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Listening and Encouraging (Notes from December 16 to Dec 22, 2019) May 21, 2020

Posted by Anthony in Automation, Blockchain, Data Science, DFS, Digital, experience, finance, Founders, Gaming, global, Hiring, Leadership, NFL, NLP, RPA, social, sports, Strategy, Uncategorized.
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Sometimes it doesn’t work. Asking the right questions to people in conversations to get a sense of what they’re truly passionate about gives me hope for those that may eventually try something different, new. However, unless I followed up repeatedly, most people let their passion slowly pass, or just remain in thought.

This is a big part of how I learn, engage and stay passionate for the things I’m curious about. Other than being scared of stagnation, hearing people come up with ideas, test them, build and hopefully succeed repeatedly gives me an energy to try to convince others to do the same. I understand the difference between being told of something that has been mulling around in someone’s head or even light discussion among friends compared to prototyping or validating with potential customers or asking people in the field if something’s viable.

A few examples of ideas people have told me they wanted to start and hadn’t (yet some that I believe have done well, just have room in the market) include an HR in Tech stories podcast, traveling medicine / tourism aggregator, and a d2c ecommerce diamond shop (which I’ll go into more detail), more social podcast sharing among friends, and still a market-taking happy hour app (yes, I had to insert my own – I’m leaning toward Glide.app through Google Sheets).

For diamond shop – this was by someone who graduated with entrepreneurship degree, had a validation for the idea and then was told by others it wasn’t worth doing because it’d be high cost. Granted, that was a few years ago, but it would’ve been hackable then. It’s certainly easier now with ecommerce shops via Facebook/Etsy/Shopify and other support, not to mention the audience you’d be in front of. The premise is that a diamond historically took the role of what a pearl represented because of the hardness – you could pass this on as an heirloom to further generations, and you know it won’t be breaking. It’s yours. There’s a legitimate attachment there that defines a core part of the worth/value. For the idea – it’s increasingly cheaper to 3D print a model you can build/customize on CAD (or related tools). This would be printed in plastic that can be melted to be replaced by silver – these rings would be sent to customers that are ordering (possibly with a small down payment / shipping covered, ie $5-20). It’s a model of what the ring would look like, just without the diamond part – but as far as sizing/size/bulk and the other key parts of the ring, customers can try them on and feel it. There’s an emotional attachment here that should occur. If they’re loving it, or have requests for changes, they can do that. Possibly a back and forth could take place, but once it’s settled, the wax/plastic mold can be printed as they would normally do a custom ring and use the materials that have been requested. We’ve removed the in-shop aspect and made it personal, simply by removing much of the fixed costs and labor costs that would go in to this. She was an expert in jewelry and had years of experience. Someone just told her no. 3D printing is now a hobby and can be done there. Many jewelers have other shops do the molding. I’ve been thinking of helping her start by just simply creating a mockup of the site. Can certainly figure out the rest.

Anyhow, let’s see the notes.

Week of December 16, 2019

  • Tyler Willis (@tylerwillis), angel investor (20min VC 2/16/16)
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    • Raised with seed companies at Index Ventures, Founders Fund, Khosla Ventures
    • Port co’s include Lyft, Patreon, Change.org
    • First co that he was on founding team on was acquired by Oracle, then had a friend raising a seed round for concept in CV
      • Preproduct, premarket where he did a small investment (decided it was bad to keep all eggs in one basket)
      • Decided to invest in Patreon, Loungebuddy (Airport lounges) and ShopApp inside of Shopify
    • Rocketship – valuation doesn’t play a role but ID opps for big (10x path, seed > 10k)
    • Customer acq and growth as a lightweight process to get a core part of the company
      • Optimizing for experiments – 1 week to test compared to 8 week deployment
    • Founder type – uniquely insightful to the place they’re in
      • Bias for people when he can sit down and get a high-octane thinking / smarts – hard to hang out to the rocketship
      • False dichotomy of domain expertise – could have learned wrong lessons or may not know anything in enterprise, for instance
    • East of Eden, Innovator’s Dilemma as great books
    • Favorite investors – Naval, Sam Altman, Gus Tai at Trinity Ventures
    • Favorite app – Omni (stuff storage), Delectable (learning about wine)
  • Ash Fontana (@ashfontana)- GP Zetta, Leo Pelovets – GP Susa Ventures (Venture Stories 12/17/19)
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    • Getting pricing power – need to find balance between incentivizing founders and price, but not a big deal
      • If they get 80% of company, 20% for founders – may not keep them looking ahead
    • Company and VC differences – companies have different roles but VC has very similar, solitary roles
    • On non-investing side, COO or Head of Ops to run operations but not particularly CEO or investing side needed
    • Working with best founders, LPs aren’t as important (but they are the primary VC customers)
      • None matters unless you have results for LPs and providing value – founders need the platform or help
    • If you were an LP, what would you want to be in: YC, First Round or Benchmark?
      • YC for Ash – lots of opportunity for capital deployment at many different levels
      • Benchmark for Leo – very large differentiated returns, ~30x according to Leo (YC may be 5x-7x possibly)
        • More variance because of smaller portfolios in Benchmark
      • YC may be beatable but it would be in losing their way as a general accelerator
        • Ash brought up operational risk for LPs – more points of failure because of all the touchpoints
    • AngelList as trading to be profitable and dynamic system for new things
    • LinkedIn as insurmountable lead in enterprise/business space of social network (as opposed to consumers)
      • Hard to disrupt with multiple verticals
    • Requests for startups: data generation/building data (synthetically) – ex w/ params
      • 10k examples of chairs that are brown that have 4 legs, in low light, at this angle
      • Weather climate, also
      • AutoML – making it easy for non-specialist engineers to experiment with ML
    • Leo Requests: ISA with bundling with coaching, training, VISAs – realigning incentives
  • Ben Tossell, founder Makerpad, Sahil Lavingia (@shl), founder GumRoad (Indie Hackers 11/11/19)
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    • No-code vs code – building a solution to a problem without being technical
    • First web-sites like Dreamweaver and tables for no-code – like WYSIWYG
      • Halfway things like WordPress where you can customize or use framework
      • Building a newsletter, can use Substack, for instance – Marc Andreesen
    • Sahil’s opinion that we’re unlikely to see a billion dollar start-up without a code base
      • But likely to see many creatives build on their own, have the options
    • Choice of no-code compared to code – using Circle as their integration testing methods
    • Nontechnical founders that had cofounders for developers or finding for cheap
    • Ben as bringing up Lambda School (Airtable, Slack, Zoom, Notion) and Makerpad member who was just starting to say it’s breaking
      • $150mn in Series A to get to worry about things breaking first
      • “What’s my Airbnb version look like?” but should focus on the first $10, 100 before there
      • gumroad-logo-retina
    • GumRoad as being built in a weekend – not competent enough for him to do no-code
      • Ben argued it was easy to do in no-code but they’re each discussing the same thing from different experiences
    • Queries on data for code – tools like Clay/Retool where you can work together – can run queries easily
      • No-coder does query and can recognize it to manipulate
    • Powerful for on-code is git and version controls – clear log of security, feedback, quality of code
      • Apply it to other things – pull requests/merge (conflicts)/conflicts in document setting on Notion, for instance
      • 100+ tutorials in MakerPad now – what’s interesting or grab attention
    • No-code as Patreon/Cameo/Airbnb/Uber where the overhead for coding sucks so much value from (Patreon at $30-40mln burn)
      • Creator would be interesting with price-motivating factors because you could have a more affordable option
      • “What’s the point of trying if I can’t even get to the ceiling?”
      • Meetup clone – need the “this is how you build it” – go look at the tutorials
    • Not enough answers for “Where can it go?” because they haven’t seen enough
  • Niccolo De Masi (@niccolodemasi), CEO and Chairman at Glu Mobile (20min VC 2/18/16)
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    • Kendall and Kylie game (#1 at app store at time), Kim K game, Deer Hunter and others
    • Was CEO at Hands on Mobile as well as CEO at Monstermob Group Plc
    • No money to be made in games in 2003 because they were for feature films, polyphonic, true tracks and he ran a public co before selling in 2007
      • Raised money to get bid for Glu from Hands on Mobile but got a call from Egahn Zander to transition to paid from f2p as CEO
      • Original IP value with games specifically for mobile on hardware
    • How will you make money in late-stage startup for future? Next year or two vs past.
      • Forward looking and professional managers – no founders anymore. Built from 350-850 people.
    • Moore’s Law as quite predictable but believes there are different models, utilities, and price models
      • Last gen console power in pockets now
    • Barometer of quarterly calls driving placements and interim 6 week calls for how they’re doing
      • If transparent in bad times, you may have quick punishment vs window-dressing
      • Rewarded more quickly in the upside, as well – private markets vs public markets
    • New startups as worth more than incumbents – bay as more regular here
      • Well ahead in private markets compared to public markets (his counter – at least they have earnings)
    • No BD or CorpDev – scour market and wait for inbounds of compellingly priced assets (often distressed), significant private markets
      • When Glu is $6-7, they can pick up companies easily but not so much at $2-4
      • Savings to be had for core customers when they have scale within Glu (mentioned PlayFirst)
      • “retirement community for young people” – startups subsidized, food, clothing and sharing app
    • By 2020 – more discipline in different sectors potentially – overvalued will have to come in line
      • King that was acquired by Activision Blizzard – consolidation forced by VC funding and people flow
    • Better to be #1 in smaller market than #10 in a larger one – be great w/ you’re good at
  • Tim Draper (@timdraper), founding partner at Draper Associates and DFJ (20min VC 2/22/16)
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    • Original suggestion for viral marketing in web-based email to geometrically spread an Internet product to its market
      • Standard marketing technique now
    • EE at Stanford before going to Apollo Computer as assistant to President before HBS
      • Came out and wanted to be a VC (grandpa/father both were VCs and didn’t want to do it) – wanted to be a consultant / cheerleader
      • Helped him having an entrepreneurial base but some can certainly do it if it’s your goal
    • Borrowed money from gov to get started – knocked on doors with software on them
      • Most VCs needed others to help fund a company so they worked together – moreso now for angels, but not necessarily VC because of money
      • VC has gone global and has enjoyed that expansion – affecting the whole industry
    • His son’s accelerator, Boost, focusing so they can accelerate any business – he enjoys investing in 2-3 people with a good mission
      • Get people set up in the right way – medical, eshares, network accounting, and other operational methods
    • Favorite pitch – Nicholas Zenstrom at Skyper – most smooth, effective way and he’d agreed before calling and changing business model
      • Enthusiastic, quiet confidence for the enormous successes – Robin Lee (Baidu), Hotmail’s founder, Martin Everhart (Tesla)
    • Draper Uni of Heroes (entrepreneurs/founders) creating school during crash for better people
      • Give these people the confidence + tools while ridding them of shielding
      • DraperUniversity and StartupU – great marketing for school
    • Bitcoin interesting for a year ahead of the time, and then post-Mt Gox hack it went down only 20% so he jumped in
      • Micropayments, fees in journalism and podcasting as well as ending credits and cross-country
    • Enjoys hearing Andreesen, Moritz, McClure at 500 Startups, Plug-n-Play as first incubator, Ron Conway
    • Reflects on The Startup Game (his father’s) and Rothschild’s Bionomics and concept of evolution of econ and bio
    • Recent investment Laurel & Wolfe (interior decorating as best furniture for crowdsourcing) – closed update Dec ’19
    • Also invested in Favor, marketplace food delivery – acquired by HE Butt Grocery
  • Brandon Deer (@bdeer26), VP of Ops & Strategy at UIPath (20min VC 12/20/19)
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    • Using RPA combined with business processes for automation
    • Using Gary Kasparov’s loss to IBM in chess before saying it’s no longer a chess or human – combination where average + average is optimum
    • Having growth and breaking things
  • Wharton Moneyball, Ken Pomeroy and Brian Burke (@bburkeespn) (Wharton XM)
    • Discussing the biggest predictors, NCAA basketball or in football
    • Pomeroy and how he’s adjusted his football predictions

