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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)
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    • 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)
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    • 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

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

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

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

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

  • Samir Kaji, (@samirkaji) MD @ First Republic Bank (20min VC 093)
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    • Leading private bank and wealth management, before at SVB
    • 1999 – “anyone with a pulse could get a job” but he was working selling vacuum cleaners at dept store
      • Was told by family to get a real job – applied to first business SVB, got resume in and interview immediately before starting
      • First couple years were tough – learned a lot, but was 2004 until companies had scaled and were getting bigger
    • First 10 years were tech companies, series A and B and venture debt – post 2009 Lehman / Bear, went to venture group at SVB for 4 years
      • Made the move with a few others from SVB to First Republic, now leading team in micro-VC and early-stage tech co’s
    • Says the micro-VC is more entrepreneurial & collegial compared to extended stage VC’s
      • First fund is that you can get traction for a second or third one, fees as pressure – most likely why many people come from some wealth
        • Writing large checks as GP, as well
      • 2-2.5% management fees initially vs 1 / 25 or 1/30 model
      • 1999 – 2002 distribution was 0.9x and you’d get 10x return (whoops) – very difficult for funds to get 2-3x for LPs
    • Barriers to entry much smaller for $20-25million as compared to $500mln – institutional, etc — he can go to family friends and high net worth
    • Seed over next 5 years: contraction in space (wrong), but said there isn’t enough returns for funds to max it
      • 1100 in the 2000 year and burst
      • Continued prominence of Angelist platforms, maybe an integral part of the ecosystem
      • Starting to see use of data (Mattermark, CBInsights, SignalFire) to more efficiently identify and action at this level
    • Favorite book is Phil Jackson’s – behavioral psychology, Give and Take is another one
    • Really respects the pioneers of the industry and first-time fund-raisers
      • Mike Maples, Michael Deering, Steve Anderson, Jeff Clavier when it wasn’t a thought
    • Habit – reading book or blog post for 20min in the morning before email
      • Disconnect from audio / video devices and reflect for an hour
      • 2 hours a day for family/friends and disconnecting, as well
    • Thomas Redpoint, Mark Suster, Brad Feld, Strictly VC, Ezra at Chicago Ventures
    • Knows awesome fundraisers but terrible at returning capital – didn’t mention any
  • Collectively Driving Change, Laurene Powell Jobs and Ben Horowitz (a16z 5/27/2019)
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    • LPJ – founder, president of Emerson Collective
    • Grew up in NJ – father passed away in a plane accident when she was 3 – 3 children.
      • Mom remarried so there were 6 of them. Wooded area of NJ.
      • Core values and dedication to education to get out of the area.
      • She went to Upenn – first student from her high school that went to Ivy League – ~20% went on to more schools
    • Addressing East Palo Alto school as a volunteer to help – 1st talk, 0 had taken SATs
      • What happens when you’re first to graduate high school? What’s it mean to the information from family?
      • What happens to be first to want to go to college, thrive&complete it?
        • To have the aspiration, can be a leader in the family – translator, get sucked into all problems
      • Started with 25 freshmen – would have to come with friends for responsibility mechanisms – for College Track
        • 3000 high school students, 1000 college, 550 grads
    • Collective of leaders, innovators – education inequities, access and need for enhanced/robust curriculum
    • 10 year time horizons – getting them together is scheduled with Monday all-staff meetings (3×3 matrix of videos)
      • 5 cities, sometimes philanthropic speakers or reports
      • Discussion of reading as you fall behind through third grade before switching to reading to learn – already behind
    • XQ as SuperSchool dream – 17 of 19 will open in August
    • Caring about impact and solving problems, not wealth increasing – wants access to policy or money and not taxes
      • Judged Giving Pledge for not wanting to be more philanthropic
      • Environmental, edtech portfolio, cancer / oncology investments, immigration incubator, new thinking to old problems
    • How do you know when you’re succeeding? Collecting data on everything they do.
      • Example: XQ – schools and districts, state of RI as switching to statewide competition
      • Chicago has good data for fatal/nonfatal deaths (I disagree)
    • Imperiled or important institutions like journalism and media need to be sustained, how many join?
      • Concentrating and following where IQ is migrating (hahaha – what a joke)
  • Data Infrastructure in the Cloud, Rohan Kumar at BUILD conference (Data Skeptic, 5/18/19)
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    • Corp VP of Eng of Azure Data Team at Microsoft – SQL and data services, open source, analytics, etc
    • Trends in data engineering in the cloud, serverless and hyperscale
      • ML and AI and enabling applications – shifting to edge vs cloud – analysts predict 70% will be on edge devices
      • Solutions and private edges – training in the cloud and deploy them on the edge applications
        • Data platform needs to be the right foundation
    • Highlight for him from conference: work they’ve done on relational databases in the cloud – as volumes grow, scalability challenges
      • Hyperscale for Azure and PostgreSQL, as well as MS SQL soon enough – system scales with needs (they’ve tested <= 100TB)
    • Acquired Citus Data, support scaling out the compute layer – strong team, great product, matches in Azure and open-source
    • Releasing serverless option for Azure database – costs designed to stay low and optimized
    • Analytics side: customers wanted to do real-time operational analytics – didn’t want to move them outside of their core product
