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

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

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

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

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

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

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

    • Future of Big Data with O’Reilly’s
    • Would take handle “5g” or something related for the future
    • Collecting, aggregating and normalizing big data now – business intelligence reports, simple averages or trends
      • What else can we do? Improve or automate processes/workflows or extract higher revenue from systems
      • Natural evolution and what are the bottlenecks for the AI / ML processes (are you early stages, models in production?)
    • Quintessential marketing for hype but developing a use case for the application
      • Tools for labeling data, data programming
      • Mentioned how to do ML with demo and user cases for interacting by Product Managers – SF O’Reilly conf
    • RPA with proper use case and proper implementation (close to the task)
      • Successful organizations have figured out how to bridge that gap – technologists with communication/collab on business side
    • On open source side, TensorFlow and PyTorch as top 2
      • Data science side – announcements about internal data science platforms to work together (share pipelines / features / models) openly
      • DataBricks, one company that he advises, also works on delivering enterprise data science platforms – IBM, cloud vendors
    • MLFlow as DataBricks’ for managing and tracking ML development life cycle
      • Monitoring alerts for retrain model, feature drift, deploy model against live data (simulating on live data but not production)
      • Model governance as tools that excite him – highly regulated industries like banking, financial services – what models and metadata
        • When was it last touched, trained, on what data, etc…
    • Managed services based on open source – managed Spark, for instance – minimal log in
      • If I have a better model but worse data, the better data should win, and that’s what drives competitive advantage
    • Big push toward hardware space – training at the edge or even model training in general – specialized hardware for accelerating DL / ML
      • More researchers working on data cleaning and data repair
      • Snorkl from Stanford researchers – easier for more people to use the product
      • Reinforcement learning – he’s most interested in UC Berkeley’s RISE Club’s REY (sp?) – distributed computation platform in C++ low latency
        • Building on top of REY as Odin – can cover 80% of Pandas, faster and other libraries
  • Frank, Chief Business Officer at Edge Sports/Analytics (Wharton XM)
  • Becky Miller, co-founder & CEO at Tinyhood (Wharton XM)
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    • Wanting to connect with other supermoms and doing a community
    • Deciding to do parenting classes online and helping subscriptions
  • Josh Phifer, co-founder at Barn Owl (Wharton XM)
    barn20owl2020rgb20_stagexchange_intro20pic

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

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

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

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

Sharing, Building and Community (Notes from June 24 – 30, 2019) July 16, 2019

Posted by Anthony in Digital, experience, finance, Founders, global, Leadership, medicine, questions, Real estate, social, Strategy, Uncategorized, WomenInWork.
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If you hadn’t noticed or read from the start, you should know that I’ve gone in order from the start on the Twenty Minute VC episodes. Many of the conversations are from 2015 and 2016 so they end up as a bit of a history lesson and window into the mindset surrounding capital / founding at that time. Bubble discussions or higher capital raises are comical when we now know what today looks like – higher, still. Although I’d say geographically, expansion has exponentially grown as it’s become easier and more common for firms to seek out what they really believe is an edge for them. Interestingly enough, people also don’t stay where they were, especially over a few years. As I look them up to refresh and review for these posts, it can be very enlightening to see where they are currently – did they start a new project? Open a new fund? Move on to different industry?

David Teten was one of those switchers, as he was with ff V/C at the time, and now over at HOF Capital. Similar role but perhaps more focused on what he’d like to see. Also, many of the firm founders and partners aren’t heralded for having more than a few roles – some probably for different reasons, but I look at that for myself as a way to stay extremely excited about continuing to learn every day, every week. To believe in yourself to be capable of doing great work, helping to create as much value as possible in the most efficient manner, hopefully. Weird that many people receive flack for multiple positions when many of us strive to follow a few particular, but potentially different, things. For this, I admire David in keeping up with his roles, while keeping us all in the loop of his thoughts, generally, as well.

Next, there were two involved in real estate, housing and construction that were pretty much aligned. What do I mean? Both believed the rising housing (and estate, generally) costs have come as construction costs have increased significantly. This is creating unduly pressure, and making it more difficult for projects to get done. Maggie Coleman went through real estate differences for certain types, while John Rahaim of SF mentioned precautions that are being taken for the coastline and bay.

Then, we had Ryan Hoover, who runs ProductHunt, and is one of my favorite follows. He built what he and his friends wanted – a place to share ideas and products to try. And 5 years later, here he is. I won’t spoil some of the nuggets that he shared, including a few of his favorite books and monetizing once he knew it was real. Not to be outdone, a discussion with Phil Southerland covered how he has built a strong community of athletes born or having diabetes. To demonstrate how they can grow, he helped form a team of all diabetics and professionally rides to bring awareness and improve the lives and conversations surrounding them.