When Innovating Away Staleness (Notes from Nov 18 – Nov 24, 2019) February 25, 2020

Posted by Anthony in Blockchain, Digital, finance, Founders, global, medicine, Politics, Strategy, training, Uncategorized.
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Entrenched. The longtime incumbents. When industry becomes too single-minded, others may start to notice. Each of these individuals from the podcast episodes are in very different industries – media/news, investing/venture, monetary policy system, and regulatory updating of provider-side healthcare. All very large, important systems that beget those that have lived the longest.

Each of the guests, however, saw opportunities in how stale an industry had/has become and attempted to take advantage. Whether that’s building something on their own directly (Jon Steinberg with Cheddar News Network) or indirectly (Gil Penchina with Flights.vc), they have a penchant for seeing innovation through. I loved hearing a few of them mention that it’s nuts to have the incumbents stagnate over some of the most advanced couple of decades we’ve ever seen.

I hope you enjoy the notes for how they structured the framework for the innovation, what opportunities they tried or came to realize, and which crazy people do you back.

  • Jon Steinberg (@jonsteinberg), COO of Cheddar News Network (Launch Pad, Wharton XM)
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    • Large appetite for live news and sports, very few people had done any in 20-30 years
    • Younger, faster, better as a business network
      • Younger, diverse anchors & audience in their 20s, 30s, 40s vs 60s and older
    • First round raised was $3mln, no big iron of typical broadcaster – different look and feel, same structural format for guest formats
    • Former president of Buzzfeed (2010-2014), DailyMail after – CNBC and live production as the best production
    • Lightspeed Capital friend who wanted to give him a first check – being part of a startup management team that’s successful to go from there
      • His first success was with Buzzfeed – played a role with many others, but combined his luck and effort to get the check
      • Gave up 20% for the $3mil
    • Showed up at the WeWork with Peter Gornstein, first partner and Chief Content Officer – looked at each other and “What now?”
      • Bought computers, then what now? Looked for vendors for equipment and build set.
      • Shot a 3min sizzle reel – shot sample video packages.
      • Next, go live from 9-10am one hour a day, basically – then how to ramp it up to 3 hours and more
    • Facebook Live launched, then they enabled the API so they could connect professional network equipment to it
    • Carriage fees – ESPN gets several dollars for every cable subscriber
      • Cheddar does advertisers and partnerships for their money and business
    • Purchasing Ratemyprofessor, MTVU – college market and network
    • Competitors are part of the network and counterparties still
    • Runs all news and advertising for Altice (after being bought by Viacom)
  • Gil Penchina (@gilpenchina), Founder at Flight.vc (20min VC 2/7/16)
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    • Note that this was the first day Harry had been to SF (meeting Jason Lemkin)
    • Network of AngelList syndicates that covers a wide range of sectors, SaaS, security, geographies
      • Biggest raise for syndicates to date – PayPal, LinkedIn, AngelList, Indiegogo – nominated for Angel of Year at Crunchys
    • One of early engineers at eBay (100 employees to 15000, 8 years)
      • Ran a spin-off of Wikipedia called Wikia – consumer content site, went to Fastly and angel investing
      • Wanted to work with entrepreneurs to fund small checks to other entrepreneurs as a community of helping
        • Didn’t want to do the full-time thing and thought he didn’t want to focus on terms all the time
    • At Flight, at time, they have 25 syndicate managers, 100+ volunteers to join the list – 2 groups – 1 analysis/learning companies, other sales/scouts
      • 3000 backers and they ask them to help their companies, small tasks (AngelMob) to improve or give introductions and recruiting
    • 5 years time – become a place for consumers to invest and save
      • Expansion fund and new projects – Eric working on traditional venture fund for follow-on in angel investments
    • 15 years ago, cost $10-15mln to get a website now and now it’s $10 or free for URLs (Reed’s blitz-scaling)
    • Next sector to be disrupted – education (investment seed and B into Allschool)
    • Start a syndicate – come up with thesis, going out and finding the deals (1 click to start), getting traction is hard
    • Investment ethos – people that are actually crazy
    • User of Nuzzel – best content for all of his friends
    • Similarity of Happn to “Chance Encounters” from newspaper – hoping someone sees it and reacts
  • Patrick Harker, President of Philadelphia Fed (Behind the Markets, Wharton XM)
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    • About 1/5 of jobs are at risk of being automated out – minorities and women in his district
      • Creating and destroying jobs with automation – not necessarily ridding them, but training will be important
      • Philadelphia Works – job training model for America, partnering with Comcast
        • Typically, it’s been “train and pray” – training and upskill, Comcast will reimburse out of the HR budget if successful
    • Biggest surprise – outside the lens of monetary policy – breadth of what they do is stunning
      • Largest collection of economic talent for all sorts of issues that aren’t celebrated
  • Pharma Drones, Veteran Health (16min on the News #14, 11/15/19)
    • Venkat Mocherla – market dev on bio team, GP Julie Yoo, Joel de la Garza security operating partner a16z
    • Pharmacy-patient relationship is highest volume/frequency interactions with healthcare system, owning node is good
      • Lots of startups on logistics on pharm, last mile and full-stack delivery/pharm, nontraditional care centers
      • Medicines/therapeutics work for patients, compliance is one of the biggest pains
    • MediPlus, Whatsapp your prescription and you can get delivery within 24 hours
      • Fastest regulatory arbitrage – where are opportunities – Zipline in Rwanda, for instance
      • Antiquated for brick-and-mortar to innovate, but instead mobile-first and digital distribution
      • Pills, small molecule drugs that are cheaper, chronic that can be easier
    • Last mile delivery solution is cost – one-off deliveries to patients to homes has cost issues – more expensive
      • All come to hub because of delivery efficiency
    • Apple opened up health records service to vets with iPhones – give them access to their medical information regardless of provider
      • VA is mired in healthcare challenges (came up with EHR)
      • Knock on digital health industry – great for pilots but unable to scale so far, VA and NHS populations are one-go scale
        • Not bastions of innovation but more captive population (1mil to 10-20mil)
      • Last decade, provider-side heads down for data that’s digitized but not interoperability
        • Get at the data is not a given, Apple unleashing data to consumers is great but is there utility in it? (no imaging data, limited)
      • Match data to patient, or doctors, scheduling appointments – technology for technology’s sake isn’t usually great
    • Voice commands as being sent by light – specific microphone design that’s vulnerable to the attack
      • Area of research to use frequencies of energy to affect systems – light to mimic sound, for instance
      • Advent of radio has been different research – cathode ray tubes, radio surveillance
    • Enabling hardware manufacturers to guard against this – microfilms or filtering fraud and security