      • How is data distributed and having compute be co-located with the data to gain Spark efficiency being nearest to node
      • Support Jupyter notebooks across all APIs to modernize to do more predictive analytics
      • Attempting to build out pipelines requires too much scripts, instead have Data Flows in Azure Data Factory – no-code and UI
      • Wrangling data visually and seeing if something can be recognized or learned to repeat across other columns/tables
    • Latency won’t be ideal if compute nodes occur nonlocal to the data changes – can’t do 50,000 nodes all at once
    • Excited for the future: Horizon 1 (next 8-12 months), Horizon 2 (~3 years), Horizon 3 (moonshots)
      • H2: Hardware trends, what do customers want? Pushing boundaries of AI and ML, healthcare, gaming, financial services, retail
  • Wide or Deep? David Epstein, author of Range (Invest like the Best, 5/28/19, ep. 133)
    • First book’s research lead him to get into specialization and finding kernel for next
      • Some countries: turning around national sports teams – why don’t we try other sports? Contrary to 10,000 hour rule.
      • SSAC – debating Gladwell – athletes have a sampling period instead of first gene – delay specialization
        • Used Tiger vs Roger – Roger had tried a ton of sports vs Tiger who was born and was playing golf
    • He was not good at predicting what people/public would attach themselves on to – 10,000 hour rule – race/gender as most talked (but weren’t)
      • 10,000 hour rule were based on 30 violinists in world famous music academy (restriction of range)
      • Height in American population vs points scored in NBA (positive correlation) but if you restrict height to NBA players, negative
    • Finnish cross country skier who has genetic mutation similar to Lance’s boosted
      • Sensitivity to pain and modification to your environment – also sudden cardiac arrest in athletes (what pushed his interests)
      • Book as opposition to Outliers and Talent Code – interpreted a lack of evidence as evidence of absence (genetics matter)
        • First year he read 10 journal articles a day and not writing – they were making conclusions they could not make based on their data
      • Differential responses to training – best talent were missed because we don’t know about training responses
    • Collection and exploration phase – competitive advantage for expansive search function to connect sources or topics
      • Has a statistician on retainer, essentially, to check models or surveys
      • Wanted to know what he was missing – “how come I broke the 800m women’s world record after 2 years of practice? – genetic difference”
        • Racing whippets – 40% had a genetic defect that gave them more muscle and oxygen
    • All of sports as a limited analogy (problem after Sports Gene; now, more tempered)
      • Robin Hogarth addressed “When do people get better with experience?” Don’t know rules, can try to deduce them but can’t know for sure.
      • Kind learning environment: feedback immediate, steps clear, information, goal ahead
      • Wicked learning environment: can’t see all information, don’t wait for others, feedback delayed/inaccurate
    • Study at Air Force on “Impact of Teacher Quality on Cadets”
      • Have to take 3 maths – calc I, II, III (20 kids randomized) – professors best at causing kids to do well (overperforming) systematically undermined their performance thereafter
        • 6th in performance and 7th in student evaluations was dead last in deep learning
        • Narrow curricula were better at the test that they had at the end would be negatively correlated with going forward in performance
      • Teachers that ignored what was on the test taught a broader curriculum (making connections vs procedures)
    • Learning hacks: Testing (wonderful – primed to test ahead of learning), Spacing (deliberate not-practicing, Spanish ex spread 4 hour twice, 8 hours), Mixed practice
      • Ease is bad – known time horizon for when you have forgotten again – interleaving and spacing mixed
    • Passion vs Grit (“Trouble with Too Much Grit” – Angela Duckworth’s research)
      • Duckworth did a study at West Point for East Barracks cadets – candidates score (test + leadership + athletic) was not good prediction of doing this (overall it was good)
        • Grit was a better predictor for making it through East Barracks – she questioned whether it had an independent aspect
        • Variance for grit was probably 1-6%, especially after “flattening” groups – looking at people that had a narrowly defined goal for short periods (cadets or spellers)
      • Cadets were scoring lower on grit at late 20s vs earlier – tried some things, learned others about what they want – grit is poorly constructed
        • Look holistically – if, then signatures (giant rave – introvert, small team – extroverts) right fit looks like grit – developmental trajectory as explosion matching spot
    • Choosing a match for a future them who they don’t know in a world they can’t comprehend – people that find good fits (in practice, not theory)
      • Paul Graham’s “Commencement Speech” that he wrote “Most will tell you to predict what you want in 20 years and march toward it.” (premature optimization)
        • Everything you know is constrained by our previous experiences – limited as a teenager – just expanding and learning as you go forward
    • Gameboy example – with so much specialized information that can be disseminated easier – can take from all types of domains and recombine them
      • System of parallel trenches – can be broader much easier now – hired people for Japanese and German translations
      • Japanese man profiled in his book – technology was changing faster than sun melts ice – didn’t get Tokyo interviews
        • When he got to Kyoto company making playing cards, he was a tinkerer who was maintaining machines – started to mess with them (arms)
        • Turned them into a toy, and it was Nintendo – cartoon-branded noodles (failed), and had toy development
          • Lateral thinking with withered technology – stuff that’s cheap, easily available – takes into other areas
            • Remote control, more features – wanted to democratize this and strips it down – LeftyRX only left-turns
        • Sees calculator from Sharp and Casio and thinks he can do a screen and handheld game – small games
          • Had issues with Newton’s rings so he found other small tech (credit cards embossed) to fix small pieces