There is important work being done by many people, including ourselves. If we can better someone’s life, it’s likely worth doing it if you’re enjoying it. We can help by just being who we are and doing what we would like to do. I’d love to hear some of your ideas or thoughts regarding the people covered in this week’s notes.

  • David Teten@dteten, Partner at ff Venture Capital, founder and Chairman of HBS (20min VC 095)
    logo-collateral-black

    • Alumni Angels of Greater NY, largest angel group in NY
    • How to Disrupt the Investing Business
    • Grew up in Marin Co, played with computers – consulted and taught Excel as a kid – bah mitzvah on knowledge test
      • Fired when he was 16 from financial services company because they figured out password was pw for admin
    • In college, worked briefly in strategy consulting before investment banking in tech – business school where he started multiple ventures in Israel and US
    • Joined ff VC in 2011 when it was 3 – now 27+ people, as largest headcount seed VC in Canada, Amsterdam, Israel, UK, US
    • Company ff should be generalists – broadest possible but don’t invest in life sciences due to no expertise there yet
      • Can’t predict in advance, so they want companies that are interesting with high growth potential
      • Admires outbound of TA or Summit, but something like SignalFire to look at data for high growth
        • Resources to help founders to reduce write-offs (1 in 6 fail for them) which attracts inbound (2000+ a year)
          • Filters down to ~12 a year to invest
    • Google Ventures / SignalFire and others as algorithmic approaches to source – increasing importance but not validated thesis
    • Loves their model as efficient – frustrations at other vc’s (80% of time with people / co you can’t invest in – partying and not meeting anyone)
      • Nobody at ff has a job as origination, Angelist as disrupting the generalist VCs (those that don’t have added value)
    • Top 3 Important Ways to Support Companies: capital raising, finance acceleration team – CFO acting, recruiting
    • How to determine value add as entrepreneur
      • Reference check
      • Do the math on portfolio: for ff, 60 portfolio co’s (active in 2/3), 24+ ee’s
        • How many people in the team? How many portfolio co’s? How many checks?
          • Use that to determine person-hours you can expect
        • What sort of technology platform to support the company?
      • A16z as huge operational side – finance, marketing, etc…
        • Short list for companies doing this – very capital intensive
      • Believes that there will be some shrinkage in the model in a downturn if it’s not fully thought out
    • Very illiquid asset class (mentioning to LPs) – 12 different academic studies for 18-54% median returns
      • 10+ years for cash returns, lot of institutions aren’t okay with that time range
      • David Swenson (head of Yale endowment) has argued long-lasting liquidity premium for illiquid asset classes
        • Even the most liquid asset classes aren’t liquid when you want them to be (2008, for instance)
    • Indiegogo as seed – crowdfunding space, competitors aren’t invested in – watch Angelist very closely, though
      • Services from Angelist as they look around at different parts of deal flow
      • Encourages member space to get involved in angel investing for next generation of companies – exposure to ecosystem
        • Promote economic growth
    • Research study: Disrupt investing – security, for instance
      • Secure (ID verification), Distill Networks (blocks bots), IONIQ (from Atl, secure cloud usage)
      • New processes and make them efficient: Addapar (Excel/PDF), Earnest Research (nontrad datasets eg: cc info, email receipt mining)
    • VC usage of social media: much more aggressive, judicious but no breakfast tweeting – sell
    • Edward Tufte – Yale prof, must read
    • PandoDaily as top blog – not afraid to make enemies, discuss what they do
    • SkyCatch as most recent – drone tech, set of tools for collecting data via drones – construction use case (Kamatsu client)
      • Monitor exact status of project, imperative they know where everything is
  • Maggie Coleman, MD and Head of Intl Capital at JLL RE (Wharton XM)
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    • Looking at different real estate measures – single family homes, for instance
    • Do different assets in real estate require a different measure to draw attention from foreign investors?
      • Didn’t seem so – would depend on where the capital was coming from (their own environmental basis)
    • Construction costs as outpacing many other costs – affecting many markets

 

  • John Rahaim, Director at SF Planning Dept (Wharton XM)
    • Having to adjust for the changing seaboard – will regulatory measures be taken?
      • No mechanical or living people in commercial buildings on the first floor, for instance
    • Says there are already precautions being taken on the bay – 5-8 feet, for instance
    • Construction costing so much already that it’s been very difficult to get building done post-land acquisition
      • Estimates of $600-800k for this due to inflationary and costs passed from the building companies
      • Said he had some 36? Projects that are being held up
  • Ryan Hoover (@rrhoover), Founder @ ProductHunt (20min VC FF026)
    ph-logo-3