Back from Vacation (Notes from Nov 11 to 17, 2019) February 11, 2020

Posted by Anthony in Automation, Blockchain, cannabis, Digital, education, experience, finance, Founders, global, gym, Leadership, marketing, NFL, NLP, questions, social, Strategy, training, Uncategorized, WomenInWork.
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It had been a long while – 9? months since taking more than 1 day off extra and closer to 20 months since I’d had a week off in a row. I visited the Big Island in Hawaii and stayed primarily on the west side of the island. Gorgeous weather and awesome beaches will bring me back, hopefully shortly.

I want to write a bit further about the escape, but I also want to get these notes out, so I’ll write further in later this week – Thursday.

Enjoy these notes on some of the fascinating people of Eniac Ventures, other investors, founder of EasyPoint, ReSolve quant, research professors, former professional football player and a Nascar driver.

  • Hadley Harris (@Hadley), Founding GP at Eniac Ventures (20min VC 2/3/16)
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    • First mobile venture, Soundcloud, Airbnb, Vungel
    • 2x entrepreneur in mobile – Vlingo (acq by Nuance for $225mln) and Thumb (acq by Wipulse)
      • Was one of first employees and execs running marketing and bd while working with product
    • Worked at Samsung and Charles Rivers Ventures
    • Studied engi & math as undergrad @ Penn, joined MSFT & Samsung
      • His 2 really good friends at Penn and him came together for Eniac in 2009
      • Mobile – next place for computing – cleantech was hot at that time, as well
    • SF was 50%, NY as 25% and the rest was elsewhere – won’t lead but will do a pro rata and be key in fundraising for next
    • Living & breathing the co – coming to right valuation, inevitable for down or flat rounds
    • 18-24 months from seed to series A or pre-seed to seed – funds becoming more institutionalized
      • Leading rounds for Eniac at $1.2 – $2mln
    • Favorite book: Freakanomics, read it in one sitting
    • Tools: gmail, relayedIQ for deal tracking, as todo list, also
    • Don Valentine – godfather of VC, great investors but great entrepreneurs and fund raisers
    • Favorite blog: Nuzzel – curation of reposts
    • Underhyped: mobile enterprise; Overhyped industry: big fan and he does work in social, but lot to weed through
    • Most recent investment: Phhhoto – knew the founders, they’d known each other for a while, great design and numbers – self-funded
  • Zach Resnick (@trumpetisawesom), founding EasyPoint (IndieHackers #130, 10/28/19)
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    • Iterating your way to founder-product fit, currently at 10 people, 5 full-time, $600k ytd with 15% yoy organic growth
    • Traveled, worked and lived abroad in Jerusalem before school, infected with wanderlust
      • CC churning and manufactured spending while he was learning at school in Ohio – VISA gift cards to $1k
    • Banking often makes more money on the chance that you’ll become a customer for other areas of business (mortgage, checking account, brokerage, etc)
    • Started when he was 19 – would give advice to parents/family/friends on the year before getting an hourly rate for paying customer as consultant
      • Enjoyed his help, he liked helping others – he was getting $1k/mo from hourly before going up
      • Consulting clients – he was helping optimize for business or vacation trip for the points
    • Started Land Happier to solve a problem of having everything in one place
      • Cultural norms, transportation, 6 other things for information in a fun and compelling app product (MVP on app store)
      • Wasn’t solving a problem that nobody has, but nobody would pay for – product/founder fit wasn’t there, either
    • What he wants – enjoys negotiating, strategic thinking, interesting conversations and sales moreso than product focused than customer focused
    • While working on Land, he productized his consulting – generally was helping family friends that were parents’ age
      • Amount of effort he was putting in compared to the value wasn’t the same – not high enough
      • Started to focus on small business or medium enterprise owners to put spending on the right cards and get 6 figures on spend return
      • Focused on people he knew through referrals, points optimization plans for small owners – acquisition and spending for more value
    • Early stage owners – hey, this isn’t free
    • Playing poker for relatively high stakes – teaching important principles, statistics, risk management and psychology
    • Consulting to productized consulting service – had a family friend with small business who would see a $50k in increased return on spend
      • He could do a quick analysis and understand business more, try to get a customized points optimization plan for points
      • Small business owners are leaving 1.5%, maybe 2.5% on the table – using points better for things you already want to do
    • Providing value but people didn’t know what it is or weren’t hurting – show them math for 5 figures within a year saving
      • Guarantee: if you sign up points optimization plan, if he doesn’t get you double what his fee is within first year, he gives money back and $10k
      • Making people aware of the problem was going to be a lot of work – never really got off the ground for outbound
        • Was just a way to make money, not necessarily grow it really fast – customers’ needs
    • Concierge service now (v3 EasyPoint) focusing on business and first-class international long-haul service
      • Over whatsapp and telegram groups – makes a flight request and they get back to them 24/7
      • They use miles and points that they buy from clients and then use those to book for others
      • Brokers buying all kinds of points and miles – so the arbitrage there contained issues with ToS and such
        • They’re buying transferable points like Chase / AMEX directly to frequent flier accounts
    • Working for someone else – interned with The Points Guy and when he was looking at doing it, he posted on the Facebook group
      • Cameron, now their COO, was very good – would he want to have his hires over for dinner?
      • Team of 10 now: Cameron manages concierge, growth marketing (5 on team, looking for Asia now)
        • Part-time business development consultants, full-time that have been searching
      • Revenues and loans for growth/cash flow, venture debt and possibly equity raise
    • Concierge service with product-market fit and being focused – enterprise value of $100mln probably but not billions
      • Not much needs to be tweaked for core product – fund raise would be for a different product
        • Help consumers decide on if they want to use their points or cash when booking – trying to automate this for concierge/back-end
        • Chrome extension and booking engine to use or not – this may be billion dollar opportunity
  • Andrew Butler, ReSolve’s Head of Quant Research (Gestalt University, 10/2/19)
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    • Machine learning in markets: Silver bullet or Pandora’s box
      • Unsupervised, supervised and reinforcement learning differences in application or finance
    • Student of mathematics, physics in undergrad, keen on not memorizing a lot of stuff – enjoyed the applied side
      • Oil reservoir simulators that modeled tidal flow in Bay of Fundy, wind turbines in giant field for optimization
      • Next step was working on a sub problem of simulators – complex, computationally expensive and trying to optimize NPV in 60d oil field
        • Navigating the nonlinear, nonconvex solutions – how to make a reasonable model approximation by sampling sparse reps of simulator
    • How would simulator/emulator apply to financial world in momentum and moving averages
      • Sample distribution would fit well to out-of-sample distributions in physical world but finance wouldn’t – nonstationary
      • Caused him to use simpler models, momentum models (and transformations) and ensembles of simple factor models
        • Mean-variance optimization, error maximizing, in-sample won’t perform well out of sample
    • Wanted formal training in financial engineering, so went and got a MFE
    • Practitioner compared to theorist – after a conference talk, his construct was mean-variance was same as regression
      • Subspace reduction and regularization as identical terms for mean-variance
