Search results for: “startup”

  • The only 3 things a startup CEO needs to master

    So, you watched Silicon Valley and read some articles on Techcrunch and you envision yourself as a startup CEO 🤑. What does it take to succeed? Great engineering skills? Salesmanship? Financial acumen?

    As someone who has been on both sides of the table (as a venture investor and on multiple startup executive leadership teams), there are three — and only three — things a startup CEO needs to master. In order of importance:

    1. Raise Money from Investors (now and in the future): The single most important job of a startup CEO is to secure funding from investors. Funding is the lifeblood of a company, and raising it is a job that only the CEO can drive. Not being great at it means slower growth / fewer resources, regardless of how brilliant you are, or how great your vision. Being good at raising money buys you a lot of buffer in every other area.
    2. Hire Amazing People into the Right Roles (and retain them!): No startup, no matter how brilliant the CEO, succeeds without a team. Thus, recruiting the right people into the right positions is the second most vital job of a CEO. Without the right people in place, your plans are not worth the paper on which they are written. Even if you have the right people, if they are not entrusted with the right responsibilities or they are unhappy, the wrong outcomes will occur. There is a reason that when CEOs meet to trade notes, they oftentimes trade recruiting tips.
    3. Inspire the Team During Tough Times: Every startup inevitably encounters stormy seas. It could be a recession causing a slowdown, a botched product launch, a failed partnership, or the departure of key employees. During these challenging times, the CEO’s job is to serve as chief motivator. Teams that can resiliently bounce back after crises can stand a better chance of surviving until things turn a corner.

    It’s a short list. And it doesn’t include:

    • deep technical expertise
    • an encyclopedic knowledge of your industry
    • financial / accounting skills
    • marketing wizardry
    • design talent
    • intellectual property / legal acumen

    It’s not that those skills aren’t important for building a successful company — they are. It’s not even that these skills aren’t helpful for a would-be startup CEO — these skills would be valuable for anyone working at a startup to have. For startup CEOs in particular, these skills can help sell investors as to why the CEO is the right one to invest in or convince talent to join or inspire the team that the strategy a CEO has chosen is the right one.

    But, the reality is that these skills can be hired into the company. They are not what separates great startup CEOs from the rest of the pack.

    What makes a startup CEO great is their ability to nail the jobs that cannot be delegated. And that boils down to fundraising, hiring and retaining the best, and lifting spirits when things are tough. And that is the job.

    After all, startup investors write checks because they believe in the vision and leadership of a CEO, not a lackey. And startup employees expect to work for a CEO with a vision, not just a mouthpiece.

    So, want to become a startup CEO? Work on:

    • Storytelling — Learn how to tell stories that compel listeners. This is vital for fundraising (convincing investors to take a chance on you because of your vision), but also for recruiting & retaining people as well as inspiring a team during difficult times.
    • Reading People — Learn how to accurately read people. You can’t hire a superstar employee with other options, retain an unhappy worker through tough times, or overcome an investor’s concerns unless you understand their position. This means being attentive to what they tell you directly (i.e., over email, text, phone / video call, or in person, etc.) as well as paying attention to what they don’t (i.e., body language, how they act, what topics they discussed vs. didn’t, etc.).
    • Prioritization — Many startup CEOs got to where they are because they were superstars at one or more of the “unnecessary to be a great startup CEO” skills. But, continuing to focus on that skill and ignoring the skills that a startup CEO needs to be stellar at confuses the path to the starting point with the path to the finish line. It is the CEO’s job to prioritize those tasks that they cannot delegate and to ruthlessly delegate everything else.
  • Advice VCs Want to Give but Rarely Do to Entrepreneurs Pitching Their Startups

    Source: Someecards

    I thought I’d re-post a response I wrote a while ago to a question on Quora as someone recently asked me the question: “What advice do you wish you could give but usually don’t to a startup pitching you?”

    • Person X on your team reflects poorly on your company — This is tough advice to give as its virtually impossible during the course of a pitch to build enough rapport and get a deep enough understanding of the inter-personal dynamics of the team to give that advice without it unnecessarily hurting feelings or sounding incredibly arrogant / meddlesome.
    • Your slides look awful — This is difficult to say in a pitch because it just sounds petty for an investor to complain about the packaging rather than the substance.
    • Be careful when using my portfolio companies as examples — While its good to build rapport / common ground with your VC audience, using their portfolio companies as examples has an unnecessarily high chance of backfiring. It is highly unlikely that you will know more than an inside investor who is attending board meetings and in direct contact with management, so any errors you make (i.e., assuming a company is doing well when it isn’t or assuming a company is doing poorly when it is doing well / is about to turn the corner) are readily caught and immediately make you seem foolish.
    • You should pitch someone who’s more passionate about what you’re doing — Because VCs have to risk their reputation within their firms / to the outside world for the deals they sign up to do, they have to be very selective about which companies they choose to get involved with. As a result, even if there’s nothing wrong with a business model / idea, some VCs will choose not to invest due simply to lack of passion. As the entrepreneur is probably deeply passionate about and personally invested in the market / problem, giving this advice can feel tantamount to insulting the entrepreneur’s child or spouse.

    Hopefully this gives some of the hard-working entrepreneurs out there some context on why a pitch didn’t go as well as they had hoped and maybe some pointers on who and how to approach an investor for their next pitch.

    Thought this was interesting? Check out some of my other pieces on how VC works / thinks

  • Geothermal data centers

    The data centers that power AI and cloud services are limited by 3 things:

    • the server hardware (oftentimes limited by access to advanced semiconductors)
    • available space (their footprint is massive which makes it hard to put them close to where people live)
    • availability of cheap & reliable (and, generally, clean) power

    If you, as a data center operator, can tap a new source of cheap & reliable power, you will go very far as you alleviate one of the main constraints on the ability to add to your footprint.

    It’s no small wonder, then, that Google is willing to explore partnerships with next-gen geothermal startups like Fervo in a meaningful long-term fashion.


  • Store all the things: clean electricity means thermal energy storage boom

    Thermal energy storage has been a difficult place for climatetech in years past. The low cost of fossil fuels (the source for vast majority of high temperature industrial heat to date) and the failure of large scale solar thermal power plants to compete with the rapidly scaling solar photovoltaic industry made thermal storage feel like, at best, a market reserved for niche applications with unique fossil fuel price dynamics. This is despite some incredibly cool (dad-joke intended 🔥🥵🤓) technological ingenuity in the space.

    But, in a classic case of how cheap universal inputs change market dynamics, the plummeting cost and soaring availability of renewable electricity and the growing desire for industrial companies to get “clean” sources of industrial heat has resulted in almost a renaissance for the space as this Canary Media article (with a very nice table of thermal energy startups) points out.

    With cheap renewables (especially if the price varies), companies can buy electricity at low (sometimes near-zero if in the middle of a sunny and windy day) prices, convert that to high-temperature heat with an electric furnace, and store it for use later.

    While the devil’s in the details, in particular the round trip energy efficiency (how much energy you can get out versus what you put in), the delivered heat temperature range and rate (how hot and how much power), and, of course, the cost of the system, technologies like this could represent a key technology to green sectors of the economy that would otherwise be extremely difficult to lower carbon output for.


  • InVision founder retro

    As reported in The Information a few days ago, former design tool giant InVision, once valued at $2 billion, is shutting down at the end of this year.

    While much of the commentary has been about Figma’s rapid rise and InVision’s inability to respond, I saw this post on Twitter/X from one of InVision’s founders Clark Valberg about what happened. The screenshotted message he left is well-worth a read. It is a great (if slightly self-serving / biased) retrospective.

    As someone who was a mere bystander during the events (as a newly minted Product Manager working with designers), it felt very true to the moment.

    I remember being blown away by how the entire product design community moved to Sketch (from largely Adobe-based solutions) and then, seemingly overnight, from Sketch to Figma.

    While it’s fair to criticize the leadership for not seeing web-based design as a place to invest, I think the piece just highlights how because it wasn’t a direct competitor to InDesign (but to Sketch & Adobe XD) and because the idea of web-based wasn’t on anyone’s radar at the time, it became a lethal blind spot for the company. It’s Tech Strategy 101 and perfectly highlights Andy Grove’s old saying: “(in technology,) only the paranoid survive”.


    Tweet from @ClarkValberg
    Clark Valberg | Twitter/X

  • The World Runs on Excel… and its Mistakes

    The 2022 CHIPS and Science Act earmarked hundreds of billions in subsidies and tax credits to bolster a U.S. domestic semiconductor (and especially semiconductor manufacturing) industry. If it works, it will dramatically reposition the U.S. in the global semiconductor value chain (especially relative to China).

    With such large amounts of taxpayer money practically “gifted” to large (already very profitable) corporations like Intel, the U.S. taxpayer can reasonably assume that these funds should be allocated carefully and thoughtfully and with processes in place to make sure every penny furthered the U.S.’s strategic goals.

    But, when the world’s financial decisions are powered by Excel spreadsheets, even the best laid plans can go awry.