    • Best community builder, Twitter engagement, winner of TC Best New Startup of 2014
    • Just wanted to build a consumer-focused thing to discuss with his friends about app ideas or companies
    • Put it on Quibb and Twitter – manual things initially, set up an email subscription, personal email connecting with people
      • Keep building communities otherwise it peters out
    • First iteration was RoR built over 5 day Thanksgiving holiday – core is the same, people using and community growth
      • Having people tell them and comment on ideas (used it as their new home page)
    • How to plan to go from early adopters to mass market?
      • Eager enough to participate, engage and not necessarily representative of the common users
    • Categorizing Podcasts and putting them on the home page – barrier before, now based on episodes
      • This Week in Startups, Startup Show (by Gimlet founders), Jake Gyllenhaal ep of Mystery Show (Gimlet media) – is he 6′ tall?
    • Worked on Hooked with Nir Eyal, using some in ProductHunt
      • Email digest is the trigger – action is to open / click on something to find inspiration or interesting
        • Built email variability – some consistency, surface different titles and content
      • Follow collection, clicking follow, reward is updates on the collections – permission for emailing to reinforce and come back
    • Ryan’s favorite collections: featured ones, Russ has game collections (browser ones), Julie created bakednight
    • Betaworks kept popping up on their engagement charts – Twitter very active, products (Without, where would he be?)
      • Always tagging the authors and being genuine, personable, funny or light-hearted
    • Monetizing PH – at its core, download/use/purchase products with the right intent
      • Over time, will explore more of this
      • Fundraising: first time for Ryan, different as a side project and growing
    • Quibb as newsfeed other than Twitter and also Crunchies where they announced Shipt but said “Actually, it’s PH”
    • Sunrise as calendar app, Pomodoro – no longer. Favorite book: Art of Game Design by Arty Shell
      • User psychology and game mechanics, how it applies to tech products as well. Game design not always thought about.
  • Brian Solis (@briansolis), author of Lifescale (Wharton XM)
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  • Survey on how social media has changed / warped views for girls
    • Hadn’t fully released yet

 

 

 

  • Phil Southerland (@philsoutherland), CEO of Team Novo Nordisk (Wharton XM)
    novo-nordisk

    • Living with T1 diabetes and bringing awareness
    • T1 and T2 can be helped, dealt with and he’s trying that
    • Cycling team focused on it
      • Full team is athletes with diabetes
        • triathletes, runners and cyclists

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)
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    • Discussed her mother, who had passed away at the age of 90 recently, who was told she couldn’t be an accountant
      • Wasn’t her role – she pursued it anyhow and ended up being an engineer before quitting and being a community leader
    • Worked in Gulf Oil’s accounting dept and helped her husband through med school
      • First councilwoman in her town, elder at the church
    • Deb started at Andersen until it folded, worked for only one woman but she was taught there were no barriers
  • Bentley Hall (@bhallca), CEO of Good Eggs (Wharton xm)
  • Mitch Berg, CTO of Vroom (Wharton XM)
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  • Alex Salkever (@alexsalkever), Vivek Wadhwa, authors “Your Happiness… Hacked” (Wharton XM)
    • With Stew Friedman, finding the middle ground of tech with children / teenagers and the happy medium
    • How is it that we find some things appealing but others are a burden
    • Facebook being a publishing agency – aren’t they responsible for what the product? “Newsfeed” example.
    • Google Maps or Waze as a hindrance at the local level – dangerous, maybe?
      • Extremely valuable, still, in new places / out of the country, especially
        • Different, maybe, for walking if alternative is talking and communicating with others
    • Problem with Facebook / Whatsapp – Whatsapp unmoderated group chats and only requiring a phone number
      • Encrypted, but what cost? Facebook – for Vivek, just limits to 1-way action
    • Social media as killing people – think India’s problems
  • Ed Sim (@edsim), FP @ Boldstart Ventures (20min VC 092)
    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

