AI Musings #1 – How The Odds Are Stacking Up?

From OpenAI getting close to $100Bn valuation and Anthropic partnering with Amazon, to Google and Meta doubling-down on their LLMs faster than ever before, the AI chess game is getting more intriguing by the day.

In this post #1 of the ‘AI Musings’ series, I share a few running thoughts on the odds for each category of players.

**This is the first post in a series called ‘AI Musings’ that I hope to write regularly over the next few months. The idea is to periodically analyze major developments and milestones in AI, both from a startup and BigTech perspective.

Frantic activity around AI continues in the US. Just in the last week, OpenAI is looking at a $80-90Bn valuation for a secondary sale of existing employee shares. Even as Anthropic announced a strategic collaboration with Amazon last week, which includes up to a $4Bn investment, there is news today of the company raising another $2Bn from Google and others at a $20-30Bn valuation. This is a 5x jump from its last round valuation in March.

Greylock has gone AI-first with its newest early-stage fund. The Nvidia stock continues to rip (read my post on how it illustrates The Bunches Principle). Dharmesh Shah (Co-founder and CTO of Hubspot) is back to coding and selling, building ChatSpot over a weekend of hacking as a first step towards making his CRM AI-powered.

Amidst all this action, I have been meeting academics, founders, investors, and BigTech operators working on the frontiers of AI, trying to refine my hypothesis on the space. Here’s a working version of some of my thoughts:

1/ High confidence that AI is real and here to stay

Though the space is definitely in a financing hype cycle, to me, it’s now beyond doubt that AI as a platform shift will be transformative for the world. Unlike Web3, progress around AI has been driven by large tech companies since the very beginning. These companies are much too shrewd and tracked to spend significant resources on something that is merely a low-probability moonshot. Therefore, they have been focused on driving real commercial value from LLMs from Day 0.

OpenAI first launched ChatGPT on Nov 30, 2022. The fact that Generative AI capabilities are already integrated into mainstream products like the MS Office suite, Google Search, LinkedIn, Notion etc. in less than a year just goes to show that this particular platform shift is happening significantly faster than the Internet, Mobile, and Cloud.

Another confidence booster for me personally has been the commercial revenue traction of AI-native hyper scalers. Here are some numbers based on my research:

CompanyStartedLatest Valuation Current Revenue Traction (Est.)Source
OpenAI2015~$80-90Bn, reported as of Sep’23$80Mn est. MRR (~$1Bn annualized), reported as of Aug’23Reuters
Anthropic2021~$20-30Bn, reported as of Oct’23$200Mn proj. revenue in 2023, reported as of Sep’23 Information
Cohere2019~$2.1Bn, reported as of Jun’23Sub $50Mn proj. revenue in 2023, reported as of Aug’23Industry Sources
Hugging Face2016~$4.5Bn, reported as of Aug’23$30-50Mn est. annualized revenue, reported as of Aug’23Axios

These are tangible business revenues generated from enterprises, SMBs, and individual developers as customers. And the ramp-up over the last 12 months is astonishing. Honestly, looking at the depth of commercial traction these hyperscalers are showing, the valuation numbers don’t look entirely out of whack.

2/ Large incumbents are highly likely to capture disproportionate value from AI

About 9 months back, when Google’s stock was tanking as a reaction to ChatGPT’s growth and OpenAI’s partnership with Microsoft (a botched Bard demo made things worse!), I asked this simple question:

In hindsight, this was a very pertinent question to ask. As various BigTech-AI hyperscaler partnerships are playing out, it’s becoming clearer that large incumbents are strongly positioned to capture a significant portion of market value created from AI. They have a unique combination of the following:

  • Chips and cloud computing infrastructure to train and deploy foundational models, as well as build custom applications that are reliable, safe, and secure.
  • Distribution reach to get Generative AI in the hands of exponentially more customers.
  • Capital to place bets on AI hyper scalers and align with them to leverage their core strengths around faster and more disruptive innovation.