    The team behind the startup Rowsie created a large language model (LLM)-powered tool which can understand Excel spreadsheets and answer questions posed to it. They downloaded a spreadsheet that the US government provided as an example of the information and calculations they want applicants fill out in order to qualify. They then applied their AI tool to the spreadsheet to understand it’s structure and formulas.

    Interestingly, Rowsie was able to find a single-cell spreadsheet error (see images below) which resulted in a $178 million understatement of interest payments!

    The Assumptions Processing tab in the Example Pre-App-Simple-Financial-Model spreadsheet from the CHIPS Act funding application website. Notice row 50. Despite the section being about Subordinated Debt (see Cell B50), they’re using cell C51 from the Control Panel tab (which points to the Senior Debt rate of 5%) rather than the correct cell of D51 (which points to the Subordinated Debt rate of 8%).

    To be clear, this is not a criticism of the spreadsheet’s architects. In this case, what seems to have happened, is that the spreadsheet creator copied an earlier row (row 40) and forgot to edit the formula to account for the fact that row 50 is about subordinated debt and row 40 is about senior debt. It’s a familiar story to anyone who’s ever been tasked with doing something complicated in Excel. Features like copy and paste and complex formulas are very powerful, but also make it very easy for a small mistake to cascade. It’s also remarkably hard to catch!

    Hopefully the Department of Commerce catches on and fixes this little clerical mishap, and that applicants are submitting good spreadsheets, free of errors. But, this case underscores how (1) so many of the world’s financial and policy decisions rest on Excel spreadsheets and you just have to hope 🤞🏻 no large mistakes were made, and (2) the potential for tools like Rowsie to be tireless proofreaders and assistants who can help us avoid mistakes and understand those critical spreadsheets quickly.

    If you’re interested in checking out Rowsie, check it out at https://www.rowsie.ai/!

    DISCLAIMER: I happen to be friends with the founders of Rowsie which is how I found out about this

  • Consulting / Advisory

    Hi! My name is Benjamin Tseng. My clients work with me because of my deep experience in:

    • Startup Advisory — I’ve spent 15+ years investing with two cross-border VC firms (DCM and 1955 Capital) in deeptech companies and in leadership / advisory roles at a number of VC-backed startups
    • Early Stage Product Management — I’ve taken on product leadership roles at several VC-backed startups including Yik Yak (consumer social), Maximus (telemedicine), Clint Health (health IT), and Stir (creator economy/fintech)
    • Market Analysis / Investment Due Diligence — I started my career at Bain doing strategic analysis for Fortune 500 semiconductor and eCommerce clients. I subsequently drove investment due diligence processes at two VC firms where I specialized in deeptech and health tech opportunities.
    • AI / ML work — I am a published researcher who’s applied AI/ML methods to electronic medical record data and have also built products powered by NLP and LLMs.

    For more about my background, take a look at my CV. For examples of projects I’ve done in the past and am open to taking on, see Types of Client Work below.

    If you’re interested in working with me in any of these capacities, please direct inquiries to [mail-at-<thisdomainname.com>].

    Types of Client Work

    • Expert Technology / Market Analysis
      • Conduct market analysis and unit economics assessment as part of a strategic planning or investment due diligence process
      • Objectively assess novel technologies and translate findings into business insights through dialogues with technical experts and business / finance stakeholders
      • Draw on 15+ years experience as an operator and investor in a wide range of areas including semiconductors, AI/ML, energy, industrials, biotechnology, diagnostics, and healthcare
    • Early Stage Product Management & Strategy
      • Combine market analysis, unit economics, customer-centered insight generation, and stakeholder input to produce actionable product roadmaps and specifications
      • Build product plans that account for operational & regulatory complexity (e.g. past experiences include integration with customer support/ops, integrations with Stripe/Plaid, handling KYC/AML, addressing HIPAA, complying with US telemedicine regulations, sensitivity to consumer privacy, etc)
      • Collaborate with engineers, designers, and other stakeholders on
        • Rapid prototyping for product discovery
        • 0-to-1 new product development and lightweight process creation
        • Product improvement pushes to address architecture/strategy issues and expand product reach
      • Design and implement metrics systems to enable product teams to understand how their product is being used and how the product is impacting other systems and processes
      • Draw on 5+ yrs experience as product leader in multiple VC-backed startups in consumer social, telemedicine, health IT, creator economy, and small business fintech
    • Startup Advisory & Strategic Planning
      • Work with management to build actionable strategic plans that account for current and expected business activities, future financing needs, likely execution risks, and the need for stakeholder support
      • Assist management teams with fundraising strategy from deck creation to cap table & financial modeling to syndicate formation and negotiation
      • Plan and execute on market & unit economic analyses that can meaningfully impact company strategy
      • Develop initial go-to-market strategies in new market segments and overhaul public web presence to align
      • Draw on 15+ years experience on startup executive teams, as a VC investor, and in advisory capacity
    • Analytics Setup and Metrics
      • Create or overhaul an existing metrics plan to help companies understand their product/business and devise better strategy
      • Collaborate with stakeholders to select analytics stacks and implement dashboards to realize a metrics plan
      • Execute on bespoke analyses (retention, lifetime value, segmentation, clustering, sentiment analysis, etc.) to answer key strategic and operational questions 
      • Draw on 5+ yrs experience as product leader in multiple VC-backed startups in consumer social, telemedicine, health IT, creator economy, and small business fintech
    • Data Science / Machine Learning / Artificial Intelligence
      • Leverage ML and deep learning/AI methods to tackle classification and prediction problems, especially those that involve textual data
      • Build LLM-powered applications leveraging both publicly available LLMs (i.e. OpenAI’s GPT4) and open source LLMs run on controlled infrastructure (i.e. Llama 2)
      • Review literature on particular methods to make a determination / proposal of how it can apply to a set of business problems
      • Scrape publicly available datasets / web pages for information to power data products or AI/ML models
      • Draw on my experience applying AI/ML methods to data from electronic medical records resulting in poster presentations at academic conferences and publications in leading medical informatics and ophthalmology journals
  • Psychedelics in the Clinic

    When I first heard about the use of psychedelics (like ketamine and psilocybin) for treatment of mental illness, I was skeptical. It just seemed too ripe for abuse.

    But, there is a growing body of credible academic work suggesting that psychedelics when dosed properly and used in conjunction with therapy / other drugs can be a gamechanger — especially for treatment-resistant depression and suicidality — and that is incredibly exciting.

    At the same time, as a former telemedicine startup operator, this makes me more alarmed by the numerous companies working to commercialize these. In the bid for venture-style growth, it’s all too easy to lose track of the “when dosed properly and used in conjunction with therapy / other drugs” part.

    In any event, this article from Medicine at Michigan is a good overview of the recent research highlights in the field and why so many clinicians and scientists are excited.


    Serious about psychedelics
    Katie Whitney | Medicine at Michigan

  • Finance

    Analysis

    Advice from a VC

  • It’s not just the GOP who misunderstands Section 230

    Source: NPR

    Section 230 of the Communications Decency Act has been rightfully called “the twenty-six words that created the Internet.” It is a valuable legal shield which allows internet hosts and platforms the ability to distribute user-generated content and practice moderation without unreasonable fear of being sued, something which forms the basis of all social media, user review, and user forum, and internet hosting services.

    In recent months, as big tech companies have drawn greater scrutiny for the role they play in shaping our discussions, Section 230 has become a scapegoat for many of the ills of technology. Until 2021, much of that criticism has come from the Republican Party who argue incorrectly that it promotes bias on platforms with President Trump even vetoing unrelated defense legislation because it did not repeal Section 230.

    So, it’s refreshing (and distressing) to see the Democrats now take their turn in misunderstanding what Section 230 does for the internet. This critique is based mainly on Senator Mark Warner’s proposed changes to Section 230 and the FAQ his office posted about the SAFE TECH act he (alongside Senators Hirono and Klobuchar) is proposing but apply to many commentators from the Democratic Party and the press which seems to have misunderstood the practical implications and have received this positively.

    While I think it’s reasonable to modify Section 230 to obligate platforms to help victims of clearly heinous acts like cyberstalking, swatting, violent threats, and human rights violations, what the Democratic Senators are proposing goes far beyond that in several dangerous ways.

    First, Warner and his colleagues have proposed carving out from Section 230 all content which accompanies payment (see below). While I sympathize with what I believe was the intention (to put a different bar on advertisements), this is remarkably short-sighted, because Section 230 applies to far more than companies with ad / content moderation policies Democrats dislike such as Facebook, Google, and Twitter.

    Source: Mark Warner’s “redlines” of Section 230; highlighting is mine

    It also encompasses email providers, web hosts, user generated review sites, and more. Any service that currently receives payment (for example: a paid blog hosting service, any eCommerce vendor who lets users post reviews, a premium forum, etc) could be made liable for any user posted content. This would make it legally and financially untenable to host any potentially controversial content.