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

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

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

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

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

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

    • Looking at field – ML Engineer to Quant/Statistician to Product Analyst to Data Analyst
    • Research tips: Open source projects (understanding current trends, gain experience, make connections)
      • Job descriptions (take time to research the differences, tailor approach)
      • Market research (professional networking, connect dots between companies you’d consider)
    • Resume tips: Concise (focus on telling a story on the experiences to highlight outcomes)
      • Factual (if listing skills or strengths, use examples to support them)
      • Related experience (highlight specific projects related to area you’re applying)
    • Networking tips: Professional profile (be detailed but concise about the skills you use and experiences)
      • Targeted outreach (connect after a conference, target approach for conversation)
      • Conferences (meet/greet if possible, follow up via email, LinkedIn, twitter)
  • Gidi (Gideon) Nave (@gidin), Assistant Professor of Marketing (Marketing Matters)
    • Cambridge Analytica before Cambridge – music research and how it relates to certain traits
      • Extroversion and openness were 2 big ones that they could pull from 5 traits (MUSIC)
      • MUSIC: Unpretentious, Sophistication were 2 of them
    • Could pull personality cues from 20 second, unreleased clips based on scores of 1 to 7, also
      • More agreeable people had higher scores in general
    • Personality on 5 (OCEAN – Openness, Conscientiousness, Extroverted, Agreeable, Neuroticism)
      • Questioned whether they could use music to test for the personality (as opposed to the other direction)
      • Personality is established at a young age, so can music likes on Facebook give you a personality side – as mentioned, it did ~2 better than others
  • Fintech for Startups and Incumbents (a16z 4/7/2019)
    • With GP Alex Rampell (@arampell) of CEO/cofounder of TrialPay and partner Frank Chen
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    • Assembling a risk pool (good and okay drivers subsidizing the bad drivers, or healthcare – same)
      • No economic model for skipping a segment – psychology for half price insurance (say, going to gym)
      • Half the number of customers – taking the ‘good’ ones, profitable ones
      • Insurance has mandatory loss ratios for different industries
    • HealthIQ – mechanism for exploitation on ‘health’ – in FinTech, it was SoFi on HENRYs
      • Positive vs adverse selection – debt settlement company ads, for instance – negotiate on your behalf to settle
      • Healthier people as living longer than non-healthy people – left them more profitable for proving being better
        • Gives them good customers (adverse selection for ‘quick, no blood test, 1 min’)
    • SoFi as stealing customers from the normal distribution – better marketing message “you’re getting ripped off, come to us”
    • Branch as investment – collect as much data as possible and look for correlations – small, mini-loans
      • Induction as pattern is a willingness to pay (credit is remembered) – went and got data from your phone
      • How many apps did you have? Did you look like you went to work? Are you gambling?
        • Counterintuitive potentially: battery goes dead leaned default, gambling app meant more likely to pay, etc…
    • Earnin – phone in pocket for 8 hours, last paycheck and RTS data confirming – will give money that you have earned but don’t have yet
      • Can tip interest or not – can you encourage people for positive community and people driving safely
      • Nurture better behavior – helping to turn customers into correctly priced customer (vs bank that doesn’t want them)
    • Vouch as company that failed but your social network had to vouch for you – Person X is okay, so you can even put up $
    • Tiffany & Co for a long time was owned by Avon lady – but its brand was massive and one of most renown jewelers
      • Could make sense for acquiring more customers, though
    • Killing Geico – take 20% of customers but only take the good ones
      • Selling negative gross widgets, for instance – probabilistic ones (and the bad ones aren’t needed)
    • Turndown traffic strategy – Chase turns down a lot of people for problems (can’t profitably do $400 loan, for instance)
      • Here’s a friend after they rejected them (but see traffic) – Chase will tell you to go to a startup for better underwriting
      • Amazon got right – HP book, for instance – had ad for B&N right next to Amazon (bought) – would make $1 on the ad click at 100% profit