    • Machine Learning as 3 subspaces
      • Unsupervised learning -> clustering and dimensionality reduction
        • Targeted marketing, customer segmentation and in finance: signal processing, optimization or portfolio construction
        • Trying to uncover relationships/groupings/clusters contained within a dataset
      • If total error is dominated by bias, it’s likely overly simplistic – X as model complexity and Y as Total Error (Bias / Variance)
        • Increase complexity, bias term can decrease, increasing the variance (instability/overfitting)
  • Kelly Peeler (@kellypeeler), founder / CEO NextGenVest (20min VC FF#034, 2/5/16)
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    • College Money mentor, empowering students to live full lives, history of financial crisis for motivation to start
      • Went over to Iraq, started and enabled some companies to build there in 2012
    • Went to JPMC after graduating to make some money before starting NGV for students
    • Financial organization to financial efficiency – going from Mint (organizing money for a user’s financial lives)
      • Now people need efficiency – time priority, optimizing time through automation and personalization
        • Leverage trust to improve time in the background (automation and not wanting to have to look)
    • High school trust and students have nobody they can trust for guidance – 8% trust banks and financial institutions
      • If you can build a product/service, on your way to building trust
        • Save users time, money, customized experience
    • Serving their customers with SMS and Snapchat – smarter push notifications for the right service in the right way
      • Couldn’t customize communication inside an app, so they did channels that they chose
    • NGV clubs at high schools across country – new high schools brought in, engagement and grassroots
    • First product that they brought on was for the financial literacy test that 17 states need
    • Favorite book: The Thank You Economy – best people outhustle to get more customers
    • As visual person, can focus on 1-3 things at a time – preps in the evening, large index cards
    • Adam Nash at Wealthfront – build trust with dynamics of product and the culture of company
    • Spent too much time at focusing her weaknesses but has tried to get better on that side
  • Sam Yagan (@samyagan), Starting OkCupid, Sparknotes (Wharton XM, Marketing Matters)
    • Turning down consulting job for OkCupid start – told he was crazy but wanted to take the chance
      • Free model and how do you value customers but competitors were Match and eHarmony
      • Had to get enough people on all sides of the market and then could use the data to help
    • Internet wasn’t designed to take an expert’s ideas and just use those – bigger than that
      • “You know what you want.” We’ll pull it out and figure it out.
      • Google comparison – index all the pages and figure those out to place on first page
      • Creating a platform to ask all the questions and focus on them
    • Sold Sparknotes in 11 months, took OKCupid 8 years (sold to Match, was there for a year)
      • Got the job running the company for another 3.5 years as Match CEO and created Tinder
  • Rob Gronkowski (@robgronkowski), All-Pro tight end (The Corp, 10/1/19)
    • A-Rod investing into Rob’s brother’s, Chris, company Ice Shaker
      • Were able to put money in, along with Mark Cuban, when they were on Shark Tank (all brothers)
      • Rob, upon retiring, bought Arod out of his shares in the business with Chris
    • Fitplan – Arod gave Rob a discount on the shares in Ice Shaker and he just wanted Rob to look through his company
      • Rob invested with Arod – parents were in business (gym equipment for retail/commercial for 28+ years)
    • Kraft being an owner for the team and being around the game – interested in everything
      • Rare to see owners in the locker room and talking with players – many players say they’ve never seen others
      • Brady, Kraft and Belichick as being the greatest people and diagnosing problems/plays and adjusting
    • Rob wants to travel – done a lot in the US
      • Traveling a week from that day to Israel with CEO Barry of CBDMedic there
    • Being reckless as single Gronk in the NFL (loves Camille now, though)
  • Horst Simon (@hdsimon), Chief Research Officer at LBNL (Curious Investor 9/3/19)
    4vfj55gu

    • Difference between ML and programming – validity of an email, for instance
      • Computer looks for “@” and domain name, iterative of if-then’s, marking valid or invalid
      • ML – give details of valid and invalid email addresses and have the computer figure it out with a statistical model for rules
        • Relationship between information
      • ML more as being able to see if something is a cat in a picture – hard to program that
    • Helped establish the Berkeley supercomputing center – big role all across the world now to complement theory by simulations
    • More data than ever before, 90% of digital data created in last 2 years – more in 2018 than all of human history
      • Finance can’t generate more data like autonomous cars, for instance (100 cars means 100 more data points)
      • Markets/economics are dynamic – return predictions of signal:noise approaches zero
        • Driven by economic features of markets – competitive, profit-seeking traders that act on it
      • HFT as real barriers to entry so they’re less efficient and more predictable, potentially
      • Quantitative traders don’t use raw data – they use transformations such as log of equity, cross-sectional rank of book to market ratio
        • Neural network tries to find what the best transformations are (X -> Y and explore all the connections)
    • Bonds example: predict if issuer will default or not with firm information using random forest
  • Rajiv Shah (@rajcs4), Data Scientist @ Data Robot, Adjunct Prof UChicago (DataSkeptic, 10/22/19)
    1024x528

    • Started engineering, studied philosophy and law, PhD in Comms before doing research as academic
      • Worked at State Farm and Caterpillar before going to Data Robot
    • Deep learning applications in motion data like NBA player data, motion tracking arms and legs (PoseNET, for instance)
      • Nature paper published that used deep learning to study after-shock patterns for earthquakes
    • Going through paper – simple starting point or baseline model was skipped – how much value is really added, then?
      • Looking at the 6-layer problem – approach wasn’t unexpected when using keras to add layers
      • Results generated: AUC of 0.85 compared to naïve benchmark of simple, physical model – AUC of 0.58
      • When he reproduced it, test set results were higher than training set – yellow or red flag for model
    • Group partitioning – 130 earthquakes happening right after each other, near each other and related
      • Make sure the information for an earthquake/customer doesn’t get split between training / test sites to avoid leakage
      • Basic grounding of fundamentals for setting up initial training data, partition based on time to avoid that, as well
    • As community, ensure that there are best practices and guidelines – reproducibility as a large problem lately
      • How to police boundaries for the general field – influence of institutions in publishing (for this, Harvard/Google/Nature mag researchers)
      • Good from them: the data and model for the code was freely available and he could do it on his laptop / notebooks
      • Academics from the earthquake field reached out to him with some qualms and he’s partnered with them for a blog on efforts
    • Interpretability focus trade-off with accuracy – that he’ll speak on at Open DS Conf
      • Lots of tools for explaining models with transparency now, though
  • Julia Landauer (@julialandauer), NASCAR driver (Stanford Pathfinders, Wharton XM)
    • Being on Survivor (suggested by a friend while Soph in college), racecar driver
      • Picking Stanford because of so many people that were awesome / ambitious
      • Mentioning Andrew Luck saying that this was why he chose it – people wouldn’t particularly care
    • Driving at such a young age and in Manhattan – not getting a license there until 18 on campus
    • Having to pitch and learn how to pitch at a young age for sponsorships, running a team and the cost, even at minors – $500k+
    • Some 12 female drivers and being competitive