    Secondly, these rules will disproportionately impact smaller companies and startups. This is because these smaller companies lack the resources that larger companies have to deal with the new legal burdens and moderation challenges that such a change to Section 230 would call for. It’s hard to know if Senator Warner’s glip answer in his FAQ that people don’t litigate small companies (see below) is ignorance or a willful desire to mislead, but ask tech startups how they feel about patent trolls and whether or not being small protects them from frivolous lawsuits

    Source: Mark Warner’s FAQ on SAFE TECH Act; highlighting mine

    Third, the use of the language “affirmative defense” and “injunctive relief” may have far-reaching consequences that go beyond minor changes in legalese (see below). By reducing Section 230 from an immunity to an affirmative defense, it means that companies hosting content will cease to be able to dismiss cases that clearly fall within Section 230 because they now have a “burden of [proof] by a preponderance of the evidence.”

    Source: Mark Warner’s “redlines” of Section 230; highlighting is mine

    Similarly, carving out “injunctive relief” from Section 230 protections (see below) means that Section 230 doesn’t apply if the party suing is only interested in taking something down (but not financial damages)

    Source: Mark Warner’s “redlines” of Section 230

    I suspect the intention of these clauses is to make it harder for large tech companies to dodge legitimate concerns, but what this practically means is that anyone who has the money to pursue legal action can simply tie up any internet company or platform hosting content that they don’t like.

    That may seem like hyperbole, but this is what happened in the UK until 2014 where libel / slander laws making it easy for wealthy individuals and corporations to sue anyone for negative press due to weak protections. Imagine Jeffrey Epstein being able to sue any platform for carrying posts or links to stories about his actions or any individual for forwarding an unflattering email about him.

    There is no doubt that we need new tools and incentives (both positive and negative) to tamp down on online harms like cyberbullying and cyberstalking, and that we need to come up with new and fair standards for dealing with “fake news”. But, it is distressing that elected officials will react by proposing far-reaching changes that show a lack of thoughtfulness as it pertains to how the internet works and the positives of existing rules and regulations.

    It is my hope that this was only an early draft that will go through many rounds of revisions with people with real technology policy and technology industry expertise.

  • CV

    Professional Experience

    Freelance (2023 – Present; remote): Product / Strategy work

    • Conduct high-impact topgrading interviews for VC-backed company to assess cultural fit and skills alignment
    • Led product management and software architecture for creator agency building internal tooling for metrics/reporting, top-of-funnel growth initiatives, an internal linkinbio tool, Chrome extension-based operational tooling, and AI-powered fan engagement tooling
    • Reviewed existing compensation scheme and financial/operational metrics to propose an alternative scheme with better incentive alignment at creator agency
    • Devised new commercial positioning and more-customer-centric webpage for VC-backed AI-powered Intelligent Document Processing startup previously focused mainly on technology
    • Wrote whitepaper about the application of reinforcement learning to the Pacific theater for VC-backed defensetech company for consumption within the Federal government
    • Identified non-defense markets and rough sizing for VC-backed defensetech startup seeking revenue diversification and means to scale up production faster (as proof point for government sales discussions)
    • Evaluated healthcare market as opportunity for VC-backed AI SaaS startup and prioritized specific segments to target vs. avoid given firm’s existing technology stack, opportunity size, and additional development needed

    Stir (2021 – 2023; San Francisco): Product & Data Science/AI Lead

    Stir was a venture-backed company focused on building technology to enable creatives to better collaborate which raised $20M from top Silicon Valley VCs such as Homebrew and a16z

    • Guided roadmap and implementation of product functionality (including key features such as Manager Access, mobile web, Invoices, international payments, early monetization, etc.) which helped grow transaction volume from <$2M/yr run rate to >$200M/yr at its peak
    • Strong experience working with Plaid and Stripe APIs to deliver customized financial service offerings
    • Built metrics and reporting infrastructure from the ground-up with a mix of SQL and business intelligence tooling for internal purposes and to support reporting needs of multiple enterprise customers
    • Served as data science/AI lead, building Playwright & BeautifulSoup-based scrapers, a prototype prompt engineering tool for image generation, serverless vector-based image search functionality, and a prototype chained LLM metasearch application

    Maximus (2020 – 2021; remote): VP Product and Strategy

    Maximus is a consumer health company that provides men with content, community, and clinical support to optimize them in mind and body. Maximus raised $15M from top Silicon Valley VCs such as Founders Fund and 8VC as well as leading angel investors/operators

    • Provided product and operational leadership culminating in release of MVP version of company’s first subscription product offering in Q2 2021
    • Oversaw team of engineers & designers supporting all software product and internal systems development within company
    • Developed user-friendly patient experience and compliant legal & software infrastructure needed for multi-state telemedicince operations in coordination with regulatory, finance, pharmacy partners, clinical labs partners, electronic medical record, clinicians, and other third parties
    • Built “operator manual” for launch operations that can be handed over to non-technical staff
    • Guided roadmap for further state and product launches, user experience enhancements, and infrastructure / operational improvements
    • Team member #1 with oversight over company’s fundraising close, as well as set up of initial legal, finance, and HR systems
    • Continuing to serve as an advisor

    1955 Capital (2016 – ; Bay Area): Venture Partner

    1955 Capital is a U.S.-based venture capital firm that invests in technologies from the developed world that can help solve the developing world’s greatest challenges in areas like energy, the environment, food safety, health, education, and manufacturing. 

    • Team member #1 with far-ranging responsibilities including sourcing, due diligence, fundraising, deal execution, team construction, and process creation
    • Generalist within the firm with emphasis across deep technologies (cleantech, semiconductors, hardware, and healthcare)
    • Worked directly with portfolio company management teams including Gridtential, Nature’s Fynd, Ampaire, Craif, and Vital Bio, and other still-unannounced investments in aviation, energy, diagnostics, and advanced materials
    • Created firm’s first special purpose vehicles for investments leveraging alternative capital formation as strategy

    Clint Health (2020; Bay Area): Product Consultant

    Clint, the clinical intelligence platform, automates the identification of a patient’s true clinical state by analyzing all medical data the same way a physician does. Clint understands what conditions a patient has, why they have it, how well they have been treated, and how they should be treated. Armed with these clinical grade insights, clinicians and researchers are able to identify and treat guideline directed care gaps, identify patients eligible for clinical trials and unite clinical care with clinical research.

    • Provided product management, design & engineering leadership, and strategic guidance for several of company’s product efforts
    • Overhauled prior dashboard product to improve usability and support insight needs for commercial / medical affairs customers within life sciences organizations
    • Developed care gap closure use case for payer / value-based provider customers and built framework for opportunity sizing to facilitate pitches
    • Built initial concept prototypes on clinical trial optimization / external control arm product

    Yik Yak (2014 – 2015; Atlanta): VP, Product & Business Development

    Yik Yak was a venture-backed startup building hyperlocal communities for mobile devices which has raised over $70M in venture funding since its inception.

    • Drove Series B fundraising process leading to a $60M+ financing
    • Built and managed team of 6 PMs, designers, and UX researchers supporting all product and systems development within company
    • Guided roadmap and implementation of over 1 year of releases (including key functionality like phone number verification, reply icons, notification center, My Herd, sharing, web)
    • Handled all preliminary partnership conversations and executed on partnerships with University of Florida, MTV, Comedy Central, and Bleacher Report

    DCM (2010 – 2014;  Bay Area): VP, Investments

    DCM is a leading early stage venture capital fund based in the Silicon Valley, Beijing, and Tokyo, managing over $3B in assets.

    • Generalist within firm with emphasis on opportunities in deep technologies (cleantech, semiconductors), connected platforms (MVNO, smart TV, OTT streaming), and new models for healthcare
    • Drove due diligence processes on and worked directly with management at portfolio companies on product strategy, fundraising, financial planning, business development and hiring including SoFi, Whistle, 1Mainstream, Analogix, Matterport, PlayStudios, Cognitive Networks, and Enovix
    • Built software tool to programmatically parse data from AppAnnie (mobile app store rankings data vendor)

    Bain & Company (2007 – 2010;  Bay Area): Senior Associate Consultant

    Bain & Company is a leading management consulting firm.