        • Used this to reduce the price on their site and wasn’t sharing it
    • Rapid fire: “Always invest super early” – 9 weeks to decision vs 1 day – can’t get good deals at length
      • Best things aren’t cheap – they’re often expensive – better strategy can be plowing in late (“Can’t believe we’re putting this much $”)
      • Gating item for entrance into a space or into different models – cost of capital and distribution as often the unique thing
        • Geico could easily add additional traffic to start-ups
      • M&A strategy early? Encouraged and used Facebook – buy existential threat (surrender 1% of market cap to buy Instagram)
        • Facebook spent 7% of market cap for WhatsApp, Oculus, etc…
        • Buy the guys that failed trying – courage to build something new -> take them and put him in charge for person that was successful (at big co)
          • Trying to build this thing for ~10 years vs start-up that built something in 1 year (put this one in charge)
          • Ex: AmTrak buys Tesla – worse thing “You work for us” but you want products to push distribution and talent for understanding
        • Only difference was distribution and the possibility to do that
  • Jennifer Dulski, author of Purposeful, Head of Groups & Community at Facebook (Wharton XM)
    • Talked about their group initiatives at Facebook – communities policing themselves as well as methods to flag content
    • Mentioned example of having an employee that came up to her and asked if she had done a good job, she just wanted a bonus or something $
      • Taught her about incentives and why people do what they do / good to know the motivators
      • What drives people?
  • Anth Georgiades, CEO of Zumper (Bay Area Ventures, Wharton XM)
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    • Purchase / hire of Padmapper in 2016 that added quite a bit of Canada business (size of California, real estate-wise)
    • How to match both sides for a marketplace – suppliers vs customers
      • Chicken and egg – focus on one, improve other and repeat
  • Word2vec (Data Skeptic 2/1/2019)
    • Produces word embeddings – autoencoders as NN for something like compression to retrieve output successfully
      • m down to n via mathematical representation (m < n)
      • Language compression for vector rep
    • Running the algorithm training on Google’s full internet, Facebook’s news article, Wikipedia, etc… to achieve similar words/spaces
      • Not super adaptive – nonsense place for words it hasn’t seen
    • Real world application – king for word2vec and subtract male – then add in female and you get queen
      • 300 dimensional space, semantics of that example
      • Bad example: training on entirety of internet results in something like doctor – male + female = nurse (gender neutral data)
    • Feature engineering for bag of words, good example for transfer learning, also (train model on text and then use parts of it on smaller area)
      • Very large corpora for NLP but can use pre-trained models of word2vec and use it in other models
  • Sean Law (@seanmylaw), D/S Research and Dev at TD Ameritrade (DataFramed #59 4/1/2019)
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    • Colleagues thinking he tends to ask lots of interesting, hard ?s – hopefully with answers
    • If he’s a hard worker, then he’ll do great – being in industry for 3 months time – has to juggle effective time spend
    • Molecular dynamics is short time scale and lots of computing power – parallel computing before and now the growth / usage of GPUs within days
    • Hypothetical example for alternative data solutions – driving to work and listening to NPR where NASA had a new dataset that was sat imagery
      • Pollution ORA dataset for air quality – area of high commodity necessity with pollution joining
    • If building ML as a binary classifier – but don’t know where the data is (do we have to collect? 3rd party API? Internal?)
      • How much effort to get it usable in the pipeline? Then, what’s the reasonable accuracy level – better than 50-50?
      • Some signal in the noise
    • Exploring chat/voice – query account balance, stock price, news articles via Alexa/Echo
      • Headless / device-agnostic option – audio to parsing of text, understanding what customer wants (NLP) and then what it means
      • Following PoC and into production
      • PoCs can miss: scalability (unless claim is to get scalability), model accuracy (not best model immediately), real-world applications (use case in mind)
    • Interpretability standpoint – regularization, L1 and linear – constraining coefficient can be very useful (background noise from video, for instance)
      • Time-series pattern-matching as non-traditional
    • Calls to action – data failures of things that didn’t work or negative results