Find Your Own Value (Notes from Nov 4 to Nov 10, 2019) January 21, 2020

Posted by Anthony in Blockchain, Digital, education, experience, finance, Founders, global, Hiring, Leadership, marketing, questions, social, Strategy, Uncategorized, WomenInWork.
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One of my favorite pieces and follows on Twitter of the last 3 months has been Tyler Tringas, founder of Earnest Capital. He came to realize that there existed a massive opportunity to fund companies that may not require or need the VC model of capital infusion – just a starter amount to do testing, easiest when people look to make sales and revenues early (maybe not the model for certain industries – marketplaces/user-dependent network effects not-withstanding).

Wild for micro and seed funding, when companies have yet to establish a true product-market fit or business model each time, typically stick with one financing style. I wonder how much innovation has been restricted by the funding style. There are advantages and disadvantages for each of those. But I’ve yet to come across more than 2-3 VC’s (out of 1000s) that do multiple and have a separation / adjustment. Makes sense from the LPs sense, but not necessarily if you want the companies to be SOMEHOW getting to a growth/scale that fits.

Less Annoying CRM Tyler King was cognizant about the capital and efficiency standpoint in business – everyone that doesn’t create value seemed expendable. Those that did will make it. I find that an important takeaway and general attitude toward either doing your own thing or being a part of a bigger company.

Hopefully each of these excite everyone enough to check the fantastic people/content out further!

  • Tyler Tringas (@tylertringas), founder of Earnest Capital (Indiehackers #131, 11/1/19)image02

    • Funding for entrepreneurs, founders, outside of the ecosystem – profitable and sustainable
      • Not competing with other options – just found a large group of bootstrappers that aligns with the goals
      • RBF doesn’t work for some
    • Green field space in the past – no competitors and could gobble the market – big risk early but if it’s worked, it can be massive
      • Launching and building became cheaper and more niche for diversifying the opportunities – limiting VC scale
      • When he sold his first business, he handed over his Stripe account, Github and Roku
    • Software companies – no retail shop meant your option was “raise money” = “raise venture capital”
      • If you were doing a bakery or something, you had a plethora of options
    • 5 years ago, he was one of the loudest critics and blogger
      • If he was bootstrapping, can you work backwards and what would you have wanted to work with
        • Is it actually a fit for you
      • No board seat, mentors for long-term
    • Raise money when you believe the money will unlock value in the business
    • Had Storemapper – where he figured out what he wanted to do next
      • Derek Sivers – Tarzan move – need the second vine before letting go of the first vine
      • Pivoted to finance to do finance models behind wind/solar farms
      • Then to micro SaaS Indiehacker before noticing people struggled to get businesses off the ground early (his $50k cc debt)
    • His basic bet is that it’s not an iron law of physics that 90% will fail
      • His fund will fail if it is an iron law – and his investors are aware of this
      • He believes the VC model is circular in that if you require growth is 11% a month for 12+ months, more likely to become unicorn
        • But if they don’t hit that, then they’re failing
    • Really interested in niche markets for a piece of software that serves a market – eg Hostify, Endcrawl post-production credits, etc
  • Tyler King (@lessannoyingcrm), cofounder of Less Annoying CRM (Indie Hackers #128, 10/21/19)
    5bac7c2c446aa-resize-710x380-1

    • Bouncing between companies after college, had joined a startup that grew after Series A, only to be acquired
      • Everyone was fired except for 5 cheapest employees (including him)
    • Marketing channels not working – word of mouth, sometimes paid ads, Google AdWords or Facebook
    • Customer support – competitive advantage as going slow, not being held to revenue standards
      • Can focus on customer service and product features
  • Maren Bannon (@maren_bannon), cofounder & Partner at Jane VC (50inTech Podcast #11)
    https3a2f2fblogs-images.forbes.com2fcarisommer2ffiles2f20182f102fjane-vc-logo-text

    • Cold-pitching VC – for cold emails, take time to research the investor and explain why they’d be interested
      • Adjacent industries, past role in competitive area, resonating project
    • Nailing the one-liner / 10 second offering in a sentence
    • Bullet points, succinct including certain things
      • Traction for user/revenue/notable customers
      • Advocates, angels with industry expertise
    • Why you? Brief description for the ideal team.
    • Include an ask – why are you contacting? Advice, seed round, etc…
    • Include right materials (letter can be brief, but more info attached or deck or 1-pager)
  • Ok Boomer, Microtransactions (16min on the News by a16z #13, 11/3/19)
    • NYT Taylor Lorenz – (perennially behind others but gets credit for the writing of it)
    • Taking on a meme, protest for what’s rigged – Gen Z affected by Boomers “hurting us”
      • How memes can turn into clothing, sales for songs, be further monetized
      • Social media generating social phenomenon and transactions and merchandise
        • V1 was ad-based, then quasi-based for sponsored ads (protein powers and such), direct transactions for monetizations
        • Can get demand and feedback for multiple types of merchandise before launching and sending out efficiently
    • In China, commerce is already in the app – button after 2nd loop you can complete purchase inside the app
      • Close the loop on-platform in China
    • Marketplace on games for platform – supporting size/scales that fraudsters can open up accounts and quickly find monetization structure
      • Build false economy and cash out quickly – advanced fraudsters for automation, maybe with virtual trades and purchases
      • If it’s $10k, they’re wrong – probably multiple millions, if not more
  • AI in B2B (a16z 10/23/19)
    189-1892846_people-ai-logo-png

    • Oleg Rogynskyy from People.AI, for sales and marketing
    • Very few users that give you private, anonymized data is much harder to make them comfortable with this data
      • How valuable is the promise you’re making to customers vs the cost to achieve it
    • For entrepreneurs: if there is human activity that generates data for how they do it that isn’t being captured, there’s a ripe opportunity
      • Shipping containers, wind farm, location of Uber driver – reliable data, aggregate and figure out what may be the next best action would be
        • Significant growth and acceleration for these actions once network effects apply
      • More sensors, edge computing, salespeople, drivers in network – more data collected and more patterns you can see
        • Smarter the graph becomes, better the predictions may be allowed to become – then, more money and lures in other network participants
      • Wind farm operators: know it will break after it breaks but someone in comes in that was there collecting data ahead of you, they are up still
        • Competitor automates process, you can go to same vendor and catch up but if you miss AI, you can’t catch up
      • Oleg mentions that he thinks AI is zero-sum and that the Fortune 500 will look very different in 10 years
    • All customers benefit from generalized data – first customers have to do a lot more than others
      • People writing contracts: only sell to me, but customers would be relics
    • When the data model changes, systems of records die – Andreesen
      • Hierarchical first, then on SQL, then cloud SQL and Salesforce
        • Next gen data model should be graph – federated shared graph model – instead of you pulling data and searching, it will push to you
        • Personalized actionable insights – pushed through the channel you’re most likely to engage with – maximum focus
      • Level of intent for the user should be known – don’t have to expose the complexity but you can be shown and execute that
    • Difference between autopilot and co-pilot
      • As human, something mundane or repetitive – automating the functions to make it more efficient use of your neurons
      • Augmenting ability to make decisions – racecar that may know what’s around the curve, making us super-productive – more human
    • Needs to be 10x on the platform vs off the platform if you’re afraid of the set-up
    • Sales & Marketers specifically
      • Shifting how they work – day-to-day: 1/3 of time on manual data entry, 1/3 on prospecting (classic problem), 1/3 on face-to-face doing selling
        • First should be gone, 2nd should be done with help on ML and AI for value-add prospecting and automate outreach
        • Face-to-face: Machines can’t replace this but may be able to help out
      • Training on the end point – best way to sell, unbundling learning management system
    • Wants to do bottoms-up but currently top-down – through standard procurement channels
      • Users will demand data-hungry approaches and solutions – apps that built AI on user data but not merging with enterprise data
        • Have easier time for value adding in these cases because you just want data to increase (single player can do single player)
    • Biggest surprises: inside sales for Oleg starting in 2006 pounding phones, went out and did a software change before downturn
      • Learned timing matters at that time.
      • Then started Symantria – sentiment analysis API in 2011, size of market matters – 20-30 companies needed it (80% of market)
      • Remembered that he was put into a conference room with COO (head of sales), cleaned Salesforce and within a month it was in ruin again
      • Couldn’t understand sales team when he took over, why it wasn’t ramping up quickly, losing deals, hiring more people but productivity was fine
        • Supposed to have data in CRM but never had it
  • Martin Mignot, Investor at Index Ventures (20min VC 2/1/16)index-ventures-768x469-1