      • What it lacked in color, graphics and durability (could dry it out, batteries would be fine, split it up, “app” developers because it was super easy to understand)
      • In areas that next steps were clear, specialists were much better – less clear, generalists were more impactful – depends on the specificity of the problem
        • 3M had a lot of areas for this, “Periodic Table of Technology” – post-it note came from reusable adhesive that had no use for
        • Only Chinese national woman to win Nobel – “Three No’s” (No post-grad, foreign research, membership in academy)
          • Interest in science, history – Chinese medicine for treatments of malaria – world’s most effective treatment from ancient text
  • Greg Isaacs, BEN (Branded Entertainment Network) (Wharton XM, Marketing)
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    • Discussion of getting data from Netflix / Amazon / Hulu / tv to better match brands and advertising
      • Dirty data via a wharton grad who set up a survey style
      • Cohorts and demographics, along with psychographics
    • After getting data, attempting to approach Youtubers / social media influencers, tv spots and channels or shows to get their brands in front of the right people
      • More pointed, depending on what interests are for their cohorts
      • Creative storytelling as the change of cultural mind shift has increased
  • Understanding the Space Economy, Sinead O’Sullivan (@sineados1), entrepreneur fellow at HBS (HBR IdeaCast #684, 5/28/19)
    • Facebook, Amazon (3000), SpaceX (12,000) and other funding like Blue Origin / SpaceX / asteroid mining or travel
    • Global space economy as $1tn by 20 years – currently $325bn so it would need to 3x
      • Breaking apart space resources and otherwise – earth-focused (delivering or existing in space that helps earth)
        • Exploration or creating interplanetary existence
    • Running out of space in space for satellites – comparing to airplane docking / loading
      • $2500 per kg now to launch, used to be $50k / kg
    • Reliance had been on unilateral agreement for space policy – one tech startup launched a satellite that didn’t have permission (but no fall-out)
      • Food / grocery stores, wifi, phone, insurance pricing due to satellite data – reliance on services are increasing as the market increases
      • Thinks that we’re close to seeing the cheapest cost of launching – cites SpaceX, but won’t allow everyone to participate
    • Ultrahigh accuracy will require higher powered satellites – GPS, nonmilitary grade is ~0.5 m – thinks it will prevent autonomous vehicles solution
    • Ton of money going into asteroid mining but thinks it’s better for testing missions to Mars and figuring out the problems for future
      • Looking at Uber at start and say “people won’t get into a stranger’s car” or other cases as how we see the future – going to Mars, etc
    • Earth-focused space technology – 100+ launched satellite start-ups, micronano satellites, relay companies, downstream analytics
      • More touchpoints for everything in this manner
      • SpaceX will increase public and government intervention and within 50 years, maybe see a human launched there
  • Investing w Twitter Sentiment, Andy Swan (@andyswan), LikeFolio (Standard Deviations, 4/25/19)
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    • 1700+ tweets examined per minute in LikeFolio – discovering consumer behavior shifts before news
      • Direct partnership with Twitter to create massive database and how they’re talked about to look for mentions
      • Purchase intent, sentiment mentions – trends across product categories or brands
    • Example – Delta (as host is a loyalist) – making adjustments
      • Expectations are the relative part – comparison to the baselines (metrics compared to itself as baseline)
    • Put out a comprehensive report on Apple day after keynote event – September 14, 2018
      • Consumers were unimpressed with iPhone lineup – more price sensitive than maybe they’d considered
      • Apple Watch was the silver lining – stock / sales may struggle over 3-9 months (upgrade cycles)
    • WTW version of keynotes – NYE resolutions – subscribing early to drive revenues the rest of the way
      • Purchasing mentions were only up 30-40% compared to 5 or 7x weekly mentions (big difference)
    • Shelf-life and how to consider the sentiment data – lead time may be binary corp event (same store sales or year)
      • Couple months with Apple, for instance, but with Crocs – resurgence that persisted to current time
    • Set up keyword structure and brand database – “I’m eating an apple” as opposed to an Apple mention – human eyes to ‘label’
      • “Closed my 3 rings” – apple watch but sarcasm / spam that wasn’t caught (estimates at 2-3% of data)
      • If spam / sarcasm are consistent portions of the data, doesn’t really have an effect
    • Twitter Mood Predicts Stock Market – Bollen, Mao, Zeng (88% and 5-6% predictions) – fund closed up shortly
    • Advantage being better than analysts or pricing and codifying sentiment behavior compared to past quarters, data
      • Some consumer trends analyzed as true tipping point or actual movements
      • Public prediction before productizing their modeling – made 40 and were 38-2 (confidence as highest)
      • Investing as very specific, concentrated and holding ammo compared to trading with option spreads and has risk profile built
    • https://arxiv.org/pdf/1010.3003.pdf
    • Diversification as 20-25 stocks, doing it over time and with conviction can be done
    • Starting in Louisville for his fintech company, host in Alabama, for instance
      • Talent can be more difficult to seek out but the world is globally flattening via the internet
      • 70% lower overhead cost than being in SF, for instance – developers would anyhow be in Slack channels / not a big deal
      • Reduction in cost maintains greater control of company since they don’t have to take reduction of equity to gather more
    • Network effects don’t matter if you don’t have a great product or product-market-fit
    • Free association game
      • grapenuts: best cereal (Co’s been around for 100+ years, branding and $ spent and they can’t figure it out)
      • Fintech Future: individualization and customization
      • Victory: most important thing in life, achieved what you set out to do – setting goals and achieving these
      • Bourbon: pappie von winkle – collecting for dust on shelf 10 years ago and now going for $3000
  • Jonathan Abrams, co-founder Nuzzel news (Launch Pad)
    nuzzel