    • Performed competitive benchmarking to compare a client’s manufacturing operations with industry best-in-class
    • Analyzed technology industry profitability to identify attractive growth vectors for large tech client and highlight key differences between profit/power concentration in different verticals
    • Conducted financial and strategic diligence on wide range of potential acquisition targets (ranging in value from ~$100M to over $50B) to map out strategic acquisition options and gameboarding scenarios for a Fortune 500 technology client
    • Facilitated concept development and pilot phase of client initiative to provide operational support services for supply chain
    • Provided strategic and financial analysis to aid multiple clients in determining appropriate response to potentially disruptive trends; topics covered include cloud computing, mobile commerce, next-generation semiconductor manufacturing, cross-border eCommerce, social networking, etc.
    • Devised presentation for CEO-level conversation on a process to pro-actively acquire/partner with assets which can aid client in dealing with disruptive innovations
    • Overhauled Bain toolkit for codification in book by Bain partners Mark Gottfredson and Steve Schaubert, The Breakthrough Imperative

    Roche Pharmaceuticals (2005; Palo Alto, CA): Research Intern, Drug Metabolism & Pharmacokinetics

    Roche Pharmaceuticals is a leading pharmaceutical company

    • Validated use of Isothermal Titration Calorimeter in enzyme kinetics studies
    • Assessed factors limiting application of approach to study of Cytochrome P450 enzyme system
    • Presented findings at department seminar

    Abgenix Corporation (2003; Fremont, CA): Intern, Process Sciences

    Abgenix was a biotechnology company focused on humanized antibody therapeutics which was acquired by Amgen in 2006

    • Performed optimization studies for ELISA protocols used by Abgenix’s Process Sciences division
    • Presented findings at department seminar

    Academic Research

    Stanford Ophthalmic Informatics and Artificial Intelligence Group (2019 – ; Stanford, CA): Stanford University Byers Eye Institute

    Work with Professor Sophia Ying Wang applying AI/NLP methods to understanding and making predictions based on electronic medical record data. Example work includes:

    Maniatis Group (2004 – 2007; Cambridge, MA): Harvard University Department of Molecular and Cellular Biology

    Completed senior thesis “Transcriptional Regulation of Members of the Tripartite Motif Family” (linkreport PDF) in lab of Professor Tom Maniatis.

    Brenner Group (2006 – 2007; Cambridge, MA): Harvard University Division of Engineering and Applied Sciences

    Worked with Professor Michael P. Brenner on applying separation of timescales and dominant balances towards simplifying complex biological math models, specifically with regards to what sets the Ran gradient in nuclear transport (linkreport PDF).

    Brown Group (Summer 2004; Stanford, CA): Stanford Medical School Center for Clinical Science Research

    Worked in the lab of Professor Janice (Wes) M. Y. Brown researching stem cell reconstitution of murine immune systems in conjunction with antifungal agents and combinations of lymphoid and myeloid progenitor cells. Research summarized in: Arber C, et al. Journal of Infectious Diseases [2005]

    Education

    Harvard University (2003 – 2007; Cambridge, MA): A. B. Magna Cum Laude with Highest Honors in Biochemical Sciences, Secondary Field in Mathematical Sciences

    • Honors: Phi Beta Kappa, Dean’s Research Award (2006), Harvard College Research Program Award (2006), Harvard College Scholar (2005-2006), Tylenol Scholar (2005-2006)
    • Thesis: Transcriptional Regulation of Members of the Tripartite Motif Family in Response to Viral Infection (link)
    • Selected Activities
      • Harvard College Asian Business Forum: Operations Director (2005-2006)
      • Next Generation MD: Associate Editor (2005-2007), writer
      • Harvard International Review: Media Department Head (2004-2005), Visuals Editor(2003-2004)

    Skills/Other

  • About

    Benjamin Tseng is a Product Leader & AI technologist with over 8 years of experience as a deeptech/cross-border VC.

    Some of my past writing:

    About Me:

    Benjamin Tseng is a versatile executive with experience in product management and strategy; as a researcher applying deep learning methods to healthcare; and as a venture investor with focus on cleantech, semiconductors, hardware, and healthcare.

    Ben has significant experience working with startups in both operating and advisory capacities. His prior roles include product and operational leadership roles at Yik Yak (location-based social media, raised over $70M in VC), HealthPals (health IT), Maximus (consumer telemedicine, raised $20M in VC) and Stir (financial services for creators, raised $20M in VC). He has also served as an advisor to companies like Whistle (pet-tech, acquired by Mars), Dextro (computer vision, acquired by Axon), and Companion Labs (computer vision applied to pet-tech, raised $14M in VC).

    He is a published researcher collaborating with Stanford’s OPTIMA group in developing AI/NLP-based methods to understanding electronic medical records. He was also an Associate Fellow with the Tony Blair Institute for Global Change where he contributed to their work in Technology and Public Policy.

    Ben spent nearly a decade in venture capital at 1955 Capital and DCM, two cross-border venture capital firms, where he had a special focus on deeptech (e.g., cleantech, semiconductors, hardware, and healthcare) and worked actively with the two firms’ portfolio companies on fundraising and strategy.

    Ben started his career at Bain & Company in the Bay Area after graduating Phi Beta Kappa with Highest Honors in Biochemical Sciences and a minor in Mathematical Sciences from Harvard University. He is an avid scifi & comic book fan, an amateur programmer, occasional blogger, and an unabashed knowledge junkie. 

    He can be reached at [mail-at-<thisdomainname.com>]. He can also be reached @BenjaminTseng on Twitter

    (Photo credit: River Suh)

  • What I’ve Changed My Mind on Over the 2010s

    I’ve been reading a lot of year-end/decade-end reflections (as one does this time of year) — and while a part of me wanted to #humblebrag about how I got a 🏠/💍/👶🏻 this decade 😇 — I thought it would be more interesting & profound to instead call out 10 worldviews & beliefs I had going into the 2010s that I no longer hold.

    1. Sales is an unimportant skill relative to hard work / being smart
      As a stereotypical “good Asian kid” 🤓, I was taught to focus on nailing the task. I still think that focus is important early in one’s life & career, but this decade has made me realize that everyone, whether they know it or not, has to sell — you sell to employers to hire you, academics/nonprofits sell to attract donors and grant funding, even institutional investors have to sell to their investors/limited partners. Its a skill at least as important (if not more so).
    2. Marriage is about finding your soul-mate and living happily ever after
      Having been married for slightly over half the decade, I’ve now come to believe that marriage is less about finding the perfect soul-mate (the “Hollywood version”) as it is about finding a life partner who you can actively choose to celebrate (despite and including their flaws, mistakes, and baggage). Its not that passionate love is unimportant, but its hard to rely on that alone to make a lifelong partnership work. I now believe that really boring-sounding things like how you make #adulting decisions and compatibility of communication style matter a lot more than things usually celebrated in fiction like the wedding planning, first dates, how nice your vacations together are, whether you can finish each other’s sentences, etc.
    3. Industrial policy doesn’t work
      I tend to be a big skeptic of big government policy — both because of unintended consequences and the risks of politicians picking winners. But, a decade of studying (and working with companies who operate in) East Asian economies and watching how subsidies and economies of scale have made Asia the heart of much of advanced manufacturing have forced me to reconsider. Its not that the negatives don’t happen (there are many examples of China screwing things up with heavy-handed policy) but its hard to seriously think about how the world works without recognizing the role that industrial policy played. For more on how land management and industrial policies impacted economic development in different Asian countries, check out Joe Studwell’s book How Asia Works
    4. Obesity & weight loss are simple — its just calories in & calories out
      From a pure physics perspective, weight gain is a “simple” thermodynamic equation of “calories in minus calories out”. But in working with companies focused on dealing with prediabetes/obesity, I’ve come to appreciate that this “logic” not only ignores the economic and social factors that make obesity a public health problem, it also overlooks that different kinds of foods drive different physiological responses. As an example that just begins to scratch the surface, one very well-controlled study (sadly, a rarity in the field) published in July showed that, even after controlling for exercise and calories, carbs, fat, fiber, and other nutrients present in a meal, diets consisting of processed foods resulted in greater weight-gain than a diet consisting of unprocessed foods
    5. Revering luminaries & leaders is a good thing
      Its very natural to be so compelled by an idea / movement that you find yourself idolizing the people spearheading it. The media feeds into this with popular memoirs & biographies and numerous articles about how you can think/be/act more like [Steve Jobs/Jeff Bezos/Warren Buffett/Barack Obama/etc]. But, over the past decade, I’ve come to feel that this sort of reverence leads to a pernicious laziness of thought. I can admire Steve Jobs for his brilliance in product design but do I want to copy his approach to management or his use of alternative medicine to treat his cancer or condoning how he treated his illegitimate daughter. I think its far better to appreciate an idea and the work of the key people behind it than to equate the piece of work with the person and get sucked in to that cult of personality.
    6. Startups are great place for everyone
      Call it being sucked into the Silicon valley ethos but for a long time I believed that startups were a great place for everyone to build a career: high speed path to learning & responsibility, ability to network with other folks, favorable venture funding, one of the only paths to getting stock in rapidly growing companies, low job seeking risk (since there’s an expectation that startups often fail or pivot). Several years spent working in VC and startups later, and, while I still agree with my list above, I’ve come to believe that startups are really not a great place for most people. The risk-reward is generally not great for all but the earliest of employees and the most successful of companies, and the “startups are great for learning” Kool-aid is oftentimes used to justify poor management and work practices. I still think its a great place for some (i.e. people who can tolerate more risk [b/c of personal wealth or a spouse with a stable high-paying job], who are knowingly optimizing for learning & responsibility, or who are true believers in a startup’s mission), but I frankly think most people don’t fit the bill.
    7. Microaggressions are just people being overly sensitive
      I’ve been blessed at having only rarely faced overt racism (telling me to go back to China 🙄 / or that I don’t belong in this country). It’s a product of both where I’ve spent most of my life (in urban areas on the coasts) and my career/socioeconomic status (it’s not great to be overtly racist to a VC you’re trying to raise money from). But, having spent some dedicated time outside of those coastal areas this past decade and speaking with minorities who’ve lived there, I’ve become exposed to and more aware of “microaggressions”, forms of non-overt prejudice that are generally perpetrated without ill intent: questions like ‘so where are you really from?’ or comments like ‘you speak English really well!’. I once believed people complaining about these were simply being overly sensitive, but I’ve since become an active convert to the idea that, while these are certainly nowhere near as awful as overt hate crimes / racism, they are their own form of systematic prejudice which can, over time, grate and eat away at your sense of self-worth.
    8. The Western model (liberal democracy, free markets, global institutions) will reign unchallenged as a model for prosperity
      I once believed that the Western model of (relatively) liberal democracy, (relatively) free markets, and US/Europe-led global institutions was the only model of prosperity that would reign falling the collapse of the Soviet Union. While I probably wouldn’t have gone as far as Fukuyama did in proclaiming “the end of history”, I believed that the world was going to see authoritarian regimes increasingly globalize and embrace Western institutions. What I did not expect was the simultaneous rise of different models of success by countries like China and Saudi Arabia (who, frighteningly, now serve as models for still other countries to embrace), as well as a lasting backlash within the Western countries themselves (i.e. the rise of Trump, Brexit, “anti-globalism”, etc). This has fractured traditional political divides (hence the soul-searching that both major parties are undergoing in the US and the UK) and the election of illiberal populists in places like Mexico, Brazil, and Europe.
    9. Strategy trumps execution
      As a cerebral guy who spent the first years of his career in the last part of the 2000s as a strategy consultant, it shouldn’t be a surprise that much of my focus was on formulating smart business strategy. But having spent much of this decade focused on startups as well as having seen large companies like Apple, Amazon, and Netflix brilliantly out-execute companies with better ‘strategic positioning’ (Nokia, Blackberry, Walmart, big media), I’ve come around to a different understanding of how the two balance each other.
    10. We need to invent radically new solutions to solve the climate crisis
      Its going to be hard to do this one justice in this limited space — especially since I net out here very differently from Bill Gates — but going into this decade, I never would have expected that the cost of new solar or wind energy facilities could be cheaper than the cost of operating an existing coal plant. I never thought that lithium batteries or LEDs would get as cheap or as good as they are today (with signs that this progress will continue) or that the hottest IPO of the year would be an alternative food technology company (Beyond Meat) which will play a key role in helping us mitigate food/animal-related emissions. Despite the challenges of being a cleantech investor for much of the decade, its been a surprising bright spot to see how much pure smart capital and market forces have pushed many of the technologies we need. I still think we will need new policies and a huge amount of political willpower — I’d also like to see more progress made on long-duration energy storage, carbon capture, and industrial — but whereas I once believed that we’d need radically new energy technologies to thwart the worst of climate change, I am now much more of an optimist here than I was when the decade started.

