip for DL
      • CPU is completely general so less efficient
      • GPU has a boost over CPU but behind TPU accelerators (starting GPU chips, Tensor unit)
    • Kaggle Days and Google IO for cloud Pixel modeling and AutoML performing very well
    • Herrari’s book – 21 Problems for 21st Century
  • Tricia Han, CEO of Daily Burn (Wharton XM)
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    • Community of like-minded fitness fanatics
    • Live 365 – 30min shows on working out, regulars
    • In survey, millenials said fitness #1 and health/wellness at #5
    • Fitness had about happiness equal to making $25k more

 

 

 

  • State and Future of Robotics, ML and Digital Celebs (Venture Stories, 8/8/19)
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    • Michael Dempsey (@mhdempsey) – partner at Compound
    • Read, Listen, Write, Talk – Cunningham’s Law – share something with a strong opinion is likely to get responses
      • More value when shared publicly
    • Robotics, ML as cascading forward – robots broadly, initially – types, how to make them intelligent (2013)
      • Drones, hardware platform (DJI as leader), space and now as unsupervised or self-supervised learning
      • Deep dive on innovation for what he’s spent the last year or two – investments, as well
    • Women’s health as growing market for fertility and experience layer in healthcare system
      • Higher-end service around egg freezing (but was shattered by Tia founders), IVF or embryo screening
      • 2 investments for him already in the space, maybe more after
    • Strategic robot acquisition for Amazon, why now? Major companies in the space – he’s punted in that space, more investors.
      • Didn’t see meaningful differentiation in the space – didn’t see a company that had that from an investing side
      • Food was where he saw robotics as consistent – grew up in the industry
      • Really easy to get pilots but not for revenue – wants full-stack robotics company
      • Robots taking over entire industry – automated X / Y / Z (rebar, construction robotics)
      • Front of house and back of house retail (analytics, stocking)
    • Weird robot applications (in-home, manicures, old person help)
    • If company is built on algorithm being best, company probably won’t survive
      • Must talk to people doing operating – not just reading
      • Self-driving cars – spent time with Daniel Gruber, discussing local maximum and rules to write
        • If you can drive in NY, you can drive in SF, LA, etc…. 2007 DARPA challenge Waymo / Tesla / Cruise as result – path-planning
        • Intelligence approach – what are incentives / agents to accomplish in a car for end-to-end approach to scale
      • 1 model to move them all – enough compute that model can solve it (DL is direct function of this, for Google)
    • Investment in data labeling space – more people moving into production requires more people getting good data and filtering data
      • Larger data builds where it may cause $50-200mln per year to label but 50% is useless
      • Environmental impact and thinking about it – consolidating data but into better (CartaAI and SkillAI)
    • DeepGram end-to-end audio inscription – 80-85% can be good, but if you mess up some key words in certain industries, it’s more expensive
      • Voice side, horizontal players are pretty good – if x% of users will have same questions, simple workflow or algorithms
    • GANs and new generation of faces – Disney and animation nerd for a while – power of IP on agencies, CAA for example and Marvel
      • Stories through animated content, Robot Chicken, others – Robert Dillon – bringing in GANs
      • Watching live action is watching someone else’s story whereas an animated one brings you into the story
    • Trusting the people that have been given permissions – Reddit or being anonymous
  • John Roese, Global CTO of Dell EMC (Mastering Innovation, Wharton XM)
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    • Talking about the 20 year vision to be autonomous but incremental parts until then
      • Driving assist, improved AI in driving, maybe geofenced before autonomous
      • Autonomous vehicles as source of innovation – sensors / LiDar very useful for other industries but too expensive
        • Had talked to studios about virtual studios or conferences – expense should come down with auto
      • Vast problems with uncontrolled or unconstrained problems – already have fully autonomous warehouses or geofenced areas
    • Interested in bio feedback as input to AI or MI systems
      • Used example of video conferences with sensing stress levels – clearer audio, accent correction, more people = more stress
      • Cars already using bio feedback
      • People already wearing sensors via devices – can use that as more input
    • Attacking low hanging fruit because of data ethics or biased data inputs – easier to solve problems that are valuable in neatly constrained
  • Amri Kibbler, Katya Libin, Hey Mama co-founders (Wharton XM)
    • Collaborate and share and support their work for mothers as executives
  • 13 Minutes to the Moon
    • Ep. 06 – “Saving 1968”
      • Apollo II’s first landing – without Apollo VIII, Pathfinder and 250k mi to the moon, maybe gutsiest flight until then
      • Flying VIII before end of year – “We were not ready”
      • 2 deaths of MLK and Kennedy – April had hundreds of cities taking part in riots, thousands arrested
        • 1968 Apollo program was in shock and Saturn V rocket was malfunctioning – troubled test flights
        • Almost busted in all 3 phases the last time it had flown, and the lunar module had slowed down, as well
      • Taking lunar module away from Apollo VIII – former test pilot Jim Lovell said it as Lewis & Clark expedition
        • So many firsts, risks that were enormous on a 100x scale – reason Jim was there in the first place
        • Crews normally had 6 months but VIII only had 4 – mathematicians were responsible for all of the angles and engine durations
      • 1 chance in 3 for mission successful, 1 in 3 for non-crash but unsuccessful and 1 in 3 for not coming back – wife accepted this
      • Media as delivering “death pills” for dying painlessly – respondents would say oxygen would run out and it’d be fairly painless
      • Trans-Lunar Injection – don’t shoot at the duck, shoot out front – wanted to go to 60 mi ahead of where the moon would be
        • Spacecraft needed to get to the right moment, speed, angle and altitude for the moon
        • Human computer – Katherine Johnson – was responsible for the trajectory for launch time (Hidden Figures)
        • Took 3 days from launch to get to target – Lunar Orbit Insertion
      • Astronauts were late on radio contact from dark side of moon
