Category: AI

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…

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Macro-Optimism, Micro-Skepticism: A Framework for AI Investing

This Generative AI wave is both a tremendous opportunity over the long term and a ticking bomb in the short term.

Sharing a framework to navigate & eventually thrive in this hype cycle as a tech investor.

As the Generative AI fire rages on with full force, I have been thinking through the best approach for me as an operator-angel to navigate the current environment.

What makes this AI wave particularly challenging for venture investors is that it’s full of contradictions depending on what time horizon you choose to view it from.

In the short term…But over the long term…
The space is clearly in the early stages of the hype cycle.It’s perhaps the most defining technology shift of our lifetime, likely to drive a socio-economic change like the agrarian ➡ industrial age transition.
Though AI is “consensus” in Silicon Valley, the agreeing crowd has a track record of being right quite often.The only way to generate outlier returns is to be “non-consensus-and-right”.
Early entrants are likely to attract significant venture capital, potentially generating quick mark-ups for early investors.Like previous platform shifts (eg. Web and Mobile), early entrants are unlikely to be the eventual winners (there were at least 8 major search engines before Google came along).
Pre-product stage startups commanding rich valuations is perhaps justified, given investor-demand & the hockey stick growth potential of the space.The best way to generate above-average returns is investing in the best companies at reasonable valuations.

Clearly, there is a time horizon tension at play here. As an investor, one doesn’t want to miss out (or appear to have missed out) on the earliest stages of the greatest platform shift in our lifetimes. At the same time, as the recent Web3 wave taught us, maintaining discipline during hype cycles is key to ultimately realizing cash-on-cash returns.

To manage this tension & navigate this wave in a risk-adjusted manner, I have been using a framework I like to call “Macro-Optimism, Micro-Skepticism”. This approach involves always keeping two opposing emotions in your mind while evaluating opportunities:

Macro-Optimism – a strong belief that AI is going to be a super-powerful force of positive change in our lifetimes. Having this belief should translate to an immense yearning to learn as much as possible while the tech is still embryonic. It should also translate to keeping an open mind about its possibilities & having the imagination to think about “if it works in this way, what could this idea become?”.

It should lead to a low-ego & eyes-wide-open mindset while meeting founders working on the frontiers of AI. It should also lead to having the awareness to not underestimate any person or idea, no matter how divergent it sounds within your current lens.

Micro-Skepticism – realizing that in the initial stages of a hype cycle:

(1) most ideas will turn out to be invalid, as how a major platform shift shapes the future is, to quote Brad Gerstner of Altimeter Capital, “unknown & unknowable”. And;

(2) the space will initially attract a lot of low-quality actors, including scammy founders, tourist investors & others with a get-rich-quick mindset.

Realizing this should translate to looking at each new investment opportunity with default-skepticism – keeping the bar high, asking hard, intellectually honest questions & calling BS when you see it. This approach requires running a rigorous conviction building process, keeping FOMO at Bay & staying true to your investing value system.

Of course, parallel processing these opposing ideas is easier said than done. As I wrote in my recent post “Investing Landmines”, we are susceptible to many biases that get further exaggerated during hype cycles. Some ways to get better at managing them include:

1/ Leveraging complementary peers or team members that can keep you honest & call out your blind spots.

2/ Using some sort of light-weight system to ensure you are asking all critical questions & spotting typical pitfalls. As an example, learning from the likes of Atul Gawande & Mohnish Pabrai, I have found simple checklists to be helpful.

3/ Consciously sleeping on a deal before pulling the trigger, giving the ‘think-slow’ part of your mind enough time to digest facts.

Ultimately, am excited at the opportunity this AI wave is providing for investors with a growth-mindset to test & fine tune their systems. While I have no doubt that all of us in the tech ecosystem will benefit from this platform shift one way or another, I also hope some of us emerge wiser from it.

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David (Microsoft) vs Goliath (Google)

Image Source: Channel Futures

By coming out & saying “I want people to know we made them dance” in this clip, Satya Nadella has officially announced the beginning of an AI war with Google, who in turn, has also accepted the challenge by launching its own version of ChatGPT called Bard (unfortunately, the launch was botched, wiping out ~$100Bn from its market cap in a day).

Btw, how awesome was this clip? Just the look in Satya’s eyes & the intent behind the statement fired me up, & I don’t even work at Microsoft.