    • Investments including Deliveroo, Blablahcar, Algolia, SwiftKey, others
    • Worked on 50 transactions like CodeAcademy, FlipBoard, Soundcloud
    • UBS Investment Bank on TMT team and co-founded beauty subscription company called Boudoir Prive (acquired by BirchBox)
      • Comes from entrepreneur family and action/doer and the creative
      • VC seemed to be between acting and thinking part of the job as he’s followed it for 10-12 years
    • Split on idea of career VC without operating experience
    • 3 ways to look and slice companies: at Index, they have thematic and geographical approach since they need to have ppl on ground in Europe
      • Stage-focused: seed / growth
      • Thematic: fintech, adtech
      • Geographical: Germany, France, London, Amsterdam and building the network there with angels, seed funds
    • 6 hour drive test or drunk test with founders – no formal founder test to determine invest-ability
      • Are they able to attract and hire the people they need
      • Trying to decide if the risk is worth reward – not beholding themselves to a valuation cap if they believe
    • Favorite book: I have America Surrounded by Tim Leary
    • Investor who has shaped his theses is Fred Wilson – being right, companies and sharing insight, communicating as USV and himself
  • Elaine Beak, consulting and HBS (Career Talk, Wharton XM)

    • She wasn’t too scared but whenever she had problems, the solutions would arise
      • For others, the security blanket is the scariest for most people when she tries to help them on decisions or convincing them
    • Writes her books in 2 weeks each – written and published 80+
    • Word of mouth, should have 6 months saved up, and have 50 people that you can contact for saying you’re going out on your own
    • Following own rules:
      • Billing clients the same day that you finish a project.
      • Clients may have 30 day billing window, so if you waited 2 weeks, they’ll forget or not be as appreciative.
    • Don’t discount, add to the service instead – charge more
      • Bad reputation for discounting.
    • Go for the big fish – large companies but the time to get smaller companies is the same for larger. Repeat business is there
      • Repeat business and more of a budget to continue work.
    • Learn to say no. Non-paid speaking engagements should be limited.
    • Manage your time well – make sure it pays off.
      • Find ways to automate things – invoices, payroll, accounting, responses to common questions
        • Make a standard paragraph or find an app/template once you have these
    • Project will end but not relationship – stay until the end and do a good job for the client.
    • Incorporating, LLC for sure

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

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

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

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

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

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

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

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

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

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

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

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

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

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)
    41bmp2he9el._sx322_bo1204203200_
  • 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)
    gx-global-center-for-corporate-governance-new-promo

    • Discussed her mother, who had passed away at the age of 90 recently, who was told she couldn’t be an accountant
      • Wasn’t her role – she pursued it anyhow and ended up being an engineer before quitting and being a community leader
    • Worked in Gulf Oil’s accounting dept and helped her husband through med school
      • First councilwoman in her town, elder at the church
    • Deb started at Andersen until it folded, worked for only one woman but she was taught there were no barriers
  • Bentley Hall (@bhallca), CEO of Good Eggs (Wharton xm)
  • Mitch Berg, CTO of Vroom (Wharton XM)
    mb_vroom_id_1
  • Alex Salkever (@alexsalkever), Vivek Wadhwa, authors “Your Happiness… Hacked” (Wharton XM)
    • With Stew Friedman, finding the middle ground of tech with children / teenagers and the happy medium
    • How is it that we find some things appealing but others are a burden
    • Facebook being a publishing agency – aren’t they responsible for what the product? “Newsfeed” example.
    • Google Maps or Waze as a hindrance at the local level – dangerous, maybe?
      • Extremely valuable, still, in new places / out of the country, especially
        • Different, maybe, for walking if alternative is talking and communicating with others
    • Problem with Facebook / Whatsapp – Whatsapp unmoderated group chats and only requiring a phone number
      • Encrypted, but what cost? Facebook – for Vivek, just limits to 1-way action
    • Social media as killing people – think India’s problems
  • Ed Sim (@edsim), FP @ Boldstart Ventures (20min VC 092)
    page_55dc6912ed2231e37c556b6d_bsv_logo_v15.3

    • LivePerson, GoToMeeting are 2 of his biggest investments as lead, exited / public
    • Started a fund in 1998, DonTreader Ventures – left in 2010
      • Idea was to bring SV style to NY – VCs would look at financials / models, but they looked at people and product – focus on markets
      • Most investors were corporate but cratered after 2008
    • Started a new seed fund for sticking with what he knew as well as recognizing a shift in 2007 for open source and cloud – consumer-based
    • SaaSify vertical markets with GoToMeeting founders who wanted to do new things – $1mln, $1.5mln
      • Enterprise people were looking to get a market for small ~$1mln investments
    • Hated starting a fund – “Fundraising sucks.” – Could find a great enterprise and tech entrepreneurs at seed stage – got $1mln and made 10 inv
      • First 5-6 investments were less than $5million pre-$, sold 4 by 2012 – had option values for series A or being sold to strategic companies
        • Entrepreneurs wanted to sell in those cases, but with cloud, definitely found that it was reasonable and cheaper to do SaaS
    • First / second generation founders or single vs others – “No single founders”
      • As the first institutional round, they’re first big money in. Last few investments were second or more founders – little bigger rounds
      • If first-gen founders, funding rounds are smaller – deep expertise in their field (and have to be engineers building product)
    • “Enterprise can be fucking hard” – have to know the industry – he has 20 years, partner has 10 and new partner as building 5 companies
      • Why he went this route? Started at JP Morgan as building quant trading models as liaison Business QA between engineers and portfolio managers
        • Derivatives models to real-time pricing models – feeds from Reuters or others, risk metrics and crank out the other side
      • Enterprise was exciting to him
    • Could take enterprise founders and redo or build a new company by changing the pain point – customers can be repeat because new pain point
      • Harder to do that in consumer
    • Leads come from founders – roughly 75% as recommendations from portfolio companies (wants to be first thought or call)
      • Helps founders get their pick and decide where to go – if you have an analyst report, may not be a great market opportunity initially
    • Environment of seed funding: Jeff Clovier of SoftTech as one of few microVC’s and now it’s 400+
      • Just want to be hyper-focused and being nimble – main value add as understanding the cadence (2 founders coding together to selling)
      • Stratification of VC – best ones have gotten so large that they can’t write small checks efficiently
        • Entrepreneurs don’t want $5-10mil immediately out of the gate – mismatch, looking for less for less dilution
      • Deal flow of crowdfunding: says sometimes they will leave $250k after leading for AngelList or building new relationships
    • Jason Calcanis blog Launch Ticker, trend as rise of the developer (multiple people in company using same thing – buying licensing)
      • Messaging as another interesting trend in the enterprise space – his most used app – Slack (SlackLine – private, external channels)
    • Most recent investment – stealth investment in a repeat founder (founded and sold before) – security focused on developer
  • Kim Wilford, General Counsel at GoFundMe (Wharton XM)
    go_fund_me_logo_courtesy_web_t670

    • Talking about joining, hadn’t considered nonprofit space
      • For profit arm and the nonprofit
    • Mentioning pushing marketing and following metrics for raising vs donations
    • Can influence news stations and push for higher engagement
    • Done almost $5bn in funding across 50 million donations

How to Humanize Data for Enhancement (Notes from May 20 – 26, 2019) June 12, 2019

Posted by Anthony in Automation, Blockchain, Digital, experience, finance, Founders, global, Hiring, Leadership, NFL, questions, social, Strategy, Uncategorized, WomenInWork.
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I’m going to keep this brief this week, as I’m trying out an added format of posting. I believe I’m going to test Nuzzel news to connect and share the links of articles I’ve read through the week (maybe once or twice – weekend one out on Monday, and another for the week through Thursday?). This post, concentrating on my week’s podcast or segment listens, will still go out. That’s my goal, though.