    Here’s to more worldview shifts in the coming decade!

  • How to Regulate Big Tech

    There’s been a fair amount of talk lately about proactively regulating — and maybe even breaking up — the “Big Tech” companies.

    Full disclosure: this post discusses regulating large tech companies. I own shares in several of these both directly (in the case of Facebook and Microsoft) and indirectly (through ETFs that own stakes in large companies)

    Source: MIT Sloan

    Like many, I have become increasingly uneasy over the fact that a small handful of companies, with few credible competitors, have amassed so much power over our personal data and what information we see. As a startup investor and former product executive at a social media startup, I can especially sympathize with concerns that these large tech companies have created an unfair playing field for smaller companies.

    At the same time, though, I’m mindful of all the benefits that the tech industry — including the “tech giants” — have brought: amazing products and services, broader and cheaper access to markets and information, and a tremendous wave of job and wealth creation vital to may local economies. For that reason, despite my concerns of “big tech”‘s growing power, I am wary of reaching for “quick fixes” that might change that.

    As a result, I’ve been disappointed that much of the discussion has centered on knee-jerk proposals like imposing blanket stringent privacy regulations and forcefully breaking up large tech companies. These are policies which I fear are not only self-defeating but will potentially put into jeopardy the benefits of having a flourishing tech industry.

    The Challenges with Regulating Tech

    Technology is hard to regulate. The ability of software developers to collaborate and build on each other’s innovations means the tech industry moves far faster than standard regulatory / legislative cycles. As a result, many of the key laws on the books today that apply to tech date back decades — before Facebook or the iPhone even existed, making it important to remember that even well-intentioned laws and regulations governing tech can cement in place rules which don’t keep up when the companies and the social & technological forces involved change.

    Another factor which complicates tech policy is that the traditional “big is bad” mentality ignores the benefits to having large platforms. While Amazon’s growth has hurt many brick & mortar retailers and eCommerce competitors, its extensive reach and infrastructure enabled businesses like Anker and Instant Pot to get to market in a way which would’ve been virtually impossible before. While the dominance of Google’s Android platform in smartphones raised concerns from European regulators, its hard to argue that the companies which built millions of mobile apps and tens of thousands of different types of devices running on Android would have found it much more difficult to build their businesses without such a unified software platform. Policy aimed at “Big Tech” should be wary of dismantling the platforms that so many current and future businesses rely on.

    Its also important to remember that poorly crafted regulation in tech can be self-defeating. The most effective way to deal with the excesses of “Big Tech”, historically, has been creating opportunities for new market entrants. After all, many tech companies previously thought to be dominant (like Nokia, IBM, and Microsoft) lost their positions, not because of regulation or antitrust, but because new technology paradigms (i.e. smartphones, cloud), business models (i.e. subscription software, ad-sponsored), and market entrants (i.e. Google, Amazon) had the opportunity to flourish. Because rules (i.e. Article 13/GDPR) aimed at big tech companies generally fall hardest on small companies (who are least able to afford the infrastructure / people to manage it), its important to keep in mind how solutions for “Big Tech” problems affect smaller companies and new concepts as well.

    Framework for Regulating “Big Tech”

    If only it were so easy… Source: XKCD

    To be 100% clear, I’m not saying that the tech industry and big platforms should be given a pass on rules and regulation. If anything, I believe that laws and regulation play a vital role in creating flourishing markets.

    But, instead of treating “Big Tech” as just a problem to kill, I think we’d be better served by laws / regulations that recognize the limits of regulation on tech and, instead, focus on making sure emerging companies / technologies can compete with the tech giants on a level playing field. To that end, I hope to see more ideas that embrace the following four pillars:

    I. Tiering regulation based on size of the company

    Regulations on tech companies should be tiered based on size with the most stringent rules falling on the largest companies. Size should include traditional metrics like revenue but also, in this age of marketplace platforms and freemium/ad-sponsored business models, account for the number of users (i.e. Monthly Active Users) and third party partners.

    In this way, the companies with the greatest potential for harm and the greatest ability to bear the costs face the brunt of regulation, leaving smaller companies & startups with greater flexibility to innovate and iterate.

    II. Championing data portability

    One of the reasons it’s so difficult for competitors to challenge the tech giants is the user lock-in that comes from their massive data advantage. After all, how does a rival social network compete when a user’s photos and contacts are locked away inside Facebook?

    While Facebook (and, to their credit, some of the other tech giants) does offer ways to export user data and to delete user data from their systems, these tend to be unwieldy, manual processes that make it difficult for a user to bring their data to a competing service. Requiring the largest tech platforms to make this functionality easier to use (i.e., letting others import your contact list and photos with the ease in which you can login to many apps today using Facebook) would give users the ability to hold tech companies accountable for bad behavior or not innovating (by being able to walk away) and fosters competition by letting new companies compete not on data lock-in but on features and business model.

    III. Preventing platforms from playing unfairly

    3rd party platform participants (i.e., websites listed on Google, Android/iOS apps like Spotify, sellers on Amazon) are understandably nervous when the platform owners compete with their own offerings (i.e., Google Places, Apple Music, Amazon first party sales)As a result, some have even called for banning platform owners from offering their own products and services.

    I believe that is an overreaction. Platform owners offering attractive products and services (i.e., Google offering turn-by-turn navigation on Android phones) can be a great thing for users (after all, most prominent platforms started by providing compelling first-party offerings) and for 3rd party participants if these offerings improve the attractiveness of the platform overall.

    What is hard to justify is when platform owners stack the deck in their favor using anti-competitive moves such as banning or reducing the visibility of competitors, crippling third party offeringsmaking excessive demands on 3rd parties, etc. Its these sorts of actions by the largest tech platforms that pose a risk to consumer choice and competition and should face regulatory scrutiny. Not just the fact that a large platform exists or that the platform owner chooses to participate in it.

    IV. Modernizing how anti-trust thinks about defensive acquisitions

    The rise of the tech giants has led to many calls to unwind some of the pivotal mergers and acquisitions in the space. As much as I believe that anti-trust regulators made the wrong calls on some of these transactions, I am not convinced, beyond just wanting to punish “Big Tech” for being big, that the Pandora’s Box of legal and financial issues (for the participants, employees, users, and for the tech industry more broadly) that would be opened would be worthwhile relative to pursuing other paths to regulate bad behavior directly.