    • Landing hedgehog as the mascot – animal as cute, 99designs and surveying 50 friends – 25 men/women
    • Discussing how VC’s don’t have great advice, especially when general – too hard to be an expert in such a wide range
      • Finds it easier to be very context-driven and providing solutions or action-oriented questions to founders
      • Investing now easier with YC and Angelist, etc…
    • Timing and other mistakes he made – out of control, losing equity part early (but depends on where you are / what you need)
  • Etan Green, professor at Wharton (Wharton Moneyball)
    • Discussion on paper of how sharp money comes in at horse racing tracks
      • Difference between sites – fairground action compared to tracks, and specific to region (New Orleans, Minnesota, for instance)
      • Big sharp money comes in very late, pushing the underdog prices to higher values
        • More expensive to bet while at the track than the APIs enabling higher volume bets
        • Books at the track are incentivized to bring in as much $ as possible, so $0.20 on $1 vs $0.15 rebate on $0.20 for volume
    • Value and differences in how people will bet
  • Edith Dorsen, Women’s VCFund founder, MD (Wharton XM)
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    • Talking about their focus on first fund, approach
    • Opportunity for finding diverse founders, 25% of their fund had a woman founder
    • Starting a second fund
    • Had consumer tech, enterprise and not so much b2b, but trying to increase
      • Hard to say or give advice if one of their partners don’t have expertise in the domain
  • Sophie Lanfear, Silverback Films producer on Netflix “Our Planet” (Wharton XM)
    • Species that are dying, going extinct
    • What we can do about it
  • Aliza Sherman, Ellementa co-founder, CEO (Wharton XM)
    logo