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

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

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

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

Steve Mast, President and CIO at Delvinia (Measured Thoughts, Wharton XM)
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    • Using Methodify for geolocation data / surveys
    • Digital tech to help marketers, researchers and leaders collect, visualize and enable data
    • Educated as an architect, then video game designer and producer in the 1990s
    • Joined Delvinia in 2000 to build interactive design and digital marketing
      • Talked about doing events where they get volunteers to sign up for brand / marketing analysis
      • Ask 2-3 questions that are pointed, geo-enabled for brand / important points at the event
      • Makes sure not to have personal identifiers
  • Joseph Jaffe (@jaffejuice), author Built to Suck (Wharton XM)
    • Admiral, co-founder at HMS Beagle, strategy consulting for surviving
    • Talked about how Harley Davidson is in every marketing book but what are they doing now? Floundering
    • Nike ads – never talked about the product (shoes), but call to action – Just Do It
      • Nike as providing the tools for which you act
      • Used their stores as ex of environments for their product – having treadmills
        • Each employee was a runner, wearing Nike and touting the products, experts
    • Remembers asking his class if they knew the first bank to implement ATMs
      • Didn’t provide the answer – jumped into 4 P’s – one student asked what the answer was
        • Answer was that it didn’t matter because every single bank mentioned had ATMs
      • Only thing that mattered – first-mover’s “advantage” if you can keep it
      • “What are you doing now?”
  • Chris Albon (@chrisalbon), Getting First Data Science Job (DataFramed #55)
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    • Data Scientist at Devoted Health, helping to fix healthcare system
    • Co-host of podcast Partially Derivative, since stopped, and had a kid / moved
    • Humanitarian non-profits, working on team for building companies with a soul
      • Devoted – health insurance company started by Todd (CTO of US) & Ed Park (CEO of health company)
        • Creating company that you’d want family members be a part of
        • Make healthcare that works (primarily senior citizens, Medicare)
    • His background is from quantitative political science – politics and civil wars
      • Perspective of research, experimental, statistics – PhD with these fellows
      • Meeting friends with a ton of amazing, applied projects (LinkedIn, etc…)
      • He needed to be applied vs research in order to get out of academia – joint Kenyan nonprofits (election monitoring and disaster relief)
      • Real data or fake reports, safety, ethic and morals come up – threat models aren’t the same
    • First hire at Brick (free wifi to Kenyan homeless, etc…)
      • Using established tools to provide others data / analysis – for a team to not know that going in, it was impressive (wizardry)
    • As a team, you can hire and absorb senior data scientists
      • People who got first time jobs at Facebook or something, got to see scale and experience that they can move on easily
      • At a Facebook/Google, end up doing heavy data analyses for the massive scale and is a big role
        • Hard, analytical challenges
      • Smaller companies may ask someone to do a ‘full stack’ / general data scientist that has to build everything on their own
    • Early on in hiring process – ex with Master’s in ML, and that’s what you want to do
      • Generalist builders at Devoted, but not strictly ML or other thing
      • Heavy AI or ML would be theory-based, dissertation level technical discussion (obvious focus)
    • Doing data science generally – many other problems – Bayesian analysis, RF, etc…
      • Far more jobs for those that are generalists at companies for business data – predicting drones watering crops, customers churn, illnesses
    • With different backgrounds, should figure out how to feature yourself & experience
      • Side projects, blog posts, portfolio, visualizations in a way that’s easy – testing, GitHub, versioning
    • Talked about his first meeting at Devoted Health – 4 data scientists in the room with a doctor, discussing the coding of health / diagnosis
      • Said he was fascinated in the meeting as he wanted to know that side, new business
    • He genuinely enjoys new techniques, analysis that he doesn’t know and learning about it – passionate about what they are and learning
      • Not hiring for junior – it’s because you will want to grow into senior
    • RF > SVM since it works out of the box, but said SVM is an awesome mathematical tool
      • Used it as a teaching point and visual – but in production, he’d never seen it
  • Eric Winston (@ericwinston), President of NFLPA (Wharton XM, Leadership in Action)
    • Talked about how important relationships and the soft skills were
    • Financial literacy as a passion of his – talked about how little players know going in, especially after college
      • College finance doesn’t teach it, either
  • Austen Allred, Founder/CEO at Lambda School (20min VC 3/8/19, FF)
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    • Bedrock, GGV, GV, Stripe and Ashton Kutcher as investors – $48M so far
    • Prior, Senior Manager for Growth at LendUp and co-founded Grasswire
      • Income inequality, financial health thoughts – nothing was moving incomes
      • Was in a small town in middle of nowhere, Utah
    • Had to live in his car in SV for a while and figured out how to schedule – during summer, would get hurt obviously
    • Raised $500k initially, couple months of cash left, due diligence – investor decided to not continuing Dec 23 (daughter was born soon after)
      • Never wanted to be in that position again – thought it would’ve been VC but it was more about a successful business
      • At YC, wasn’t focused on demo day – modeled 2 scenarios: 1 with VC money vs otherwise going wrong and seeing no VC money didn’t work
    • About the right time to raise: $1 today would be $3 or $4 later, still had much of their series A – getting dozens of VC emails and say no
      • No goal to raise B at that point, walked through the numbers with Jeff (one of investors) over dinner
      • So Good They Can’t Ignore You (Steve Martin quote, but Cal Newport book)
    • Looking at product-market fit – people would pay whatever to get to the job / signal
      • Incentives aligning, job and person – $1000 to start and pay after getting a job: Got into YC and thought no upfront deposit, etc…
      • List of 7k people, trying to refine and make sustainable
    • Training people online was tough, free upfront / no SITG – no Bay Area / NY, online engineering students
    • Iterating on all facets of business so quickly: had to do it, quickly and concurrently
      • Each 5 weeks do a project, roll people together and do an app – if they can’t, roll it back
      • “Insane” – but more people just can’t fathom DOING, the ACTION
      • Before running the experiment, they determined the metrics for success and failure (if it doesn’t happen, fail)
      • Career coaches / meetups / staff bonuses for people trying to get people hired – success of those 8 trials
    • Wright Brothers biography book and Les Miserables (humanity)
    • Changing SV – fundamental human problems, he wants them to build more, try more
    • 500k students in the year for 5 years goal