    That being said, its become clear that anti-trust needs to move beyond narrow revenue share and pricing-based definitions of anti-competitiveness (which do not always apply to freemium/ad-sponsored business models). Anti-trust prosecutors and regulators need to become much more thoughtful and assertive around how some acquisitions are done simply to avoid competition (i.e., Google’s acquisition of Waze and Facebook’s acquisition of WhatsApp are two examples of landmark acquisitions which probably should have been evaluated more closely).

    Wrap-Up

    Source: OECD Forum Network

    This is hardly a complete set of rules and policies needed to approach growing concerns about “Big Tech”. Even within this framework, there are many details (i.e., who the specific regulators are, what specific auditing powers they have, the details of their mandate, the specific thresholds and number of tiers to be set, whether pre-installing an app counts as unfair, etc.) that need to be defined which could make or break the effort. But, I believe this is a good set of principles that balances both the need to foster a tech industry that will continue to grow and drive innovation as well as the need to respond to growing concerns about “Big Tech”.

    Special thanks to Derek Yang and Anthony Phan for reading earlier versions and giving me helpful feedback!

  • Why Tech Success Doesn’t Translate to Deeptech

    Source: Eric Hamilton

    Having been lucky enough to invest in both tech (cloud, mobile, software) and “deeptech” (materials, cleantech, energy, life science) startups (and having also ran product at a mobile app startup), it has been striking to see how fundamentally different the paradigms that drive success in each are.

    Whether knowingly or not, most successful tech startups over the last decade have followed a basic playbook:

    1. Take advantage of rising smartphone penetration and improvements in cloud technology to build digital products that solve challenges in big markets pertaining to access (e.g., to suppliers, to customers, to friends, to content, to information, etc.)
    2. Build a solid team of engineers, designers, growth, sales, marketing, and product people to execute on lean software development and growth methodologies
    3. Hire the right executives to carry out the right mix of tried-and-true as well as “out of the box” channel and business development strategies to scale bigger and faster

    This playbook appears deceptively simple but is very difficult to execute well. It works because for markets where “software is eating the world”:

    Source: Techcrunch
    • There is relatively little technology risk: With the exception of some of the most challenging AI, infrastructure, and security challenges, most tech startups are primarily dealing with engineering and product execution challenges — what is the right thing to build and how do I build it on time, under budget? — rather than fundamental technology discovery and feasibility challenges
    • Skills & knowledge are broadly transferable: Modern software development and growth methodologies work across a wide range of tech products and markets. This means that effective engineers, salespeople, marketers, product people, designers, etc. at one company will generally be effective at another. As a result, its a lot easier for investors/executives to both gauge the caliber of a team (by looking at their experience) and augment a team when problems arise (by recruiting the right people with the right backgrounds).
    • Distribution is cheap and fast: Cloud/mobile technology means that a new product/update is a server upgrade/browser refresh/app store download away. This has three important effects:
    1. The first is that startups can launch with incomplete or buggy solutions because they can readily provide hotfixes and upgrades.
    2. The second is that startups can quickly release new product features and designs to respond to new information and changing market conditions.
    3. The third is that adoption is relatively straightforward. While there may be some integration and qualification challenges, in general, the product is accessible via a quick download/browser refresh, and the core challenge is in getting enough people to use a product in the right way.

    In contrast, if you look at deeptech companies, a very different set of rules apply:

    Source: XKCD
    • Technology risk/uncertainty is inherent: One of the defining hallmarks of a deeptech company is dealing with uncertainty from constraints imposed by reality (i.e. the laws of physics, the underlying biology, the limits of current technology, etc.). As a result, deeptech startups regularly face feasibility challenges — what is even possible to build? — and uncertainty around the R&D cycles to get to a good outcome — how long will it take / how much will it cost to figure this all out?
    • Skills & knowledge are not easily transferable: Because the technical and business talent needed in deeptech is usually specific to the field, talent and skills are not necessarily transferable from sector to sector or even company to company. The result is that it is much harder for investors/executives to evaluate team caliber (whether on technical merits or judging past experience) or to simply put the right people into place if there are problems that come up.
    • Product iteration is slow and costly: The tech startup ethos of “move fast and break things” is just harder to do with deeptech.
    1. At the most basic level, it just costs a lot more and takes a lot more time to iterate on a physical product than a software one. It’s not just that physical products require physical materials and processing, but the availability of low cost technology platforms like Amazon Web Services and open source software dramatically lower the amount of time / cash needed to make something testable in tech than in deeptech.
    2. Furthermore, because deeptech innovations tend to have real-world physical impacts (to health, to safety, to a supply chain/manufacturing line, etc.), deeptech companies generally face far more regulatory and commercial scrutiny. These groups are generally less forgiving of incomplete/buggy offerings and their assessments can lengthen development cycles. Deeptech companies generally can’t take the “ask for forgiveness later” approaches that some tech companies (i.e. Uber and AirBnb) have been able to get away with (exhibit 1: Theranos).

    As a result, while there is no single playbook that works across all deeptech categories, the most successful deeptech startups tend to embody a few basic principles:

    1. Go after markets where there is a very clear, unmet need: The best deeptech entrepreneurs tend to take very few chances with market risk and only pursue challenges where a very well-defined unmet need (i.e., there are no treatments for Alzheimer’s, this industry needs a battery that can last at least 1000 cycles, etc) blocks a significant market opportunity. This reduces the risk that a (likely long and costly) development effort achieves technical/scientific success without also achieving business success. This is in contrast with tech where creating or iterating on poorly defined markets (i.e., Uber and Airbnb) is oftentimes at the heart of what makes a company successful.
    2. Focus on “one miracle” problems: Its tempting to fantasize about what could happen if you could completely re-write every aspect of an industry or problem but the best deeptech startups focus on innovating where they won’t need the rest of the world to change dramatically in order to have an impact (e.g., compatible with existing channels, business models, standard interfaces, manufacturing equipment, etc). Its challenging enough to advance the state of the art of technology — why make it even harder?
    3. Pursue technologies that can significantly over-deliver on what the market needs: Because of the risks involved with developing advanced technologies, the best deeptech entrepreneurs work in technologies where even a partial success can clear the bar for what is needed to go to market. At the minimum, this reduces the risk of failure. But, hopefully, it gives the company the chance to fundamentally transform the market it plays in by being 10x better than the alternatives. This is in contrast to many tech markets where market success often comes less from technical performance and more from identifying the right growth channels and product features to serve market needs (i.e., Facebook, Twitter, and Snapchat vs. MySpace, Orkut, and Friendster; Amazon vs. brick & mortar bookstores and electronics stores)

    All of this isn’t to say that there aren’t similarities between successful startups in both categories — strong vision, thoughtful leadership, and success-oriented cultures are just some examples of common traits in both. Nor is it to denigrate one versus the other. But, practically speaking, investing or operating successfully in both requires very different guiding principles and speaks to the heart of why its relatively rare to see individuals and organizations who can cross over to do both.

    Special thanks to Sophia Wang, Ryan Gilliam, and Kevin Lin Lee for reading an earlier draft and making this better!

    Thought this was interesting? Check out some of my other pieces on Tech industry

  • The Four Types of M&A

    I’m oftentimes asked what determines the prices that companies get bought for: after all, why does one app company get bought for $19 billion and a similar app get bought at a discount to the amount of investor capital that was raised?

    While specific transaction values depend a lot on the specific acquirer (i.e. how much cash on hand they have, how big they are, etc.), I’m going to share a framework that has been very helpful to me in thinking about acquisition valuations and how startups can position themselves to get more attractive offers. The key is understanding that, all things being equal, why you’re being acquired determines the buyer’s willingness to pay. These motivations fall on a spectrum dividing acquisitions into four types:

    • Talent Acquisitions: These are commonly referred to in the tech press as “acquihires”. In these acquisitions, the buyer has determined that it makes more sense to buy a team than to spend the money, time, and effort needed to recruit a comparable one. In these acquisitions, the size and caliber of the team determine the purchase price.
    • Asset / Capability Acquisitions: In these acquisitions, the buyer is in need of a particular asset or capability of the target: it could be a portfolio of patents, a particular customer relationship, a particular facility, or even a particular product or technology that helps complete the buyer’s product portfolio. In these acquisitions, the uniqueness and potential business value of the assets determine the purchase price.
    • Business Acquisitions: These are acquisitions where the buyer values the target for the success of its business and for the possible synergies that could come about from merging the two. In these acquisitions, the financials of the target (revenues, profitability, growth rate) as well as the benefits that the investment bankers and buyer’s corporate development teams estimate from combining the two businesses (cost savings, ability to easily cross-sell, new business won because of a more complete offering, etc) determine the purchase price.
    • Strategic Gamechangers: These are acquisitions where the buyer believes the target gives them an ability to transform their business and is also a critical threat if acquired by a competitor. These tend to be acquisitions which are priced by the buyer’s full ability to pay as they represent bets on a future.

    What’s useful about this framework is that it gives guidance to companies who are contemplating acquisitions as exit opportunities:

    • If your company is being considered for a talent acquisition, then it is your job to convince the acquirer that you have built assets and capabilities above and beyond what your team alone is worth. Emphasize patents, communities, developer ecosystems, corporate relationships, how your product fills a distinct gap in their product portfolio, a sexy domain name, anything that might be valuable beyond just the team that has attracted their interest.
    • If a company is being considered for an asset / capability acquisition, then the key is to emphasize the potential financial trajectory of the business and the synergies that can be realized after a merger. Emphasize how current revenues and contracts will grow and develop, how a combined sales and marketing effort will be more effective than the sum of the parts, and how the current businesses are complementary in a real way that impacts the bottom line, and not just as an interesting “thing” to buy.
    • If a company is being evaluated as a business acquisition, then the key is to emphasize how pivotal a role it can play in defining the future of the acquirer in a way that goes beyond just what the numbers say about the business. This is what drives valuations like GM’s acquisition of Cruise (which was a leader in driverless vehicle technology) for up to $1B, or Facebook’s acquisition of WhatsApp (messenger app with over 600 million users when it was acquired, many in strategic regions for Facebook) for $19B, or Walmart’s acquisition of Jet.com (an innovator in eCommerce that Walmart needs to help in its war for retail marketshare with Amazon.com).

    The framework works for two reasons: (1) companies are bought, not sold, and the price is usually determined by the party that is most willing to walk away from a deal (that’s usually the buyer) and (2) it generally reflects how most startups tend to create value over time: they start by hiring a great team, who proceed to build compelling capabilities / assets, which materialize as interesting businesses, which can represent the future direction of an industry.

    Hopefully, this framework helps any tech industry onlooker wondering why acquisition valuations end up at a certain level or any startup evaluating how best to court an acquisition offer.

    Thought this was interesting? Check out some of my other pieces on how VC works / thinks

  • Dr. Machine Learning

    How to realize the promise of applying machine learning to healthcare

    Not going to happen anytime soon, sadly: the Doctor from Star Trek: Voyager; Source: TrekCore

    Despite the hype, it’ll likely be quite some time before human physicians will be replaced with machines (sorry, Star Trek: Voyager fans).

    While “smart” technology like IBM’s Watson and Alphabet’s AlphaGo can solve incredibly complex problems, they are probably not quite ready to handle the messiness of qualitative unstructured information from patients and caretakers (“it kind of hurts sometimes”) that sometimes lie (“I swear I’m still a virgin!”) or withhold information (“what does me smoking pot have to do with this?”) or have their own agendas and concerns (“I just need some painkillers and this will all go away”).

    Instead, machine learning startups and entrepreneurs interested in medicine should focus on areas where they can augment the efforts of physicians rather than replace them.

    One great example of this is in diagnostic interpretation. Today, doctors manually process countless X-rays, pathology slides, drug adherence records, and other feeds of data (EKGs, blood chemistries, etc) to find clues as to what ails their patients. What gets me excited is that these tasks are exactly the type of well-defined “pattern recognition” problems that are tractable for an AI / machine learning approach.

    If done right, software can not only handle basic diagnostic tasks, but to dramatically improve accuracy and speed. This would let healthcare systems see more patients, make more money, improve the quality of care, and let medical professionals focus on managing other messier data and on treating patients.

    As an investor, I’m very excited about the new businesses that can be built here and put together the following “wish list” of what companies setting out to apply machine learning to healthcare should strive for:

    • Excellent training data and data pipeline: Having access to large, well-annotated datasets today and the infrastructure and processes in place to build and annotate larger datasets tomorrow is probably the main defining . While its tempting for startups to cut corners here, that would be short-sighted as the long-term success of any machine learning company ultimately depends on this being a core competency.
    • Low (ideally zero) clinical tradeoffs: Medical professionals tend to be very skeptical of new technologies. While its possible to have great product-market fit with a technology being much better on just one dimension, in practice, to get over the innate skepticism of the field, the best companies will be able to show great data that makes few clinical compromises (if any). For a diagnostic company, that means having better sensitivty and selectivity at the same stage in disease progression (ideally prospectively and not just retrospectively).
    • Not a pure black box: AI-based approaches too often work like a black box: you have no idea why it gave a certain answer. While this is perfectly acceptable when it comes to recommending a book to buy or a video to watch, it is less so in medicine where expensive, potentially life-altering decisions are being made. The best companies will figure out how to make aspects of their algorithms more transparent to practitioners, calling out, for example, the critical features or data points that led the algorithm to make its call. This will let physicians build confidence in their ability to weigh the algorithm against other messier factors and diagnostic explanations.
    • Solve a burning need for the market as it is today: Companies don’t earn the right to change or disrupt anything until they’ve established a foothold into an existing market. This can be extremely frustrating, especially in medicine given how conservative the field is and the drive in many entrepreneurs to shake up a healthcare system that has many flaws. But, the practical reality is that all the participants in the system (payers, physicians, administrators, etc) are too busy with their own issues (i.e. patient care, finding a way to get everything paid for) to just embrace a new technology, no matter how awesome it is. To succeed, machine diagnostic technologies should start, not by upending everything with a radical solution, but by solving a clear pain point (that hopefully has a lot of big dollar signs attached to it!) for a clear customer in mind.

    Its reasons like this that I eagerly follow the development of companies with initiatives in applying machine learning to healthcare like Google’s DeepMind, Zebra Medical, and many more.

  • Laszlo Bock on Building Google’s Culture

    Much has been written about what makes Google work so well: their ridiculously profitable advertising business model, the technology behind their search engine and data centers, and the amazing pay and perks they offer.

    Source: the book

    My experiences investing in and working with startups, however, has taught me that building a great company is usually less about a specific technical or business model innovation than about building a culture of continuous improvement and innovation. To try to get some insight into how Google does things, I picked up Google SVP of People Operations Laszlo Bock’s book Work Rules!

    Bock describes a Google culture rooted in principles that came from founders Larry Page and Sergey Brin when they started the company: get the best people to work for you, make them want to stay and contribute, and remove barriers to their creativity. What’s great (to those interested in company building) is that Bock goes on to detail the practices Google has put in place to try to live up to these principles even as their headcount has expanded.

    The core of Google’s culture boils down to four basic principles and much of the book is focused on how companies should act if they want to live up to them:

    1. Presume trust: Many of Google’s cultural norms stem from a view that people are well-intentioned and trustworthy. While that may not seem so radical, this manifested at Google as a level of transparency with employees and a bias to say yes to employee suggestions that most companies are uncomfortable with. It raises interesting questions about why companies that say their talent is the most important thing treat them in ways that suggest a lack of trust.
    2. Recruit the best: Many an exec pays lip service to this, but what Google has done is institute policies that run counter to standard recruiting practices to try to actually achieve this at scale: templatized interviews / forms (to make the review process more objective and standardized), hiring decisions made by cross-org committees (to insure a consistently high bar is set), and heavy use of data to track the effectiveness of different interviewers and interview tactics. While there’s room to disagree if these are the best policies (I can imagine hating this as a hiring manager trying to staff up a team quickly), what I admired is that they set a goal (to hire the best at scale) and have actually thought through the recruiting practices they need to do so.
    3. Pay fairly [means pay unequally]: While many executives would agree with the notion that superstar employees can be 2-10x more productive, few companies actually compensate their superstars 2-10x more. While its unclear to me how effective Google is at rewarding superstars, the fact that they’ve tried to align their pay policies with their beliefs on how people perform is another great example of deviating from the norm (this time in terms of compensation) to follow through on their desire to pay fairly.
    4. Be data-driven: Another “in vogue” platitude amongst executives, but one that very few companies live up to, is around being data-driven. In reading Bock’s book, I was constantly drawing parallels between the experimentation, data collection, and analyses his People Operations team carried out and the types of experiments, data collection, and analyses you would expect a consumer internet/mobile company to do with their users. Case in point: Bock’s team experimented with different performance review approaches and even cafeteria food offerings in the same way you would expect Facebook to experiment with different news feed algorithms and notification strategies. It underscores the principle that, if you’re truly data-driven, you don’t just selectively apply it to how you conduct business, you apply it everywhere.

    Of course, not every company is Google, and not every company should have the same set of guiding principles or will come to same conclusions. Some of the processes that Google practices are impractical (i.e., experimentation is harder to set up / draw conclusions from with much smaller companies, not all professions have such wide variations in output as to drive such wide variations in pay, etc).

    What Bock’s book highlights, though, is that companies should be thoughtful about what sort of cultural principles they want to follow and what policies and actions that translates into if they truly believe them. I’d highly recommend the book!

  • What Happens After the Tech Bubble Pops

    In recent years, it’s been the opposite of controversial to say that the tech industry is in a bubble. The terrible recent stock market performance of once high-flying startups across virtually every industry (see table below) and the turmoil in the stock market stemming from low oil prices and concerns about the economies of countries like China and Brazil have raised fears that the bubble is beginning to pop.