        • Came back to light and could hide behind his thumb – 5 billion people and everything he ever knew
        • Finishing Apollo VIII with scripture and then Good Night, Good Luck and Merry Christmas
  • Bill Clerico, co-founder and CEO of WePay (DealMakers 8/13/19)
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    • Leading provider of integrated payments for software platforms, raised $75mil from SV Angel, Highland Capital, Ignition Partners, August Cap
      • Founders of YouTube and PayPal also in
    • Grew up in NJ, spent time in NY and father worked in Air Force and construction – taught himself computers in 80s
      • Received a scholarship to go to BC, met his co-founder for WePay waiting for the flight for the interview 6 years prior
      • Went to do IB at Jeffray’s – advising tech and software companies with clients, passionate and building for a year to quit
    • Installed a suit rack in his car because he wasn’t going home – long hours, brutal fundraising
    • Group payments that they saw repeatedly at the age of 22 – big market for payments, testing it out
      • Wouldn’t have less responsibilities than at that time – Rich deferred law school and Bill had worked on it full time
      • Tried to pitch Boston investors and failed – less receptive to early stage investing, applied to YC instead
        • Came out to the valley for an interview
    • Spent 1.5 year to invest and took money and sold furniture and drove to the west, taking turns
      • Product was conceptual, pitch deck was opinion and it was hard to prove a market need to investors – conceptual idea
      • In YC, built product by talking to fraternity treasurers at SJSU, ski club coordinators – got them using the product
        • Went to talk to investors by showing them the traction
      • Why would a treasurer to accept payments with different product? Host bbq and invite them over. Go to dorm room and watch product usage.
        • Responsive to requests – take feedback and be better than existing solutions. Gain knowledge in start by doing things not scaling.
    • Group payments were a big problem and needed a solution – weren’t willing to pay, or pay transaction fees
      • Venmo had raised money and had a bunch of momentum by giving away services for free
      • Competitors were taking advantage, 2 years after YC – pivoted but weren’t growing as fast
        • Built an events tool, donation, invoicing tool and an API for customer use – other companies were just doing those
      • Realized they could build an API making payments experience easy and simple and let competitors do whatever
        • Saw huge traction/benefit where they could be brought in via the API (since they had raised $30mln)
        • Needed the business to be grown but expectations were higher
    • 600 lb block of ice for marketing $500 in front of PayPal Dev Conf at Moscone Center – still highest market day
      • Since PayPal had a knack for freezing people’s accounts randomly
    • Pivoted to shut off 70% revenue stream from consumer product, gaining growth on API from other customers
      • GoFundMe used them as a payments processor from when they were 2 person company
    • Prior to acquisition by JPMC – 200 employees at that time, now fintech / bank
      • Asset purchase agreement day – tired – was negotiating final points of deal in person, had some drinks to celebrate
      • Bought a cabin in Mendocino County – deal was valued at $400mln
    • Part-time partner at YC now – helping companies in general – relevant to the next entrepreneurs and the scale
    • Angel investing on the side – much longer and harder and scarier than he ever would’ve imagined
      • Reinforces this to his younger self – startup doesn’t fail unless you give up
  • Evolving Narratives in the Crypto Space with Andreas M. Antonopoulos (FYI 3/12/19)
    • With Arjun Balaji, as well — and similar for me as host, his intro to Crypto space video YT
    • Conflict of Crypto Visions article by Arjun and host
      • Identified closely with unconstrained vision and doing talks on not playing zero-sum mentality
      • Ethereum as different than Bitcoin – evolving directed by design choices
    • Engineering consists of design tradeoffs – choices of optimizing and de-optimizing parts of systems
    • If you want to make something that is Bitcoin-ish, you run into problems for all the strengths that are already inherent to Bitcoin network
      • Differentiate enough to be a new thing from Bitcoin – can’t mingle or occupy that niche
      • Is privacy a big enough differentiator to separate from Bitcoin network?
        • Strong privacy in base layer – can end up with inflation bugs that can damage sound money policy of Bitcoin for the privacy
      • Sound money vs private money – not clear yet.
    • Hard money displaces other forms of money in long term but only if they’re maximalists and logical
    • Friction levels determining switching back and forth on a wallet between utility or store of value tokens / coins in the future
      • Automated backend where they are optimized
    • Interest in Ethereum – tradeoff worth making for smart contracts and applications that aren’t just money outside of Bitcoin
      • How the technology of VM blockchains work
      • Scaling is harder in Ethereum – proof of stake has different security model than proof of work
      • Sharding, beacon chain, polka dot – not sure if it will work or what the security constraints are – could have applicability to BTC
    • Bitcoin critics – make the case for it but then explain value proposition or store of value
      • He has an opinion, others have opinions – none will determine how the market develops
      • Arguing is a waste of time. If you understand the tool that’s best for a job, you’re a better user of tools.
        • Which is the correct tool and how to use it properly – perception is limiting in general
  • Sam Yagan, CEO of ShopRunner (Wharton XM)
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    • Founding dating OkCupid and then going to Match and scaling to IPO
      • Going from running a team of 30 to 1000 in a month
    • Ecommerce ShopRunner as retailers combatting Amazon and Walmart – providing scale and guarantees with 2-day shipping for many retailers
      • Joining after Michael Rubin had founded it on premise of “Amazon for all others”
    • Making sure they have AMEX partnership to make it easy for customers
  • Travis Katz, VP of Product at Skyscanner (Wharton XM)
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    • Had been cofounder of Trip.com and at Myspace prior
    • Social media giants Facebook and Myspace – selling to NewsCorp and getting revenue compared to funded Facebook acquiring users
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Transformation of Innovation (Notes from Aug 12 to Aug 18, 2019) September 4, 2019