    • Discussion of client talks when she made them aware of her cannabis endeavors
    • How friendly the community is
      • Then knocked the idea that ~30% was female to start before diving off a cliff
    • CBD to mask opioids – does it really do anything from a pain/treatment perspective, though?
      • Anti-chemo because of CBD – really?
    • Sounded too rehearsed – made it sound fake, not genuine
      • Passion/motivation/mission and kept repeating as the best advice she could give – painful

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

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

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

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

Hope you enjoy! Leave a comment or follow along!

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

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

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

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

The Journey (Notes From Feb 25 – March 3, 2019) March 22, 2019

Posted by Anthony in cannabis, education, experience, Founders, global, Hiring, medicine, questions, social, training, Uncategorized, WomenInWork.
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I wanted to focus on the variety of journeys that these amazing people have been  on. All different, all learning. The commonality of assessing one’s place and moving strategically to take advantage of an opportunity that allowed each of them to do what it was, at that time, that they wanted to do or focus. I believe that is an innate skill.  Some have to build up to have the confidence to assess what they want. Others let it sit in the back of their mind until someone brings it out.

As a founder, I believe that becomes even more of an important skill. You have to not only know what you want to chase, but also where you want to go. Then, follow that up with being able to creatively attract others to do the same – whether they’re investors, customers, or potential employees/partners.

  • Julia Silge, co-author of Text Mining with R (Data Skeptic 2/22/19)
    cover

    • At StackOverflow now, phD in astrophysics, astronomy
      • Worked in academia and went to edtech start-up for academic development
      • Transitioned into data science – had needed to brush up on some of the skills and updated machine learning
      • Data scientist for 3-4 years
    • Did some public work for her portfolio, worked with state stuff on Drought, etc…
      • Thought about NLP for analyzing Jane Austen texts (public, projectgutenberg), and opened it up
        • Which parts of book have narrative more sad / joyous and sentiment analysis with heat maps
      • Started to develop TidyText package and R build with a friend – bridging text and R analysis
    • Using R as data science
      • Tidyverse database, messy real source & into the form she needs quickly
      • Mature community for statistical modeling in R
      • Text classification – regex as building blocks for effective results
    • At StackOverflow – texts every day and statistically analyze the numbers
      • Developers survey as one of the largest projects
    • Book for people who may have tried other approaches with text
      • 1st half lays out concepts, common tasks in text mining
      • 2nd half is beginning to end case study – eda, what’s in dataset, implementation of model
  • Brian Wong, Founder at Kiip (20min VC FF018)
    230px-kiip_logo_image