Engineers, Research & Starting Up! Also, Women in Work (Notes from Week of Nov 12 – 18) December 4, 2018

Posted by Anthony in education, experience, finance, global, questions, social, Uncategorized, WomenInWork.
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Hello all! Hope everyone’s Thanksgiving went well and the start of December hasn’t been too cold. By California standards, we’re freezing. Literally only for a few minutes (frost warnings) and no snow in the bay area, but we’re used to much warmer weather. It’s delaying my morning routine. May have to zoom to Chicago and colder weather this weekend just to come back and act like it’s warm (comparatively). It’s definitely part of my hesitation in doing client engagements east of California.

The past few weeks were busy! Now that I have my Surface up and running again, though, we’re back in business. The week of notes came from a variety of people, primarily researchers we’ll say, who dove into businesses that sprung from that work. What did they learn in their time as operators? What did they learn in building / searching for funding?

Later in the week, as I was driving back to the Bay Area and attempting to avoid smoke inhalation, I caught the end of a women at work segment on Wharton’s Business XM channel with George Yancy (author of #MeToo article) questioning the foundation / construct of our environment that’s fueled the movement, as well as a very quick (but didn’t catch the name) piece of SheEO and HeyMama founders/members (?) that I’ll need to search further for. The women there were entrepreneurs that wanted a community of like-minded successes juggling family, life, work and everything in between. I bookmarked it as something to pass along to my sister and those I know who are questioning that juggling while venturing into family life.

Let me know what you think!

 

  • Fabrice Grinda, Super Angel at FJ Labs, BeepBeep (20min VC 072)
    • 200+ investments, start-ups, FJ Labs, former co-founder of OLX
    • Treats himself as an entrepreneur vs investor – built ebay-type company 18 years prior (late 90s)
      • From start, other entrepreneurs would come for advice as he gained traction and notoriety
      • Increased volume and pace of investments at start of 2000s (up to 20-30 in 08-09 and more)
    • No time to sit on boards – 200+ investments
      • Few % of ownership, don’t often lead rounds – lead VC A, seed and maybe a later one (3-5 board members only)
    • Deal flow from all kinds of people, LinkedIn, websites, etc
      • Has invested in 200+ companies which lends them to 500 founders and they can talk to them that way
      • Other countries where they may be able to invest, as well
    • Mentioned his 2014 book of What If?, Think Like a Freak
    • FlexPort and freight forwarding where B2B platforming, product people inline with end to end consumer bases
  • From Research to Startup, John Hennessey – chairman of Alphabet (a16z 10/6/2018)
    • RISC – sentence with hard words vs clear, precise English for faster computing
      • IBM and Deck in 1980s, first thing you had to do to find info was go back east and ask
      • Early 1980s initially invented architecture, dominant nearly flipped in late 1980s
    • Server cost is first, then the power or energy cost is next
      • Embedded space was the one where RISC made a breakthrough because of computing necessary
    • He was a reluctant entrepreneur (had a paper and figured people would take it and run – they didn’t because it didn’t sell east)
      • Gordon Bell came to him and said John would have to run a company
      • Said he thought engineering should get 50% of revenue (quickly learned sales needed them)
      • Selling to people was easier than to give it away and have impact
        • More you charge, more successful the implementation – people will have to commit
    • Had cut from 120 people to 80 and then give a TGIF speech on being a “great company”
      • 28% of Stanford’s endowment disappeared in financial dot-com bubble and meant the company couldn’t spend how it had
      • Decided a big cut to lean out was the method to continue forward
    • Technology licensing is like extracting blood vs being partners with the entrepreneurs (unis should get tech out there – be respectable of faculty, as well)
      • Wider range of experience and students will typically go into industry vs education
      • Universities are the hub of innovation starting – Silicon Valley elsewhere
        • SV has gotten larger over last 15 years (he said, no doubt, China is 1)
    • In leadership, humility as important, as long as you maintain ambition
      • “If I show weakness, my people will lose faith in me” – humble with a decision made
      • Talked about expanding Stanford (and now, changing education) – “Everyone should watch more Shakespeare”
        • How to leverage technology to get cost of education down, otherwise more and more expensive since they’re less able to save
          • Bryan Caplan’s “Case Against Education” (7/8 of education and out, don’t get 7/8 of value so the value is in the signal of the finish)
        • Interdisciplinary and how comp sci is a meta-discipline (algorithmically thinking)
    • Has been a shift from research at universities to industry, driven by data at Google, Microsoft, Facebook, Amazon
      • Prior industry research with IBM, Bell Labs pre-1980s, they had long-term driven research because they were all monopolies and could afford it
      • Waymo winning self-driving car with DARPA project was tipping point, Cisco as a different form of acquiring businesses with interesting tech
        • Spin-ins that have been immensely successful (send team out to develop, build a company, and then bring back in)
    • Computer Science and Women in the Business
      • 1980s they dominated the field until it absolutely blew up in the 1990s, and now it’s getting closer to critical mass
      • Tools are much more sophisticated and being able to learn
  • Author of #IamSexist article, George Yancy, prof of philosophy at Emory (Women@Work, WhartonXM)
    •  Author of Dear White People and the #IAmSexist articles that tried to deconstruct some of the inherent biases that many people grow up with
    • Clearly a majority of people aren’t blatantly racist or sexist, but rather it’s a construct of our environment that we’ve grown up in
    • He declared himself an Antisexist Sexist (has to fight his notions each day)
  • SheEO  & HeyMama (Wharton XM)