The first battleground of this war is Search. And it’s expected to see classic “David vs Goliath” type asymmetric warfare (Google has 90%+ market share in Search, as opposed to single digit % for Bing). Goliath has everything to lose while David has relatively fewer resources (existing Search distribution in this case).

So, how would each of them be thinking about war strategy? There are clues in asymmetric military wars that have unfolded historically eg. the US in Vietnam.

David’s view (Microsoft):

David can’t beat Goliath in conventional warfare due to the sheer gap in resources. So, it doesn’t make sense for him to engage Goliath by following standard rules in the open. David’s best bet is to engage with Goliath unconventionally, perhaps playing by a new set of rules ‘cos that’s when existing resources will mean less.

Real-world examples of this include (many of these ideas are covered in Sun Tzu’s Art of War, & can be seen in historical military confrontations):

  • Attack Goliath when he least expects it.
  • Target areas where Goliath has more to lose than David (eg. a classic nuclear threat).
  • Avoid a battleground that Goliath is familiar with. Take the battle to unfamiliar territories.
  • Prefer guerrilla warfare over all-out confrontation.
  • Use new modes of warfare wherein there is more parity with Goliath eg. economic warfare, communications warfare, strategic diplomacy etc.
  • Engage in indirect conflict by leveraging third parties that have some edge over Goliath.

If one closely observes how Microsoft is approaching the AI war in Search, it’s using many of the above elements.

First, under Satya’s leadership, Microsoft made itself stronger as a software conglomerate (Teams winning over Slack, LinkedIn’s massive moat, Azure taking a significant lead over GCP etc.). This has brought it more parity with Google at a group level.

Second, while Microsoft has increasingly become an agile & aggressive war machine, Google’s unthreatened monopoly in Search has eroded both the rate of innovation & sense of urgency from its operating culture. In a way, Microsoft is attacking Google when it is at its weakest culturally, while itself being at its strongest in a decade.

Third, the rise of AI is fast changing the rules of the game and as OpenAI’s ChatGPT has shown, Search is likely to look very different in the future. This change is being organically driven by a technology inflection, making Google’s existing dominant position in Search potentially less meaningful going forward.

Fourth, Search is a battleground where Google has much more to lose than Microsoft – the classic Innovator’s Dilemma. Microsoft can afford to take bolder bets, while Google has to fend it off while also protecting its existing business.

Fifth & final, Microsoft is leveraging a third party (OpenAI) as a main actor in this war. Unencumbered, unpredictable, agile & brave – third parties like OpenAI are hard to figure out & gameplan against by large incumbents, similar to how large military machines often struggle against guerrilla warfare.

So, how can Goliath counter these tactics?

Goliath’s view (Google):

While David’s main aim is to use his “brain” & make the battle as unconventional as possible, it makes sense for Goliath to use his “brawn” & exploit David’s vulnerabilities, in particular the disparity of resources.

Some ways he can do this include:

  • Attempt to drag the war back to familiar territory.
  • Open multiple fronts against David so he is forced to spread his resources thin.
  • Drag the war out for as long as possible, to drain David’s resources.
  • Cut off any access points that David can use to replenish.
  • David’s key strength is his morale so think of ways to destroy it.
  • Focus on de-throning the general & the army will automatically collapse.

So, while Microsoft’s challenge appears stiff, Google can use many strategies to counter it.

First, Google shouldn’t be deterred by the first punch. It can strategically prepare itself for a long drawn-out war & leverage its Search distribution might to outlast the competitor.

Second, it can open up multiple fronts against Microsoft to distract it. Potential areas include Cloud, enterprise workflow (GSuite) etc.

Third, given AI is so early, there isn’t likely to be any first-mover advantage. As we speak, many high-quality teams are already working on OpenAI competitors, providing Google with a valuable opportunity to partner with them & make up for lost ground.

Fourth, one of Microsoft’s major strengths is its leader. Google should be open to making moves in the market that distracts Satya or puts him under pressure.

Fifth & final, Google should use this rare competitive pressure to revitalize its execution culture. Perhaps one of the founders returning to the helm is a possibility? If taken in the right spirit, this is a valuable opportunity for the company to reset itself for the next 2 decades.

These are just game-theory conjectures at this point. Given the resources at the disposal of both companies, this AI war in Search is likely to unfold over several years. We will see many of the above tactics get played out in each scene, which will be tremendous learning for lifelong students of strategy like myself.

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