    • Started after university at Digg (laid off after 6 months) before starting Kiip, focused on mobile rewards network
    • People he truly knows are the ones he’s been with over 5 years
      • True Ventures, Relay Ventures, AMEX ventures, Hummer Winblad
    • Founder-friendly in his terms: creating services and ecosystem of the founders among the invested, not taking a massive chunk immediately
      • Services as you’re getting formed, early on
    • Quiet with his board – once every two, three months meet up, depending on financing
      • Sources for him if he needs others, find specific customer or advisor, analytically looking at problems
      • Trained by True Ventures initially about dealing with the board
    • Gamification tactics derived from Predictably Irrational book
    • “Nothing is ever as good as it seems and nothing is ever as bad as it is”
    • Jason’s Calacanis blog – seems to agree with a few
    • Inspired by a few founders: Elon, Elizabeth Holmes; moreso maybe less loud founders, Mike (one of his investors – NASA scientist)
    • Favorite apps: Tinder for dating, Evernote, Box app (storage – mobile app is awesome – faster than DropBox)
    • For Kiip, ad-blocking fever-pitch and being ones that can help – MasterCard as one of their big partners, usage / app data that they’re sitting on
  • Matt Lerner, Distro Partner with 500 Startups (20min VC 082)
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    • Runs the London office, specializing in conversion optimization, analytics engagement, retention
    • Helps them build and grow (Distro team) to growth engines and scale
    • Worked at PayPal in 2004, marketing director initially before later
    • Skype calls over 45 minutes, brainstorm over tests with a cycle time and see results in 48 hours – 2 days
      • Was told he could do this full-time and enjoyed it (Distro Dojo – growth to product-market fit)
        • Invest in post-seed, pre-series A typically – early stage / accelerator program for earlier
      • In London, he looks for live, functioning product with corpus of people out of beta
    • Talked about Mayvenn (connecting to the NEW episode about Series B) – Series A here
    • Where in the funnel do you need to focus on?
      • Understand the business, then brainstorm in the “dojo” – all kinds of ideas
        • 20% CTA button change occasionally – not always
    • Just invested in Fy, Founder Tom in Berlin – built entire business with growth in mind
    • Anna Kerenana “Happy families are all alike but each unhappy family is unhappy in its own special way.”
      • Companies don’t get their product out to customers in a way
      • Measuring / optimizing for wrong targets
      • Tactical things to ensure spend is done properly
      • Way to test quickly – 4 Hour Workweek – Bought 5 different ad-words and checked his titles for unpublished book
      • Paid acquisition in way that CAC is much lower than proven LTV of customer, can go quickly through advertising
        • Most businesses need organic acquisition channels over paid
    • Ultimate growth hacker – David McClure (his boss) – pirate metrics talk (viewing of video)
      • Sean Ellis (from DropBox, GrowthHackers.com owner) – mentored him at PayPal – attachment too big for email, send DropBox
      • Eddie Johns (Growth at Wealthfront, before at Quora and Facebook)
      • In London, Millen Paris?
    • Favorite growth hacking tools: MarTech talk, 500 Startups for best tools
      • Deck in show notes, Top 35 and Top 10
    • Books: The One Thing You Need to Know?
    • Tamatem – exciting startup in Dojo, Middle Eastern mobile games publisher
      • License other successful games, translate them, half the revenue and found money for developers
      • Don’t have to be good at making games – just need to have the database and quick adoption of other games
  • Chuck Smith, CEO / co-founder of Dixie Brands, Cannabusiness (Wharton XM)
    dixielogo