Posted by Anthony in Blockchain, Digital, education, experience, finance, Founders, global, Hiring, Leadership, marketing, NLP, Politics, questions, Real estate, social, Uncategorized.
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Hello! Hope Labor Day treated everyone properly, whether you snuck in some time-and-a-half pay for work, avoided it altogether or vacationed. I am going to keep the brief at the start short today because there’s a common theme. And I have been considering longer form writing without the notes on other topics maybe once or twice a week.

From last week – I still am working on the 13 Minutes to the Moon podcast – excellent. And it’s engaging as they went through the building and prep work that went in to getting there before decade-end.

The new segment that a16z has produced with the 16 minutes on the news has been fun, especially if you like an audio version of what’s been popular in tech/news. Sonal has done a great job leading most of them. I found the two that I listened it related to the title – transforming innovation. Software as eating the world (any company/product/service that can be digital will force the company to become software company), along with digitizing many of the slowest movers because the pressure has become high enough (re: Fed with ACH Now). At some point, in order to command more control or to make sure you aren’t disrupted out of the market, companies have to compete and give the customers or users what they want – faster, easier transactions in Fed Now’s initiative.

There were also some fantastic investors / founders that are included. How they developed and framed their careers to step from one thing to the next. If you noticed, many of the 20min VC episodes I listen to are in order from 2015 to now 2016. Fascinating to hear the comments made at that time to update to 2019 (as many of the same bullish comments are made with caveats that have yet to come to fruition – and valuations increased accordingly).

Hope you enjoy the listens!

  • 13 Minutes to the Moon
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    • Ep 05 – “The fourth astronaut”
      • Intertial navigation – if you have your speed and know where you are, can control where you’re going
      • Self-guiding ballistic missiles that couldn’t get thrown off course via radio or otherwise – knew where it was
        • GPS, primitive computer received navigations and could adjust course if necessary
        • Charles Stark Draper who founded MIT’s guidance instrumentation lab
      • Had been a grad of Stanford and went to MIT and became leading expert in aircraft instrumentation / guidance
        • Dedicated to the astronaut program, so much so that he applied – was turned down
          • Practical application with such sensors to be useful was his expertise – size / practicality in flight control systems
      • Had to convince everyone that the computers would work and be trusted
      • Apollo bought 60% of the chips that were out and being manufactured – huge boost for computer industry
        • Good hardware required good software (an afterthought)
      • Called on programmers for building the software Margaret Ate Hamilton (started as programmer, then was in charge as program manager)
        • Developed a system to write software so that it would be reliable and she sought out the bugs/errors – no way to do it otherwise
          • Right times vs wrong time, wrong data, wrong priorities (interface errors) – we take for granted everything we have now
        • No rules or field at the time (akin to “Do you know these English words?” – yes, you’re qualified)
        • Don Isles – math graduate looking for something to do next who joined in 1966, software had been written initially – app code to fly was starting
          • Lunar landing phase commanding – in retrospect, huge – but it was a job at the time
      • Apollo Guidance Computer – 70 lbs in 1 cu ft, 55 W with 76kb, 16-bit words, 4 kb were RAM R/W memory, rest was hardwired
        • Got to the moon on punch cards – 100 people working on it at the end – submit in one run overnight and run simulations
        • 2 women that worked to keypunch before working as full-time – printed lines of code to turn into punch codes
      • Noun-verb inputs for flying – lunar landing, for instance
        • Built the computer interface with idea of “Go to moon” and “Take me home” but it instead had 500 buttons and was much more interactive
          • First system where people’s lives were at stake with it – fly by wire system. Astronauts didn’t control it, they controlled the joystick, etc…
    • Ep 06 – “Saving 1968”
      • Armstrong and Buzz Aldrin
  • Fed reaction (a16z, 16min on the News, 8/12/19)
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    • FedNow – 24/7 open service for access to checks faster to launch in a few years
      • Half the population lives paycheck to paycheck and should care for the $30 overdraft fees that a ton of people do
      • Massive amount of losses to banks here in the US
    • ACH batches all payments in a day or maybe twice vs instant
      • Realtime payment network – 26 banks but need all banks to be a part of this network
    • Against Fed would say to just run the regulatory part vs the operational side
      • Obligate banks to join ACH, etc…
      • Infrastructure for checks has not updated to the tech advantages that we’ve gotten to now
      • Catching up to rest of world, which is 10 years ahead
    • Death of retail – Barney’s filing for bankruptcy, closing 15 of 22 stores
      • Been around since Great Depression
      • Ecommerce coming and direct to consumer is going toward market share
      • Highly leveraged fixed costs, inventory but can go sales to hemorrhaging money and become unviable
    • Grocery is largest single category of US retail, more than apparel and personal – completely immune to digitization historically
      • Inventory is better served close to consumer, physical grocery as distributed warehouse
  • Philipp Moehring, Head of Angelist EU (20min VC 1/6/16)
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    • First European hire for Angelist since Jan 14, venture partner at 500 Partners and Principal at SeedCamp
    • Angelist Syndicate for his
    • Worked for a bunch of startups during his studies, but realized he didn’t want to work for a large company or consultancy like when he started
      • Worked for a professor that was doing research on VC – did his thesis on same topic, asked for data
      • Fulltime job came from a guy who went off on his own to start firm and he was asked to join
    • MBA in Tech Management and Tech Entrepreneurship, where management is very different there
      • Analyst and associate work can be a great job but it’s not a quick way to partner or anything
      • Seeing founders doing a second business after 7-8 years, even after do great and get raises
        • People don’t usually stay at their first job for 8 years but starting at VC, people will jump to a startup second
    • EU vs US scene – SV where VC started and is much more advanced, simply due to a lack of epicenter
      • Angelist looking to get into Series A (not necessarily leading, though) – movement
    • Certainly London for VC – number one ecosystem in Europe, as the largest metro area, tech and VC and money
      • Hard to copy for other places – culture, politics and what makes the city to be interesting
      • Berlin has the momentum as the number two, as well as Stockholm or in Finland, maybe Paris (inward), Lisbon and distribution of eastern Europe
    • $400mln funding for Angelist from CSC Upshot into syndicates – GPs investing directly
      • Does his 500 Partners role on the side – usually someone with investing on the side and has more firepower
      • Wants the deal flow or coverage in the areas they won’t have
      • Knows an entrepreneur and can get in the chance on seed or small amounts to invest in
    • Known the partners at 500 Startups for a bunch of years and could invest similarly to his Angelist style
      • Could be flexible and born out of the way the fund is positioned and investing
    • Most exciting for him is having people that he’s invested in hitting their stride and succeeding
    • William Gibson as a writer who influences his thinking, Snowcrash as a book that depicts the future
      • Looks more at science fiction for tech advances now
    • Most read blog – too many to count, Brad Feld – has a tool called SelfControl against social media
  • Phil Libin (@plibin), co-founder and CEO All-Turtles (Mastering Innovation, 8/8/19)
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    • Discussing real problems with AI

 

 

 

  • Andrew Chung, Founder and CEO Innovo Property Group (Marketing Matters, 8/7/19)
    • Partner at The Carlyle Group, US real estate
    • Started IPG in 2015
  • Stefan Thomke, professor at HBS (Wharton Knows, 8/13/19)
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    • Discussing his paper on magic stick of customers
    • Online experiments – running them quickly and decisively

 

 

 

 

 

  • Ivan Mazour, (@ivanmazour) founder and CEO of Ometria (20min VC FF 029)
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    • Serial entrepreneur, author, investor – Ometria: predictive/marketing analytics platform
    • Born in Moscow, parents PhDs – mom brought him to UK to study math @ Cambridge
    • Started his first thing in property since that was biggest, public industry to get involved
      • Around 26, didn’t utilize any of his studies and data-focused nature, so he leveraged proceeds with his cofounder to make angel investments
    • Wanted to become relevant and learn about tech industry – made 30 investments in 4 years, stopped prop dev, did a Masters in App Prob
      • Refreshed knowledge to build a data company
      • Founding after investing – wrote a blog post as his approach to investment and his dream
        • Build a truly world-leading tech company but accepts lack of experience
    • Thought about how much capital to allocate to invest and how much to invest to be taken seriously – needs to be able to learn from it
      • Angel investor as $20-30k pounds
      • Received a second seed or extension round with Ometria – significantly bigger than seed, but reality is not enough for Series A
        • Hire more engineers, increase team from 20-30. But Series A would be to set up internationally and expand S&M
    • One-sided barbell – huge amount of funding on early, early stage investing
      • Anyone can work to get funding at early, small stage – lots of companies are vying for more eyeballs from bigger ones they need
      • At late stage, if you have the metrics, you’ll have the funding – growing 300%, hit $1m ARR and no question you’d get round, SaaS-wise
    • Launched as an ecommerce analytics company, wanted massive market for data – $3tn ecommerce and retail
      • Launching 2013, analytics was hottest thing (KPMG raised $100mln fund for this only) – by 2015 for big round Ometria, analytics wasn’t relevant/interesting
      • Fascinating to experience – marketing was far more important – actions engaging revenue and data, leveraged
    • First ones to come in were validating – people who he worked/invested with previously
      • Angels that were amazing, AngelLab’s Rachel that was meeting best founders and seeing best companies
      • Had tried to sell Phil as a customer on Ometria and he ended up investing – Alex is on board as 2nd largest institutional investor
    • Pitching angels vs other investors
      • With angels, he had engagement metrics, not revenues – introduced team and had beta user metrics (logging in 7x a day and loving it)
      • Four founders and engagement of platform that allowed closing of round
      • For VCs, chart of MRR that was up and right – increasing growth
    • Several funds liked the company and wanted to consider investing – said he should’ve held off, probably – got excited and continued conversation
      • Waste of time for both sides – hadn’t moved far enough on VC metrics to get a big enough investing for what you’re raising
    • Offline retail – stores won’t go away – thinks there will be an entire platform that will be an ecommerce platform that is based on personalization
      • Product recs, change website and order them – complicated and difficult – best platforms aren’t designed to do that – $1bn company
    • His highlight: sitting in his boardroom after increasing it, Elizabeth Ying (PayPal, head of D/S), Mike Baxter, Allie Mitchel (Huddle founder)
      Looking around that they were talking about his company and making a few investments that he was CEO of and they had 10-20 years experience
    • Favorite productivity tools: ToDoIst, Google Keep for managing main reports, HangOuts
    • Favorite books: Rich Dad, Poor Dad as formulating a way of thinking, and Dale Carnegie’s How to Win Friends and Influence People
  • David Tisch (@davetisch), MP at BoxGroup, Inc (20min VC 1/11/16)
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    • Also, cofounded Spring – brands to consumers via mobile with his brother, Allen
    • Coded as a kid, kept using the internet, entryway into internet and software – didn’t think of it as investor
      • Went to college and law school, became a lawyer and joined real estate finance in m&a but he did that for a year and wasn’t into it
      • Started a company, experimented and sucked – sold to a larger company and was there for 2 years at KGB
      • Went to TechStars – launched and run the NY program after he had made 3-4 investments
    • Cementing of the NY scene would be a magnet company like Amazon, Facebook, Apple, Google – huge magnet for talent
    • The Box in NY as a cool club that he hadn’t been to and his first investment was in a company called Boxy
    • A 20th employee is exponentially more valuable than a seed stage investor – tries to be an valuable investor, though
    • Magical utility or happiness for user or incredibly polished path to where you’re going – different from early days of mobile
      • Should happen soon – hasn’t happened since Snapchat/Tinder as consumer
    • Spring for him – exact opposite of sitting above the clouds as VC and strategy – incredible other side with his brother
      • Mall on your phone – 1200 brands directly (Etsy as maker’s story) – single mobile experience to make it better
      • Free shipping and free returns in 2015 for marketplace and working with their partners
      • VIP, customer service, making a single experience
      • Apparent that the opportunity was sitting there – he had told his brother “Don’t start a company”
    • Doesn’t read much – watches a lot of tv and consumes that as a way to learn
    • Finding his partner Adam at Techstars is probably the highlight
    • Reads online a lot – design blogs/architecture/city – Fred Wilson as successful VC in NY
    • Invested in SmartThings – sold to Samsung a couple years prior and built into products
      • Deep affinity for space, so he invested into Nucleus – video intercom in houses but it allows outbound, also
      • Uncomplicates the phone – primary thing on cell (voice, messenger and text bringing into house)
  • John Wirtz, CPO at Hudl (Wharton XM)
    hudl-logo.1de182540fb461fded02ad2cb75963d4945c560d