The week I listened to these, I was actually on my way to Monterrey with my family, though, so I had a solid 90 minutes each way to go through some longer episodes – thankfully, I found Doug’s take on culture and leading a community with Eat Club very insightful, as well as a blockchain discussion by the a16z squad particularly fun.

With the news and media focused on AI, Bias, Diversity and Ethics of ML and scariness of algorithms, it’s good to hear opinions that are focused, but thoughtful on the premise of what is being proposed. In a vacuum, yes, these can be dangerous. However, that’s not often the case when models are being deployed into production. Some are unintentional or started with a simple question of relationships (think Cambridge Analytica and how it started via Likes research on music at Facebook). That’s not necessarily justification for something that clearly had a large impact, but we’d like to know that teams are improving and have that awareness now more than ever before. That’s a positive.

I believe that these episodes, though, take a bit more of a nuanced approach of mathematics and numbers and attributes a human eye that has acquired the necessary expertise to design better models or come up with a better framework for action. We hear that with Dan Waterhouse of Balderton Capital (of a delightful Applied Math degree), who had to learn to apply more of an operational and human/market behavior investor since he had the metrics focus while learning. Then, a fascinating segment with Laura Edell, a sports fan, who struggled to believe that the metrics that were widely accepted as ‘most accurate’ (re: not necessarily max accuracy) included all components of what could be measurable – made some assumptions to test based on twitter/media analysis and coupled it with the data that was widely acceptable to come up with an excellent Final 4 Model.

To continue the theme, not to be outdone, Geoffrey Batt and Nikos Moraitakis each spoke of differing aspects of metrics that they honed in on and ignored the common threads of why those weren’t valid, despite what they saw as opportunities. Geoffrey as it pertained to Iraq investing and Nikos in the form of starting and building a company in Greece, not exactly the traditional hotbed. However, each persisted and are successful today – so there’s a lesson in doing the things that we want to do.

Remember, ultimately, numbers and averages usually take the aggregate, collection of those that may not have had the timing, willingness or ability to push but with enough persistence and support, some will certainly persevere.

Hope you enjoy!

  • Doug Leeds (@leedsdoug), CEO at Eat Club (Dot Complicated, Wharton XM)
    cropped-logo-highres1

    • Talking about going from Ask.com to Eat Club – other companies, as well
    • Quiz leading in was on different likes/dislikes
      • Harry Potter-themed eating pop-ups
      • Anti-spoiler pin (digital, season and episode to avoid conflict)
        • Anything was good that brings community together
        • Said that there were employees at work that took off Monday after GoT finale since they hadn’t watched
    • Delivery people are employees, not contractors – become regulars at corporate events and a part of the culture
      • Help build the culture of the locations – food and how they match
      • Building culture of Eat Club with improv – ability to improve skills there, as well
    • Eat Club numbers – 1000+ companies served, about 25k lunches provided
      • Regional difference of Indian focus in NorCal, Mexican focus in SoCal
        • Acquired an Indian food provider for new techniques – Intero?
        • Acquired another, as well
  • Five Open Problems Toward Building Blockchain Computer (a16z 5/16/19)
    ah-logo-sm

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

    • Sits on boards of Top10, ROLI, Lovecrafts, TrademarkNow etc
    • Was 5 year partner at Wellington Partners and invested in EyeEm, Hailo, Yplan, Bookatable, SumAll, Readmill (sold to DropBox), Qype (sold to Yelp)
    • Break in 1999 by getting to work at Yahoo for about 10 years before getting to venture in 2009
    • Applied Math background, tend to understand complexity and appreciate solutions in technology – different experience for entrepreneurs
      • Learned to be less metric-driven – less obsessed on numbers and data, more human and market dynamics
    • Operators vs career VCs – successful firms take different skills at different times
      • Exit, raising money, portfolio management, scaling, growing
      • Important to watch how a business is grown, whether it’s yourself or by being a long-term investor or close in other ways
    • Entrepreneurship as a career path and as a global one, investors in similar mindset and ambition – can take it anywhere
    • More seed capital and smaller checks than later in Europe at this time – maybe more competition higher stages
    • Thinks that consumerizing SMB software has a lot of room to grow, as well as enterprise software – easier UI-driven tools
      • Developments in AI – mentioned real-time trademark analysis (TradeMarkNow, for instance)
      • How to give great recommendations after gathering consumer tastes and insights
    • Book: The Brain That Teaches Itself – neuroplasticity of the brain
    • The first $10mln you invest, you’ll lose – advice
    • Overhyped sector: food delivery; Neglected: apply behavioral science to tech problems
    • Favorite blog/sector: longs for better content – thought TC was great when it was started, but now it’s Medium individual articles
    • Most recent investment Curious.ai – made it in 40 minutes & ambition was compelling
  • Geoffrey Batt (@geoffreysbatt), Nature of Transformational Returns (Invest Like the Best, 4/9/19)
    • Iraqi equities – history of asset classes
      • Time and patience is a big factor for how you look for results
      • Japan, for instance, 1950 – 1989 100,000% return but now is still below the peak from 1980
    • May have a decade of not working out return-wise
      • Nothing to show for investments, incredibly challenging, especially now – hedge funds or LPs
        • Reporting weekly or daily basis, long-term investment could be 6 months or 1 year – pulling money after first issue
      • Hard to ride out periods of long-term returns (think: re-rating countries)
    • LPs as agreeing to the time horizon – maybe investment committees that are making decisions on career-risk for institutions
      • Iraq to one of those – not likely to get paid off if it works, but if it doesn’t, get demoted or fired
      • Had to approach HNW and family funds – adjacency risk for people next to seed investors (if one is weighted on fund)
    • As student, he majored in psychology – said there was a guy in the seminar class that was a unique thinker
      • Daniel Cloud had co-founded a fund that invested in post-Soviet Russia before doing his PhD
      • Asked Geoffrey to come work for him and learn investment markets
    • Wanted to find next big thing – first, find a place that everyone thinks is awful (in 2007, this was Iraq)
      • Does perception meet reality? How many people dying in the war every month?
      • Country portrayed as “failed state” but oil production was increasing, CPI was 75% yoy but now 5-10%
        • Currency appreciating against the dollar, now civilian casualties in a month were down to 200 from thousands
      • Normal visit to Baghdad – mundane here, out-of-place was that 2 guys in middle of afternoon were playing tennis, kicking soccer
        • Visit companies, stock exchange, meet executives, go to a restaurant, how easy is it to hail a cab, get around
        • Critical process of infrastructure – are people paying in dollars (bad sign), local currency?
    • News media is just not set up to convey complexity to the audience – alienate, progressivism as a service, readers change
      • Experts don’t have skin in the game so they don’t face career risk – betrayal if you suggest otherwise, likely (even if true)
    • On his first trip, mentions Berkshire – early 1990s where they first invest in Wells Fargo
      • Managerial skill in banking is paramount if levered 20:1 (make 5% mistake and you’re done)
        • Don’t want average banks at great prices – want great banks at slightly unreasonable prices (thought about this in Iraq)
      • CEO of first Iraqi bank – unsecured loans to taxi drivers – less likely to take out the loan “between you and I, I’m a cowboy”
      • Entrepreneurs in these areas have to be better than others because of the instability vs the stable environments
    • Stock and capital guys – stock may trade at 3x earnings or 4-5x FCF, top and bottom lines growing at 20% per year
      • Usually just put there as knowing someone powerful but they can be bigger as allocators
      • 2008-09: Size at the time of the stock market – traded 3 days a week, 10am – 12pm on a white board
        • 60-70 companies, maybe 20 suspended from trading because 0 annual report – $1.8bn, smaller than Palestinian market
      • High growth company that is super opaque – can’t meet or won’t meet with CEO, maybe some others that state-owned enterprises that just want to keep paying salaries; maybe 5-10 companies that are investable
        • Equally weighted these companies initially and was still learning – now, he developed relationships and is on the board for companies
      • Does he want to put more $ and concentrate on the companies that he trusts and follows the guide
    • Largest holding for them – Baghdad Soft Drinks (Pepsi bottling and distribution for Iraq) – mixed-owned enterprise in the 90s
      • Local businessman (Pepsi is the dominating market share – ~70-80% vs elsewhere where it’s split) saw it was mismanaged
      • By 2008-09, were going to default on the loan they were floated – businessman bought the loan, fired management & 2000 ee’s, switched equity
        • Within a year, it was fully certified, tripled production, profitable business
    • Now, 5 days a week but still 2 days a week – had foreign investor interest, $5-6bn, couple IPOs successful
      • One telecom has about $1bn market cap, 14.5% dividend yield, 33%+ FCF yield
      • Foreign money came in 2013 but ISIS scared them off – coming back and interest from banks – arranging trips and momentum
      • Key problem – no 3rd party custodians (compared to Jamaica or even in Africa, HSBC or anything) – working on getting one
        • Makes it difficult to bring in foreign money – exchange is the custodian (which is actually safe)
        • No margins, cash-based market and settlements of t+0, no short selling (can’t sell unless have stock)
      • Oil collapse depressed prices and ISIS issue but has been up over last 2-3+ years, cheaper today than when he invested 11 years ago
    • Multiple expansion is the question for returns – 1x to 25x or 4x to 15x – depends on what they are as compounding
    • Kobe Bryant complimented him after a junior year game in high school – already looked at as a superstar – saw Geoffrey was dejected
  • Nikos Moraitakis (@moraitakis), Founder, CEO Workable (20min VC FF024)
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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.
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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)
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    • Looking at field – ML Engineer to Quant/Statistician to Product Analyst to Data Analyst
    • Research tips: Open source projects (understanding current trends, gain experience, make connections)
      • Job descriptions (take time to research the differences, tailor approach)
      • Market research (professional networking, connect dots between companies you’d consider)
    • Resume tips: Concise (focus on telling a story on the experiences to highlight outcomes)
      • Factual (if listing skills or strengths, use examples to support them)
      • Related experience (highlight specific projects related to area you’re applying)
    • Networking tips: Professional profile (be detailed but concise about the skills you use and experiences)
      • Targeted outreach (connect after a conference, target approach for conversation)
      • Conferences (meet/greet if possible, follow up via email, LinkedIn, twitter)
  • Gidi (Gideon) Nave (@gidin), Assistant Professor of Marketing (Marketing Matters)
    • Cambridge Analytica before Cambridge – music research and how it relates to certain traits
      • Extroversion and openness were 2 big ones that they could pull from 5 traits (MUSIC)
      • MUSIC: Unpretentious, Sophistication were 2 of them
    • Could pull personality cues from 20 second, unreleased clips based on scores of 1 to 7, also
      • More agreeable people had higher scores in general
    • Personality on 5 (OCEAN – Openness, Conscientiousness, Extroverted, Agreeable, Neuroticism)
      • Questioned whether they could use music to test for the personality (as opposed to the other direction)
      • Personality is established at a young age, so can music likes on Facebook give you a personality side – as mentioned, it did ~2 better than others
  • Fintech for Startups and Incumbents (a16z 4/7/2019)
    • With GP Alex Rampell (@arampell) of CEO/cofounder of TrialPay and partner Frank Chen
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    • Assembling a risk pool (good and okay drivers subsidizing the bad drivers, or healthcare – same)
      • No economic model for skipping a segment – psychology for half price insurance (say, going to gym)
      • Half the number of customers – taking the ‘good’ ones, profitable ones
      • Insurance has mandatory loss ratios for different industries
    • HealthIQ – mechanism for exploitation on ‘health’ – in FinTech, it was SoFi on HENRYs
      • Positive vs adverse selection – debt settlement company ads, for instance – negotiate on your behalf to settle
      • Healthier people as living longer than non-healthy people – left them more profitable for proving being better
        • Gives them good customers (adverse selection for ‘quick, no blood test, 1 min’)
    • SoFi as stealing customers from the normal distribution – better marketing message “you’re getting ripped off, come to us”
    • Branch as investment – collect as much data as possible and look for correlations – small, mini-loans
      • Induction as pattern is a willingness to pay (credit is remembered) – went and got data from your phone
      • How many apps did you have? Did you look like you went to work? Are you gambling?
        • Counterintuitive potentially: battery goes dead leaned default, gambling app meant more likely to pay, etc…
    • Earnin – phone in pocket for 8 hours, last paycheck and RTS data confirming – will give money that you have earned but don’t have yet
      • Can tip interest or not – can you encourage people for positive community and people driving safely
      • Nurture better behavior – helping to turn customers into correctly priced customer (vs bank that doesn’t want them)
    • Vouch as company that failed but your social network had to vouch for you – Person X is okay, so you can even put up $
    • Tiffany & Co for a long time was owned by Avon lady – but its brand was massive and one of most renown jewelers
      • Could make sense for acquiring more customers, though
    • Killing Geico – take 20% of customers but only take the good ones
      • Selling negative gross widgets, for instance – probabilistic ones (and the bad ones aren’t needed)
    • Turndown traffic strategy – Chase turns down a lot of people for problems (can’t profitably do $400 loan, for instance)
      • Here’s a friend after they rejected them (but see traffic) – Chase will tell you to go to a startup for better underwriting
      • Amazon got right – HP book, for instance – had ad for B&N right next to Amazon (bought) – would make $1 on the ad click at 100% profit
        • Used this to reduce the price on their site and wasn’t sharing it
    • Rapid fire: “Always invest super early” – 9 weeks to decision vs 1 day – can’t get good deals at length
      • Best things aren’t cheap – they’re often expensive – better strategy can be plowing in late (“Can’t believe we’re putting this much $”)
      • Gating item for entrance into a space or into different models – cost of capital and distribution as often the unique thing
        • Geico could easily add additional traffic to start-ups
      • M&A strategy early? Encouraged and used Facebook – buy existential threat (surrender 1% of market cap to buy Instagram)
        • Facebook spent 7% of market cap for WhatsApp, Oculus, etc…
        • Buy the guys that failed trying – courage to build something new -> take them and put him in charge for person that was successful (at big co)
          • Trying to build this thing for ~10 years vs start-up that built something in 1 year (put this one in charge)
          • Ex: AmTrak buys Tesla – worse thing “You work for us” but you want products to push distribution and talent for understanding
        • Only difference was distribution and the possibility to do that
  • Jennifer Dulski, author of Purposeful, Head of Groups & Community at Facebook (Wharton XM)
    • Talked about their group initiatives at Facebook – communities policing themselves as well as methods to flag content
    • Mentioned example of having an employee that came up to her and asked if she had done a good job, she just wanted a bonus or something $
      • Taught her about incentives and why people do what they do / good to know the motivators
      • What drives people?
  • Anth Georgiades, CEO of Zumper (Bay Area Ventures, Wharton XM)
    z-pm

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

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

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
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