    • Discussing CBD vs THC products and difference in integration / vertical distribution
      • THC requires state and full distribution
      • CBD can be sold online
    • Keeping the brand as a reputable one and making sure it sees plenty of time
    • Partnership with Latin American company for full integration / distribution channels, laying foundation for easy process
      • Ventures with other companies to engage quickly or acquisitions
  • Solomon’s Code authors (Wharton XM)
    • Olaf Groth, Mark Nitzberg
  • The Ultimate Side Hustle – Elana Varon (Wharton XM)
    • Different types of start-ups and trading compensation (time vs money)
  • Marvin Liao (@marvinliao), Partner at 500 Startups – SF accelerator (20min VC 083)
    • 10+ year vet at Yahoo!, came to Bay Area/Silicon Valley in 1999 tech boom, laid off  2001
    • Left Yahoo in 2012, did angel investing and speaking at conferences, mentoring
    • Learned investing game by angel investing, though, to his wife’s scolding, didn’t do well
      • Operator as investors – used to be in the same role – lots of services
      • Online marketing / sales experts in accelerator in the portfolio
      • Both models-Greylock, Accel vs 500 Startup & First Round,service-based)
    • Why 500 Startups? Strongly focused on sales and marketing – fit for him, especially being international (global)
      • First 2-3 meetings or intros are free, but after that – some value returned
    • Went from 1100 companies down to 36 for the accelerator
      • Seed fund – 12-30 cos a week, one inv ~2 weeks – not necessarily random
    • Average check size is $50-100k – doesn’t take board seats but gets board observer rights
      • Look at pre-launch phase, consumer mobile phase wants to see traction (10mil vs 1mil downloads)
      • Won’t look at enterprise SaaS pre-launch, wants to see $10-15k mRR in established space
      • Different industries requiring different attention
    • Industries that he’s looking at – marketplaces / platforms (SkillBridge), digital health
    • Challenge in his 2 years: cycles of learning (shocked that there are arrogant investors), still treats himself as a complete novice
      • Great investor and develop the instincts, thesis and to risk being wrong a majority of the time
    • Favorite book: Art of Worldly Wisdom, Dune (science fiction – key) – SingularityHub
    • Calend.ly and Evernote, Amy.X.AI (?)
    • Take on Yahoo: “They’re toast.” No disrespect to Marissa – trying M&A and most big companies aren’t good.
    • Challenge for 500S: scaling @ quality, going from 2 accelerators to 4 in Silicon Valley
      • Lucky and systematic difference to get to that point
    • Interested in the most recent batch: Neighborly (batch 10, fintech – hates Wall Street so disrupting this), AgFinder (agtech – not much attention but such a vital part of the global problem)
  • Ashley Whillans (@ashleywhillans), Asst Prof at HBS in Negotiations, Orgs and Markets (Wharton XM – Time Poverty)
    • Went through study in Canada with subjects that would receive $40
      • One group subjected to restriction that it has to be spent on “time saving”, other could be whatever
        • Measured happiness after each day (with a call)
      • Time saving could be fast food of some sort, hiring a neighborhood boy to shop, etc…
      • Happiness was higher with the $40 spent for time saving
    • Check the white paper for time saving and happiness
  • Elizabeth Hogan, Brand Dev at GCH, Cannabusiness (Wharton XM)
    • Discussing various levels of products – CBD vs THC and other treats
    • Company founded by Willie Nelson in 2015
      • Willie’s Reserve (flower, edibles, vape products at both med and rec dispensaries)
      • Willie’s Remedy – CBD oil-based products – talked about the neuroscience behind activation with cbd products
    • 8 oz cups of coffee with 5mg dose of CBD – often bring as product demos for concerts, festivals, events
    • Marketing is difficult because of federal regulations and the big marketing channels – Facebook, Instagram, Google, etc
      • Some influencers have been used but have to be careful – can lose their accounts if wrongly done
    • Plenty of organic marketing currently, but looking for paid channels has been a difficult task
  • Hooked author, Nir Eyal, (Wharton XM)
    • Habit building – playing on pains
      • 4 different ways to take market shares
        • Velocity, frequency (think)
      • Pains as psychological effects – pleasure as a result, and minimizing pain
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