Bill Ackman, who runs Pershing Square and is one of the top-performing hedge fund managers, has been doubling down on Google since its price hit the $80-90 range post-ChatGPT. Here’s his rationale on why Google is strongly positioned in an AI world:

Bill Ackman’s (Pershing Square) pitch on Google’s positioning in AI

Based on my conversations with senior AI operators at the likes of Google and AWS, I believe the AI manifestations we are currently seeing in their mainstream products are not even the tip of the iceberg. Think of them as small experiments or POCs. The depth and range of their pipeline of AI capabilities are beyond regular imagination.

Btw, I am a believer in Bill Miller’s thought – “The economy doesn’t predict the market. The market predicts the economy. Going by how BigTech stocks are ripping amidst a rather cool economic and market environment, the wisdom of public markets also suggests that these incumbents are poised to reap huge dividends from AI.

So, amidst all the noise and hype, if you are trying to figure out a simple, risk-adjusted way to benefit from this AI platform shift, here’s a thought to consider:

3/Early-stage startup plays are still fuzzy

After spending significant bandwidth meeting AI founders, I am seeing that, as opposed to the BigTech and AI Hyperscaler plays, there is significantly more fuzziness in the early-stage ecosystem (and rightfully so!).

Inspired by the recent SaaStr session between David Sacks (Craft Ventures) and Jason Lemkin, here are my running thoughts on 3 categories of AI startups:

(I) Infrastructure

These include LLMs and other aspects of foundational AI infra. This bucket is really challenging to invest in simply because:

  • Building AI infra requires deep technical chops and/ or very specific prior experience, ideally in a particular set of companies. These teams are rare, extremely hard to source, and often get spotted very early by the likes of Sequoia and A16Z.
  • AI infra startups require large amounts of capital and therefore, need major VCs to be in them from very early on. In other words, these companies are hard to bootstrap, and funding them requires playing a very different kind of game that’s hard for a small check writer to play.

(II) Classic vertical SaaS with AI capabilities

The hypothesis here is that given AI is a massive platform shift, does it create new gaps in existing verticals like healthcare, education, sales, customer support etc. that a fresh generation of AI-first startups can exploit?

The hurdle I face while evaluating these startups is – why wouldn’t an existing growth or late-stage company just leverage AI as a new capability in their existing product suite? Incorporating AI features into an existing installed base (eg. what Microsoft is doing with OpenAI) seems like a superior ROI proposition compared to taking a brand-new product to market.

If this generalization is indeed true, it definitely raises the bar for this bucket. However, again to think out loud, there are some contexts where there could be a real commercial case for new AI-powered vertical software. For eg.:

  • Legacy verticals where fewer growth-stage startups of the prior generation have entered – say transportation? Or construction? The argument here is that it’s easier to beat old incumbents by using AI as tech leverage, compared to other late-stage startups who might be equally good at incorporating it.
  • Verticals where brand new paradigms are opening up, which will change the game itself – given winner-takes-all dynamics in tech, most incumbents are hard to beat at their own game. But, if the game itself changes (often due to a tech inflection), then David has a better chance against Goliath (read my post “David (Microsoft) vs Goliath (Google)“). Eg. using AI in genomics, drones, automotive etc. to solve problems and deliver work in totally new ways.

(III) Job co-pilots

The hypothesis here is that AI will spawn a generation of job-specific assistants called co-pilots, that will make a specific job more efficient and effective. So everyone from a doctor and lawyer to CFO and marketer will have a co-pilot that does everything from workflow automation to insights generation, all in a conversational UX.

This seems to be an extension of the productivity-software thesis that many VCs followed over the last 5 years. Sounds interesting and plausible, though I am still not able to build conviction on what a winning company in this space could potentially look like, how it would need to be capitalized and built, and whether it can generate venture returns.

I am learning new thesis, approaches and frameworks every week, especially related to the early stage startup plays in AI. More to follow in AI Musings #2…


to my weekly newsletter where in addition to my long-form posts, I will also share a weekly recap of all my social posts & writings, what I loved to read & watch that week + other useful insights & analysis exclusively for my subscribers.

Author: Soumitra Sharma

Operator-Angel I Product Leader I US-India corridor I Believer in Power Laws I Love building & learning

One thought on “AI Musings #1 – How The Odds Are Stacking Up?”

Leave a Reply

%d bloggers like this: