Author: Elad Gil

AI: Startup Vs Incumbent Value


This post is by Elad Gil from Elad Blog


In each technology wave the value, revenue, market cap, profits and great people captured by startups versus incumbents differs. In some waves it all goes to startups, while in others it goes to incumbents or is split between them. Unexpectedly, the prior wave of value from AI roughly all went to incumbents over startups, despite a lot of startup activity. This post explores that dynamic and posits the current unsupervised learning wave of AI will contain strong startup success, in addition to incumbent value.

Some history

In the first internet wave most of the value went to startups (Google, Amazon, Paypal, Ebay, Salesforce, Facebook, Netflix) while some was captured by incumbents (Microsoft, Apple, IBM, Oracle, Adobe) who extended their franchises onto the internet. Perhaps this was a 60:40 or 70:30 startup:incumbent split.


For mobile, most of the value went to incumbents (Apple, Google, and then every mobile version of an incumbent’s app - e.g. “Mobile CRM” was not a stand alone startup but rather Salesforce on your iphone) while there will still significant capture by startups (Whatsapp, Uber, Doordash, Instagram, Instacart etc). Perhaps this was a 20:80 startup:incumbent split.


Crypto in contrast has been roughly 100% startup capture (Bitcoin, Ethereum, Coinbase, Binance, FTX, etc) with very little participation in value creation by existing financial services or infrastructure companies. Perhaps the biggest incumbent participants in crypto have been semiconductor companies like AMD or NVIDIA whose chips are sometimes used for token mining.


(Please note the term "startup" is meant to mean (Read more...)

Back to the office


This post is by Elad Gil from Elad Blog


Prior to COVID, there were only 3 companies in tech that reached any real scale as remote first companies - Automattic, Gitlab, and Zapier [1]. During COVID companies were forced to work remote. Many companies are now going back into the office and companies are navigating this transition in real time.

This post summarizes conversations with ~10 growth stage founders, CEOs, and companies about their go back to office approaches and attempted to summarize common takeaways below. This post is not about whether going back in is the right decision for a given company[2]. Rather, it attempts to capture some of the tactical considerations to go back in, if one chooses to do so. The focus is also on mid to late stage companies as back to office for an early stage 5 person company tends to be straightforward.


# of days back

Companies are ranging from 2 to 5 days a week already back in the office, with many clustering at ~3 days. A subset of companies plan to go all the way back to 5 days but are doing it in a stepwise fashion. Other intend to stay at 3 days a week ongoing. Companies that have already been going back 5 days a week for 5-6 months now and said it was a smooth transition. The key on choosing days includes:

1. Make the days in the office the “meeting days” for the company. Try to cluster meetings, social activities, etc. on the days (Read more...)

AI Revolution – Transformers and Large Language Models (LLMs)


This post is by Elad Gil from Elad Blog


NLP & AI Revolution - Transformers and Large Language Models (LLMs)

Part of the challenge of “AI” is we keep raising the bar on what it means for something to be a machine intelligence. Early machine learning models have been quite successful in terms of real world impact. Large scale applications of machine learning today include Google Search and ads targeting, Siri/Alexa, smart routing on mapping applications, self-piloting drones, defense tech like Anduril, and many other areas. Some areas, like self-driving cars, have shown progress but seem to continuously be “a few years” away every few years. Just as all the ideas for smart phones existed in the 1990s but didn’t take place until the iphone launched in 2007, self-driving cars are an inevitable part of the future.


In parallel, the machine learning (ML) / artificial intelligence (AI) world has been rocked in the last decade by a series of advancements over time in voice recognition (hence Alexa), image recognition (iphone unlock and the erm, non-creepy, passport controls at Airports). Sequential inventions and discovery include CNNs, RNNs, various forms of Deep Learning, GANs, and other innovations. One of the bigger breakthroughs of recent times was the emergence of Transformer models in 2017 for natural language processing (NLP). Transformers were invented at Google, but quickly adopted and implemented at OpenAI to create GPT-1 and more recently GPT-3. This has been followed by other companies or open source groups building transformer models such as Cohere (Read more...)

Startup Markets, Summer 2022 Edition


This post is by Elad Gil from Elad Blog


About a month ago, I wrote a tweet storm on the changing startup financing and employment environment. This blog captures aspects of that tweet storm and some of its predictions and extends them further. Like all predictions this is what I view as a highly likely scenario versus the only potential future path for the next 3-18 months or so.

The high level view is that things have yet to get truly bad in private tech.  2021-2022 were an anomaly due to COVID policies which both created an incredibly cheap low interest money environment, pumped the stock market, and facilitated adoption of certain types of tech. This environment led to both excess in fundraising but also in hiring. This means that as money transitions back to to "normal" levels teams that were hired too far ahead need to shrink. Many areas (hiring plans, valuations, time venture capital raised lasts, etc) are roughly reseting to 2018/2019 norms, which themselves were all time highs prior to the COVID era.

If interest rates and money supply continue to tighten and a recession happens, then things should get worse. The below largely deals with the base case of things roughly stay where they are now. More likely, things will get worse before they get better. Nonetheless, it is still a great time to start a company.

So what do the next few quarters look like?

1. Financings

Valuations will continue to drop and are not stable yet

Private markets tend to lag adjustments in (Read more...)

Crypto Twitter


This post is by Elad Gil from Elad Blog


With Elon Musk joining the board of Twitter, now is a good time to consider all the things Twitter should be doing, that it has never done. There are lots of obvious features (edit tweets, longer form content, better user controls, better onboarding, better spam filtering, business account support and integrated CRM and CS, etc) that were being debated within Twitter even when I was a VP at Twitter 10 years ago! 

So instead of focusing on all those things, I thought it would be interesting to think about moving Twitter from the web 2 into a web 2/3 hybrid. This could open up significant user benefit, as well as monetization potential for Twitter. If a Twitter buy-out were to ever occur, these might be interesting mechanisms to increase the value of the company substantially.

Potential parts of crypto Twitter [1]:

NFTs. 

  • This is an obvious one - people on Twitter should be able to mint and distribute NFTs via Twitter. Imagine OpenSea as a deep Twitter integration. As you purchase an NFT on the platform you could be prompted to swap this in for your profile image.
  • Distribution of NFTs could occur via the platform with for example content creators providing priority access to their followers.
Tokens.
  • Twitter could do "Bitclout the right way". For example, each person could have tokens issues for them that could be purchased, distributed, and used for a variety of in app use cases including purchasing of NFTs (see above) or participating in some (Read more...)

MegaCycles in Tech & Crypto


This post is by Elad Gil from Elad Blog


Every 8-10 years, the technology industry used to go through a boom and bust cycle. A new technology or platform would emerge, there would be rampant investment and speculation, a few strong hypergrowth survivors would emerge and most of the rest of the new startups would collapse or get consolidated. This happened with semiconductors in the 60s & 70s, microcomputers in the 80s, and the internet in the 90s. 

Each successive wave was bigger than the prior - both in terms of market cap created as well as money that flooded in. 



In the 2000s until now, something odd happened. The venture boom and bust cycle stopped. Things in tech have been replaced by a single long ~20 year boom. Even the financial crisis of 2007-2009 did not impact tech startup formation or growth much.

While there are undoubtedly multiple drivers for this lack of tech cyclicality [1] one potential explanation is the stacking of technology waves on top of each other. Instead of a single cycle drawn out over 8-10 years for a single new technology, we are now seeing multiple overlapping tech waves all happening on top of one another. This is both increasing the size of the overall resultant result, as well as smoothing out any down cycles.

For example, from 2005 until now we have had overlapping waves of cloud, social, mobile, SaaS, vertical SaaS, fintech, AI, and crypto. All of these would have had their own 10-year cycle in the past. One could argue we (Read more...)

The False Narrative Around Theranos


This post is by Elad Gil from Elad Blog


One of the interesting aspects of the Theranos trial is the degree to which some folks are buying into part of the defense's narrative that "Theranos was just acting like every Silicon Valley startup". This is of course blatantly false, but it is being adopted as some form of truth. 

The claim is that every tech founder somehow pushes the envelope of truth, and therefore that is all Theranos did (versus potentially committing fraud over a 15 year period, lying to regulators, physicians, employees and investors while endangering patients).

This analogy breaks down on multiple levels. 

There is a big difference between drunk driving at 90 mph in a school zone versus driving 5 miles too fast on the freeway. (Or, in the case of most tech companies, simply respecting the speed limit).

While there are obviously some bad actors in tech, there does not appear to be quantitative evidence to suggest this is any worse than in non-profits, finance, mediahollywood, or any other sectors

Where the analogy breaks down between Theranos and the "average" tech company:

1. Most technology companies do not lie about their product or service. If they did so, they would not be able to attract or retain great employees, or scale revenue and product adoption so rapidly. If their product did not work, no one would use it. This is especially true over a decade+ journey. Despite some high profile counter-examples, most tech companies are honest companies run (Read more...)

Unicorn Market Cap, June 2021 (Almost Post-Pandemic Edition)


This post is by Elad Gil from Elad Blog


I have previously written about Unicorn Market Cap and Industry towns in 2019 and 2020. Over the last 8 months the number of tech startups worth $1B or more ("unicorns") has grown by 43% from 487 Unicorns to 701. This is almost double the 361 unicorns in June 2019 (!). 

Data was taken from CB Insights and a special thank you to Shin Kim, CEO of Eraser for the data and graphs. 

Caveat emptor: data from CBI is updated/reconciled over time, so very recent unicorns may not be included yet. However this provides a directional view.... Raw data here.

NEW UNICORNS

The regional nature of private tech market cap continues to dominate. The big shifts over the last year include:

NEW UNICORNS SINCE OCTOBER 2020
(1) United states: Over 67% of the new unicorns by # are in the USA with 154 total. (out of 227 globally)
There were 69 new unicorns in Silicon Valley, 30 in New York, and 8 in Los Angeles. 

This is increasing share for both the USA & Silicon Valley as a % of global tech unicorns, with NY and LA accelerating somewhat. New York anecdotally feels like it has transitioned into a break out cluster of its own.

The pandemic has increased unicorns in the USA at a fast clip.

(2) China has slowed on new unicorn generation.

While China is 29% of all unicorn market cap, it only added 9 new unicorns (roughly 4% of global total) since October 2020.

Anduril & Defense Tech


This post is by Elad Gil from Elad Blog


The last year has demonstrated repeatedly the lack of societal preparation for multiple forms of threats to our country and world. Examples of this include issues responding to the COVID pandemic, the cybersecurity ransomware attacks on critical US infrastructure such as our energy systems and food supply, and of course the ongoing issue of climate change[1]. In parallel, new technologies are increasingly being used by antagonistic groups and countries for terror and war. Drone attacks on international oil pipelines and their use in regional conflicts has increased dramatically in the last few years, as have drone related incidents and accidents around airports, sports stadiums, and infrastructure such as power plants. 

Just as old incumbent institutions with little to no organizational renewal impacted our ability to respond to COVID, the defense industry has undergone significant consolidation over the last 30 years. There has not been a new defense technology company of any scale to directly challenge these incumbents in many decades (SpaceX and Palantir are obviously sizeable ~20-year old “newer” entrants in adjacent areas).



One exciting newer defense technology company that is working on building capabilities around sensor-based awareness and anti-drone activities is Anduril. I am excited to lead their latest round of funding. Anduril will use this funding in part to drive new acquisitions, as well as an ongoing ramp in their team and business. Their main solutions currently include a series of sensor networks, towers, drones, and powerful software that ties it (Read more...)

Back To The Office


This post is by Elad Gil from Elad Blog


US should be roughly done with COVID in 2021

Almost exactly a year ago, I wrote a blog post warning the tech community about a new coronavirus in China[0] and the likelihood we will see multiple viral waves.


By end of March, 2021, the US is estimated to have enough vaccine to cover roughly half its adult population for COVID. By end of July, Pfizer and Moderna alone should have supplied the US government with 600 million doses - enough to cover roughly the entire US population including children. Assuming it takes a month or two to distribute the vaccines and another month between shots (and a month to make some mistakes, because, why not?), most of the USA should be covered by sometime in the fall[1]. The country should start to reopen sometime during the summer (probably red states first) with no strong logical reason not to reopen roughly everything by end of year [2]. This means by end of 2021 we should anticipate most teams back in the office barring something unexpected.


Image source 


By end of 2021, a big enough portion of the developed world will be vaccinated to allow previously remote offices to reopen and work to return to normal [2]. As companies think ahead on the future of work, a few different models emerge. In this post we discuss models of work, the future of corporate travel, and an aside on the Bay Area & tech.

Source


Many people now want to go back (Read more...)