    • Coaching and products innovation – getting cameras at 50 yd line or in arenas
      • Not so much looking at point-to-point tracking or high speed for baseball, softball
    • More on tracking all high school players and colleges – uploading of highlights and working with coaches
    • 95% coverage now
  • Software has eaten the world (a16z 8/18/19)
    • Marc and Jorge Condo discussing computer science and its eating healthcare
    • Term from his essay in 2011 after starting firm, tech industry is 70+ years old after WWII, packing $500 that used to be $10-15mln
      • Pessimism after recession – Marc held opposite opinion as just starting (platform built)
      • 3 claims: any product/service that can be software product will be software (boomboxes, cameras, newspapers, etc…)
        every company in the world in those products will become a software co
        as a consequence of 1 and 2, long run the best software company will win
    • Incumbents in auto industry – cars are very dangerous, very hard and software companies think otherwise – value of car is in software (500 in 50 mi radius)
      • Surprising innovation fields: legal, insurance, real estate, education, health care
    • Never imagined investing in new car companies – new industry in 1890, 1920s Henry Ford
      • One new major car company attempt by Preston Tucker (Automotive – Tucker movie, catastrophe)
      • Went from hundreds in 1910s to 3 in 1920s and after
    • Profound technological revolutions as ML/DL/AI as incredibly innovative and cryptocurrency
      • Software founders for how to use and those that haven’t – can be quite transformative
    • Fundamental transformation with internet was music industry – triple whammy – people loved music (? Often dogs eat dog food? – not case in music)
      • Isn’t it great customers love music so much? They want the thing – showing consumption. Music executives said no. Suppliers refusing the demand increase.
      • Pricing issue – want 1 song vs 12 songs on label. Price-fixing collusion by the 4-5 labels. Could overcharge by factor of 10.
      • Consumers were breaking law but the correct reasons. Was immoral, illegal by price collusion.
      • Went from Napster, Kazaa, Limewire, Frostwire, BitTorrent (all investor catastrophes as too early since they couldn’t get pricing from labels)
        • Spotify as 15 years later where investors were scarred but time had come
    • When layer commoditizes, the next layer can become massively valuable – focus is on commoditized layer (contraction for recorded music purchases)
      • US market for live concerts grew 4x in aggregate demand – unlimited access to music, so fun is concert and experiences
    • Marc as serving on board of hospital – mission in terms of health care and medical research and school – nonprofit with highly motivated people
      • Design and build a new hospital – finally opening in 2019 (2005 green light)
      • Well-functioning boards that he sees as 7 people vs 25 or so in hospital
      • Quality problems in auto industry in 1950s / 1960s initially, unsafe at any speed – 70s/80s/90s was TQM – debug quality manufacturing
      • Medical compliance issues – 1/3 not filling prescriptions, 1/3 just take cocktails of them
        • Organ transplants are only 60% compliance
        • Assembly line requirements to motion – decode for running properly, maybe do that for hospitals and doctors – Purell, even
      • EMR at Stanford – $400mil one bid, $100mil to Epic and $300mil for implementation system Perot Systems
        • Interoperability and open source, building on everyone’s creativity (except Epic) and APIs
    • Eroom’s Law – price of bringing new device or drug to market doubles every 10 years – VCs in both decided the economic cycles were too different
      • Names now for VC are ones that aren’t the same big firms
      • Founders are different, as well – PhD in bio but programming since 10 or hybrid tech to pitch
      • Missing middle as converging of scientific domains and getting a16z’s new partner, former Stanford professor in the middle who helped spin it up
    • Digital therapeutics, cloud biology, IT applied to Healthcare
    • Defend market or advance innovate market but SV is starting from scratch – experiments in tech, or business (famous train wrecks)
      • Portfolio approach to experiments – 10 experiments in 10 different parts of biotech / industry – look at successes and asymmetric returns
      • If there are big companies that can do obvious things, they’ll be good at increment – industry does different ones
    • Need evangelical marketer or sales – Jobs’ saying how to envision the picture because consumers have no ability to project this
      • Elon’s Model S – no superchargers or charging at home – had to paint a picture to demonstrate it, get enough sales to build the chargers
  • Dan Granger, CEO founder of Oxford Road (Wharton XM)
    oxford-road-agents-of-influence-logo

    • Advertising in LA helping acquire new customers and branding

Big Goals: Being the First (Notes from Aug 5 – Aug 11, 2019) August 27, 2019

Posted by Anthony in Blockchain, Digital, experience, finance, Founders, global, NLP, questions, social, Strategy, Uncategorized, WomenInWork.
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A friend recommended the 13 Minutes to the Moon podcast. I wish I could shout out that friend, but I currently have no recollection for who it was. Sorry! I’ve been recommending it to anyone that wants an intriguing documentation and story for the decade sprint to putting a human on the moon – and everyone that contributed to that goal. If you need more convincing, Hans Zimmer did the music production, as well. So, it has to be epic, right?

So, that’s what I would strongly suggest everyone listen to. The rest were incredibly interesting, as well.

The co-founders of Original Grain discussed watch making, selling out of backpacks and getting the approval of their military brothers before finally catching on and building the business. Setting out their approach and moving back to the PNW. Co-founders of Lovevery talked about mixing the product, box subscription service with educational, proven research and why Jessica chose this model and building their own over the licensing / branding other toys/puzzles.

Nick Maggiuli, of Ritholtz and Of Dollars and Data discussed why he’ll follow / listen to others that he may disagree with in case something clicks that makes him update his information to change his mind. Then, discussing that the market isn’t zero-share after Ken Fisher mentioned that his firm ($30bn plus) could be wiped from the face of the planet and nobody would ultimately notice when the market handles $50tn overall. 30bps – can aim high and ultimately it comes down to your execution, rarely others.

Then, Morten Lund talked of the EU investing scene, his success early, bankruptcy soon thereafter and deciding what he wanted to see and do. Sometimes you have to toil in decisions before landing what you seek.

Hope everyone enjoys the notes and checks the episodes out!

  • 13 Minutes to the Moon (BBC Worldservice)
    • First episode – ‘We choose to go’
      • Lousy communication as they dropped thrusters to 10%
      • Something happening in computer that caused issues – Armstrong was nervous (rarely)
      • Worry when Sputnik was placed up and a dog in the next month before putting a person there (BBC / Moscow reported)
      • Not having hopeless odds – could do a crash program to get men on moon by 1967, 68
        • German (vonBrown) who set up the rec for the course to get on the moon – recognized Russians needed 10x improvement
      • V2 rocket program – never having wide support but post-demonstration, went to mass production
        • Nordhausen – very aware of concentration camp workers, mistreatment and threat of sabotage
        • Surrendered and Americans were all-too-happy to accept them for rocket program (and space)
    • Second episode – ‘Kids in Control’
      • Steve Bales as the 26 yr old kid who could shut off the mission
        • Guidance officer in mission control team – lunar modules onboard computer by MIT design – controlled flight to moon’s surface
      • Junior technical in backrooms to Gemini flight controller for Apollo by age 23
      • Rapid recruitment style in technical and sciences – just threw them in for trainings and went from there
        • Hiring on rapid basis – bring on board, operations, engineering, training
      • John Aram – math and physics in North Texas to mission control – recalled so many acronyms (never been to a big city)
        • Moved to murder capital of the world, 6 weeks later and told his wife – maybe we need to load up and go back
          • We ain’t going back, she said.
        • Looked over electrical systems and the spacecraft’s electronics.
      • Average age of operators was probably 27 years old, grads of 1964 or so (older didn’t work out as well)
      • Simulations would run 20 different scenarios to demand engaging reminiscent of a fighter squadron
        • Had to trust each other well, kids and wives knew each other – risky things
        • Apollo I that killed the crew in 1967
      • Not enough time at home – many divorces from not being at home and holidays missing
      • In the trench – Gene Crantz: room bathed in blue light by the screens, smell of the room, people in for long time
        • Stale sandwiches, old pizza, full wastebaskets, coffee burnt into the hotplate, but you get feeling something will happen
        • System needed Gene’s toughness, former Marine, constant chain-smoking and needed that guidance from the flight director
      • Calling program error 1210 – never seen it in simulation and Steve had called abort – in actual mission, they got 1202 from Buzz
        • Setting a set of rules for program alarms – Steve got help from a 23 year old in the back – Jack Garmin
        • No call to abort if everything else is good – took 15seconds to push
    • Episode 3 – ‘Long Island Eagle’
      • Slowing descent was the plan, but they ended up going faster
        • Surface wasn’t what they had anticipated
      • Why is the lunar module the way that it is – way it looks? Form follows function.
        • Landing and flying in space – very different than aerodynamics for earth atmosphere
        • LTA1 – cleaner than a surgical room, higher pressure (dust and contamination avoidance)
        • Puncture a hole in skin with a pen – needed lightness and fuel efficiency
        • All engines in lunar modules had to be without electrical failure, so they were just latches with combustible gases
      • Lunar module designed by aeronautical engineers – aerodynamic and smooth, glass but had to evolve
        • Glass was too heavy and crew survival was supercritical
      • December 1968 was supposed to be lunar module flight but they flew around the moon instead
        • Would make it, but it would be close to the decade
    • Episode 4 – ‘Fire to the Phoenix’
      • Fire in the spacecraft – BBC report of Apollo I explosion, January 27 1967
        • Lost 3 heroes – Roger Jaffe, Ed White (first to walk in space in Gemini program), Gus Grissom (piloted Gemini flights)
        • Mercury and Gemini – everyone working there, 350-400 working on Apollo but at the height, it was 400k
        • Management challenge to build the program
      • Here to find out about Mr. Johnson for Block 2 design (Houston didn’t know who was in charge by 1964)
      • First space module in August 1966 delivered for flight testing, behind schedule
        • Jan 26, 1967 with service module perched on top of an Apollo rocket
        • Sitting in pure oxygen for the flight vs testing scenarios (t-shirts, atmosphere at sea level)
        • 30th of January, killed in the first / explosion of the Apollo I rocket
      • Accident had been an awful wake-up call but no national clamor for stopping the program
      • Hatch needed to be redesigned, reduce oxygen while on launchpad, new fire resistant found, electrical circuitry adjusted
        • Heat shields and modules to be tested, Apollo II to be canceled, 21 months to Apollo VII
          • Backup crew for Apollo I was the crew for VII – phoenix patches and honor the first
        • Spent 11 days in space and go around the moon – testing all systems that it could, from engine to navigation
  • Matt Britton, CEO of MRY, Suzy (Wharton XM)
    • Media entrepreneur and consumer trends expert
    • Suzy is ‘Siri for brands’
  • Ryan and Andrew Beltran, co-founders of Original Grain (Wharton XM)
    425133_t810

    • Watch category, growing up in the PacNW and serving in the military (Marines)
    • Trying to find a product that he wanted to start a brand of
    • Going to China to see manufacturing and get ideas
    • Selling the first out of his backpack, initially, to military guys
      • Got buy-in on quality that they stood up but not a ton of traction
  • LovEvery – Love Every – Jessica and Rod, founding partners (Wharton XM)
    loveveryforweb

    • Jessica worrying about giving her babies the best nutrition, and curious about what the brains craved
    • Approaching research and deciding on toys

 

 

 

  • BERT (Bidirectional Encoding Reps from Transformers) (Data Skeptic 7/29/19)
    • Neural network with input arbitrary length of text – minimal form and characters
      • Output is a fixed length vector, numeric rep of the text – can do automated feature engineering for ML
      • Translation step for encoding for the machine using masking
    • Chatbot for question answering – wouldn’t do specialized tools for observe
    • BERT develops a general option (vs ML where there isn’t enough training data)
      • Trained on general knowledge, wikipedia corpus or reddit, etc… and apply transfer learning
  • Nick Maggiuli, Of Dollars and Data (Standard Dev 5/30/19)
    • Head of Data Analytics at Ritholz Wealth – data and interesting
    • Behavioral investor line test – being the 8th person in line and hearing others in Ash experiment
      • People purposefully tell you the wrong matched line and 76% of time, switches idea – changes vision in this case
      • Connecting to fake news in the realm of bias – pie chart that showed top 5 S&P 500 on right side, bottom 282 on left
        • Data just tells you the biggest 5 companies – may be just the 5 largest that represent a total share (consistent)
      • Crowd makes the narrative, often and then people agree and it becomes an echo chamber
    • Following crypto people despite not believing in it because they may know something that he hasn’t seen or know
      • Change minds based on some information. Trend following, for instance (price signal, 200ma – will stop working at times – Corey Hoffstein)
      • Doesn’t believe in technical analysis but has to be convinced by some information to make the jump
    • Blog post: Most Important Asset (host ran the survey) – bet that none of you offered every $ of Buffett wouldn’t want to be him
      • 5%, so maybe 3% are trolls. But he wants to live his life. Human capital and time is the optionality.
    • Best book he’d read about retirement “Retire Happy, Wild and Free” and doesn’t discuss money
      • Financial crisis isn’t the priority – it’s existential – what’s your time that you want to worry about
      • Some people could go to the beach every day and not care, others do differently
    • Trading his time for tasks and outsourcing things – working otherwise and doing it via his hourly wage
      • Anything you’d regret on your deathbed for missing things that you’d want to do – ends meeting, one thing but otherwise, go for it
    • Ken Fisher at Investment Conference (EBI with Barry and Ken talking)
      • “We have no market share” – 30bps as money to be managed out of $50tn when they’re $30bn
        • Could disappear and nobody would notice (except their clients)
      • Enough pie overall where they’re not competing against each other
      • Not interested in the discipline, so any general discussion is improved and bringing people in
        • Rise of politics and twitter probably keeps some viewers away but looking at competition and peers for learning
        • Brian Portnoy writing at the same time, sharing information and going back and forth with same publisher
    • Funniest fintwit: Ramp and Josh Brown, smartest Jim O’Shaunnessey and Jesse Livermore, MMT – “Trusts Cullen Roche”
    • Book that he read early in his career when he was bored – What It Takes by Ellis – best firms in handful of industries
      • If they ‘reject us, we made the wrong choice on the person so it’s good anyway’ – Korbath in legal
  • Morten Lund, seed investor in Skype (20min VC, 1/4/16)
    • Investor, co-founder including Airhelp, 100 other startups
    • Visiting university before getting kicked out – used computer to get premade direct marketing which wasn’t possible prior
      • Turned it into a digital ad agency and made it the largest in Scandinavia and sold to Leo Burnett (ad agency) as digital acquisition
      • Could build company by then
    • Made a small incubator by then with the money he had
    • Called for investments in Kazaa initially – wasn’t comfortable with that because biz model was for iTunes but no power to negotiate with labels
      • Was helping business development at the time
    • Guys had idea of doing Skypr – wifi sharing network – shut down by 10-15 investors who didn’t want to go further
      • Calls couldn’t be afforded so why not do a digital phone with the sound cards – helped fundraise and paid founders’ apartments
        • 300-400k users after 20 days launch – roughly $50k brought back $50mln
        • When it took off and worked, it was exciting – Estonia guys being crucial and understanding p2p from Kazaa, as well
      • Very involved in the brand – ICQ (impossible to understand)
    • Bankruptcy 7+ years prior had to refocus him and figure out what he wanted to do – nothing wasn’t working
      • Co-founding, starting and investing all kinds of 70-80 startups
    • Learning that things will take 3-4x longer and 3-4x costly
      • Founder in mind for admiring – David Hilge (Unity), Reid Hoffman, demonstrating stamina
    • Spending time at TradeShift – empty on cash and barely surviving holding onto his house – internet as media business that was fairly large
      • Every bank has a budget of $1bn in tech spend – immense amount of people running around doing nothing
      • Partners came to him to do digital invoicing structure for English structure and wanted to do consulting (agreed on cloud-based platform infrastructure)
      • Every company has different file formats and being consistent (Christian becoming a rock star) – ability to close huge clients
    • EU fintech community – browser era in 94-95 and nobody knowing how to handle it – legislation is getting easier to deal with
      • Web bank is a media but can do all kinds of interesting things with accounting – unwind IBM and legacy providers from cloud
      • If you want to sell big, have to go to US but if you want to do early or continue building, can be in the EU
      • Becomes obsession for $1bn level – consequence shouldn’t be this, though – not justified without revenue
    • Favorite book: Shantaram, fun with Richard Branson (knowledge exchange), The Economist as blog, Hippocorn – placeholder or executor affiliate

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)
    balderton-capital-logo-1

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

    • Purpose Built, 5/14/19

 

 

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

Push Ahead with Technology – Robots, Retail, Blockchain (Notes from Dec 17 – 23) January 10, 2019

Posted by Anthony in Automation, Blockchain, experience, finance, social, Uncategorized.
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First week back for most people. Hope it hasn’t been too chaotic. Schedule-wise, I was expecting craziness but it seems that everyone’s trying to adjust, so changes have been aplenty. Somewhat with a theme of resolutions and just personal improvement as I had time to mull and reflect, I had retreated a few months ago from my book-goal. So, that’s begun again, starting with Meditations by Marcus Aurelius, offered at Project Gutenberg.

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Then, I heard this morning, though I forget which segment it was, that there is a woman who writes weekly blogs for progress of weekly/monthly goals. That’s quite the inspiration! I’m not against resolutions, but the segment host / guest made great points in that they can fail often due to not being measurable (‘be happier’, ‘lose weight’ – measurable, but not succinct enough, ‘walk more’). So, accountability and something where you can SEE/REFLECT on how you’re doing is more actionable.

In light of that, I am going to make an effort to go back to ending my showers with 1min cold water (this morning: 1 for 1), read more (thru 3 books/chapters of Meditations), and attend at least 1 meet-up for the month (Smartly had one today but I’m going to attend next week’s in SF).

  • Scott Peters, SAM and Robotic Construction (Wharton XM)
    CR-Logo-278x80

    • CEO of Construction Robotics, talked about his SAM (semi-automated mason) that can lay 350-400 bricks an hour
      • No fatigue, just operation and planning goes into it
      • Ideal for commercial scenarios – large, long walls – set-up time
    • Specs for robotic-enabled to see if there’s a cost saving, time saving or profit margin for company
      • Work with customers to make sure it is feasible and makes sense
  • Doug Bewsher (@dougino), Author at Leadspace (Wharton XM)
    • Didn’t start as a journalist / author – was a CEO brought in for expertise for his marketing skills
      • Prior CMO Salesforce, CMO Skype, co-led McKinsey’s CRM practice
  • Caitlin Long (@caitlinlong_), former Morgan Stanley MD (Wharton XM)
    • Focusing on state-level support in the digital assets industry in Wyoming
    • Blockchain friendly bank – focused on that type of risk (more risk, so banks don’t want to enable)
    • Asset- and debt-based business and discussing blockchain as something different altogether
  • Stephen Sadove – former Saks 5th, Mastercard Senior Adviser
    • Data from holiday says that the Sunday before Christmas is the most busiest day of the year (ahead of Black Friday / Cyber Monday)
    • Shoppers in many aspects are increasing, except for say, electronics and big box retailers
    • Across the wave, and sections (“cozy” items)
      • Stores take advantage and share on the success

 

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