    • Community of empowering mothers / women in business, connecting them to discuss their problems/solutions juggling successful careers with life
  • James Borow, Chief Product Officer at Brand Networks (20min VC FF013)
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    • Planned on being a lawyer – was at Vanderbilt, took off a year before graduating, and worked for an internet company
    • Deferred Georgetown (de-risked because he could do this), eventually met his co-founder of Shift
      • Created Buzzfeed before Buzzfeed (GirlsGuideToo?)
      • Had to programmatically get advertisers onto the social platforms before it was accessible
        • Educating clients in new market as difficult – reached a whole new audience as part of value pop
      • One marketing platform for all stakeholders and across all social networks (now vs then)
    • Make bets and invest to grow, mentors to help – not looking to reinvent the wheel (but can waste years figuring out your way)
      • Giving up equity to people for help – want investments and a piece to motivate them to help
    • Approached by Brand Networks because they were better at content – James’ team better at payments
      • As product focus, he can pay attention to things he wanted to do in the past
      • If weak in finance or product or anything, tell cofounders/advisors and get help
  • Karl Friston, Wired article
  • Shane Parrish

Notes from Hirschhorn & Cuban March 27, 2017

Posted by Anthony in experience, finance, Politics, questions, social.
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Listening to the Jason Hirschhorn interview with Mark Cuban  from the end of February (just pre-$SNAP IPO) —

Many great resources in all the current tech-hubs: SF & Silicon Valley, Los Angeles, Austin, and expanding those. Cuban makes a good point that people and ideas are easily created now in almost every area. There are places in the country that have MORE resources — events, companies, VC’s, funds, but building can be done everywhere (Cuban mentioned when he visits IU, he can stay in contact with them).

With less and less companies going public (mentioned ~9000 publicly listed in 2008, but < 4000 now), people are either scared of going public, or are getting their payouts directly from bigger companies (Cisco, Facebook, Amazon, Microsoft, Google, etc…).

Digital ad revenue for FB and Google – 85%+ market share. NFLX and AMZN are 2 biggest shares – hasn’t sold yet. Content providers – Disney, Netflix, and Amazon…. not many others. CONTENT is very difficult (Cuban mentioned Enron doc and winning awards, along with Good Night and Good Luck — hasn’t done any successful since). Content is the most difficult to maintain – very difficult to get past that giant hurdle, and these companies have the money to get above it.

Eventually got into a political discussion – using news / reactions / tweets to respond. HOW do we respond? Communicate and be patient – tough to change minds or reason – noted 52% of eligible voters didn’t vote. Trolls and dealing with internet comments – control public/private responses on twitter? Twitter must be hard-coded otherwise. Cuban mentioned an app that he’s going with – soon, machine-learning or machines will deal with the curation of information and conversation in digital platforms.

Talking about video – 7 year old son wanting to play flag football / baseball and how different it is now. Esports / watching vs watching tv (sports). His son didn’t want to watch sports / baseball / football, but wanted to play. There’s no indoctrination or religion for it anymore as we grew up on (and Cuban’s era earlier). Gaming as a big advantage in expanding NBA reach – NBA 2k and professional aspect of them since players have a deeper involvement / knowledge of the league with gaming.

The overall theme for today (not just this interview) – how can we get more young people interested in building out great ideas? The future of technology is rapidly accelerating but ideas will still be needed from the smartest people. Education seems to nerf expansive ideas – boxes people in that may be more capable, restricting opportunities. In my opinion, this is a huge flaw in the system overall.

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