    While history will judge when this bubble “officially” bursts, the purpose of this post is to try to make some predictions about what will happen during/after this “correction” and pull together some advice for people in / wanting to get into the tech industry. Starting with the immediate consequences, one can reasonably expect that:

    • Exit pipeline will dry up: When startup valuations are higher than what the company could reasonably get in the stock market, management teams (who need to keep their investors and employees happy) become less willing to go public. And, if public markets are less excited about startups, the price acquirers need to pay to convince a management team to sell goes down. The result is fewer exits and less cash back to investors and employees for the exits that do happen.
    • VCs become less willing to invest: VCs invest in startups on the promise that future IPOs and acquisitions will make them even more money. When the exit pipeline dries up, VCs get cold feet because the ability to get a nice exit seems to fade away. The result is that VCs become a lot more price-sensitive when it comes to investing in later stage companies (where the dried up exit pipeline hurts the most).
    • Later stage companies start cutting costs: Companies in an environment where they can’t sell themselves or easily raise money have no choice but to cut costs. Since the vast majority of later-stage startups run at a loss to increase growth, they will find themselves in the uncomfortable position of slowing down hiring and potentially laying employees off, cutting back on perks, and focusing a lot more on getting their financials in order.

    The result of all of this will be interesting for folks used to a tech industry (and a Bay Area) flush with cash and boundlessly optimistic:

    1. Job hopping should slow: “Easy money” to help companies figure out what works or to get an “acquihire” as a soft landing will be harder to get in a challenged financing and exit environment. The result is that the rapid job hopping endemic in the tech industry should slow as potential founders find it harder to raise money for their ideas and as it becomes harder for new startups to get the capital they need to pay top dollar.
    2. Strong companies are here to stay: While there is broad agreement that there are too many startups with higher valuations than reasonable, what’s also become clear is there are a number of mature tech companies that are doing exceptionally well (i.e. Facebook, Amazon, Netflix, and Google) and a number of “hotshots” which have demonstrated enough growth and strong enough unit economics and market position to survive a challenged environment (i.e. Uber, Airbnb). This will let them continue to hire and invest in ways that weaker peers will be unable to match.
    3. Tech “luxury money” will slow but not disappear: Anyone who lives in the Bay Area has a story of the ridiculousness of “tech money” (sky-high rents, gourmet toast,“its like Uber but for X”, etc). This has been fueled by cash from the startup world as well as free flowing VC money subsidizing many of these new services . However, in a world where companies need to cut costs, where exits are harder to come by, and where VCs are less willing to subsidize random on-demand services, a lot of this will diminish. That some of these services are fundamentally better than what came before (i.e. Uber) and that stronger companies will continue to pay top dollar for top talent will prevent all of this from collapsing (and lets not forget San Francisco’s irrational housing supply policies). As a result, people expecting a reversal of gentrification and the excesses of tech wealth will likely be disappointed, but its reasonable to expect a dramatic rationalization of the price and quantity of many “luxuries” that Bay Area inhabitants have become accustomed to soon.

    So, what to do if you’re in / trying to get in to / wanting to invest in the tech industry?

    • Understand the business before you get in: Its a shame that market sentiment drives fundraising and exits, because good financial performance is generally a pretty good indicator of the long-term prospects of a business. In an environment where its harder to exit and raise cash, its absolutely critical to make sure there is a solid business footing so the company can keep going or raise money / exit on good terms.
    • Be concerned about companies which have a lot of startup exposure: Even if a company has solid financial performance, if much of that comes from selling to startups (especially services around accounting, recruiting, or sales), then they’re dependent on VCs opening up their own wallets to make money.
    • Have a much higher bar for large, later-stage companies: The companies that will feel the most “pain” the earliest will be those with with high valuations and high costs. Raising money at unicorn valuations can make a sexy press release but it doesn’t amount to anything if you can’t exit or raise money at an even higher valuation.
    • Rationalize exposure to “luxury”: Don’t expect that “Uber but for X” service that you love to stick around (at least not at current prices)…
    • Early stage companies can still be attractive: Companies that are several years from an exit & raising large amounts of cash will be insulated in the near-term from the pain in the later stage, especially if they are committed to staying frugal and building a disruptive business. Since they are already relatively low in valuation and since investors know they are discounting off a valuation in the future (potentially after any current market softness), the downward pressures on valuation are potentially lighter as well.

    Thought this was interesting or helpful? Check out some of my other pieces on investing / finance.

  • 3D Printing as Disruptive Innovation

    Last week, I attended a MIT/Stanford VLAB event on 3D printing technologies. While I had previously been aware of 3D printing (which works basically the way it sounds) as a way of helping companies and startups do quick prototypes or letting geeks of the “maker” persuasion make random knickknacks, it was at the event that I started to recognize the technology’s disruptive potential in manufacturing. While the conference itself was actually more about personal use for 3D printing, when I thought about the applications in the industrial/business world, it was literally like seeing the first part/introduction of a new chapter or case study from Clayton Christensen, author of The Innovator’s Dilemma (and inspiration for one of the more popular blog posts here :-)) play out right in front of me:

    • Like many other disruptive innovations when they began, 3D printing today is unable to serve the broader manufacturing “market”. Generally speaking, the time needed per unit output, the poor “print resolution”, the upfront capital costs, and some of the limitations in terms of materials are among the reasons that the technology as it stands today is uncompetitive with traditional mass manufacturing.
    • Even if 3D printing were competitive today, there are big internal and external stumbling blocks which would probably make it very difficult for existing large companies to embrace it. Today’s heavyweight manufacturers are organized and incentivized internally along the lines of traditional assembly line manufacturing. They also lack the partners, channels, and supply chain relationships (among others) externally that they would need to succeed.
    • While 3D printing today is very disadvantaged relative to traditional manufacturing technologies (most notably in speed and upfront cost), it is extremely good at certain things which make it a phenomenal technology for certain use cases:
      • Rapid design to production: Unlike traditional manufacturing techniques which take significant initial tooling and setup, once you have a 3D printer and an idea, all you need to do is print the darn thing! At the conference, one of the panelists gave a great example: a designer bought an Apple iPad on a Friday, decided he wanted to make his own iPad case, and despite not getting any help from Apple or prior knowledge of the specs, was able by Monday to be producing and selling the case he had designed that weekend. Idea to production in three days. Is it any wonder that so many of the new hardware startups are using 3D printing to do quick prototyping?
      • Short runs/lots of customizationChances are most of the things you use in your life are not one of a kind (i.e. pencils, clothes, utensils, dishware, furniture, cars, etc). The reason for this is that mass production make it extremely cheap to produce many copies of the same thing. The flip side of this is that short production runs (where you’re not producing thousands or millions of the same thing) and production where each item has a fair amount of customization or uniqueness is really expensive. With 3D printing, however, because each item being produced is produced in the same way (by the printer), you can produce one item at close to the same per unit price as producing a million – this makes 3D printing a very interesting technology for markets where customization & short runs are extremely valuable.
      • Shapes/structures that injection molding and machining find difficult: There are many shapes where traditional machining (taking a big block of material and whittling it down to the desired shape) and injection molding (building a mold and then filling it with molten material to get the desired shape) are not ideal: things like producing precision products that go into airplanes and racecars or printing the scaffolds with which bioengineers hope to build artificial organs are uniquely addressable by 3D printing technologies.
      • Low laborThe printer takes care of all of it – thus letting companies cut costs in manufacturing and/or refocus their people to steps in the process which do require direct human intervention.
    • And, of course, with the new markets which are opening up for 3D printing, its certainly helpful that the size, cost, and performance of 3D printers has improved dramatically and is continuing to improve – to the point where the panelists were very serious when they articulated a vision of the future where 3D printers could be as widespread as typical inkjet/laser printers!

    Ok, so why do we care? While its difficult to predict precisely what this technology could bring (it is disruptive after all!), I think there are a few tantalizing possibilities of how the manufacturing game might change to consider:

    • The ability to do rapid design to productionmeans you could dofast fashion for everything – in the same way that companies like Zara can produce thousands of different products in a season (and quickly change them to meet new trends/styles), broader adoption of 3D printing could lead to the rise of new companies where design/operational flexibility and speed are king, as the companies best able to fit their products to the flavor-of-the-month gain more traction.
    • The ability to do customization means you can manufacture custom parts/products cost-effectively and without holding as much inventory; production only needs to begin after an order is on hand (no reason to hold extra “copies” of something that may go out of fashion/go bad in storage when you can print stuff on the fly) and the lack of retooling means companies can be a lot more flexible in terms of using customization to get more customers.
    • I’m not sure how all the second/third-order effects play out, but this could also put a damper on outsourced manufacturing to countries like China/India – who cares about cheaper manufacturing labor overseas when 3D printing makes it possible to manufacture locally without much labor and avoid import duties, shipping delays, and the need to hold on to parts/inventory?

    I think there’s a ton of potential for the technology itself and its applications, and the possible consequences for how manufacturing will evolve are staggering. Yes, we are probably a long way off from seeing this, but I think we are on the verge of seeing a disruptive innovation take place, and if you’re anything like me, you’re excited to see it play out.