By Tela Mathias

The third annual AI Ascent event, an invite-only, elite event hosted by Sequoia Capital, started with a mind-blowing presentation by Sonya Huang, Pat Grady, and Konstantine Buhler. I’d say it took me at least two hours to get through the 28-minute opener as apparently, I had a lot of catching up to do. I kept hearing unfamiliar terms like “segfault”, “test time compute”, “vibe revenue”, and the “uncanny value theory”. I wanted to truly take in what I was seeing so I had to chase all these rabbits down the rabbit holes. I was a little bit in awe of the different dimension of thought these presenters and this audience were in.

Looking back over the past 70 years they shared the major technology waves notable in each decade, laying the context for the raging AI adoption we see today. Each of these waves was additive, and now the waves are coming faster than ever. They lay the foundation for ease of adoption of the next wave.

Sequoia thinks of AI broadly within the context of three major technology segments – mobile, cloud, and now AI. Then they look at the total addressable market for each technology segments as a way of illustrating the ridiculously large market for AI. I spent about 30 minutes alone in understanding this one chart.

The top row is the cloud transition, and the first circle is the global software market when the transition began – 6B of the 350B total revenue realized. Today that market is at least 650B, with 400B realized, so a bigger market was created as a result of the technology. The bottom row represents the global software and services market that can be addressed with AI, and the tiny segment of 15B is what Sequoia sees as realized today. It absolutely dwarfs the cloud opportunity, and the inclusion of services represents the acknowledgement that AI offers both tools and the opportunities to transform tools to radically change the way we approach services. I see this in my own business; virtually all commercial services we offer today are absolutely powered by AI.

We are just starting to see the transition from AI as a tool, to AI as delivering an outcome – and outcomes are the purview (traditionally) of services. Hence the total addressable market is a staggering 10T. Yes, that’s a T. Which brings me to the next chart I spent about 20 minutes on.

I was foolishly unaware of (but intuitively applying) the new physics of distribution, which says that to have a successful product, you need three things – your target customer has to be aware of your offering (awareness), they have to have the desire to purchase your product (desire), and they have to have the ability to purchase your product (action).

I didn’t put these things together, but from an awareness perspective, no one cared about cloud at first. Mark Benioff was out there pounding the pavement for anyone who would listen. With mobile, it took a while for Blackberry to get clobbered (Blackberry forever – RIP). But with ChatGPT (v3.5, with the human user interface), it blew up in the first week.

Which brings us to desire, represented as the combined number of active users (in millions) on reddit and twitter. This number represents the way people find out about cool stuff and is a proxy for desire. At the start of cloud, these things didn’t even exist, so the number is zero. We had about 4M with mobile, and then now we have 1.8+ BILLION. And finally, action. With cloud, there were only 200M people connected to the internet to listen to Benioff and now, at 5.6B, every household and business in the world is connected. So you can see that the foundation for AI had been materially laid for ChatGPT 3.5 before it got here. There were no barriers to adoption, which is not AI-specific – this is the new reality of technology distribution.

So we’ve established that there is massive opportunity to create value (10B TAM), and Sequoia and the industry at large believe that this value will come from the application layer (Sam Altman reinforced this later in his Q&A session). The new race for startups, then, is between foundation model providers (the tech-out perspective), and vertical specific application developers coming with deep customer intimacy (the customer-in perspective).

The second scaling law (test time compute) focuses on enhancing AI model performance during inference rather than training – so rather than more extensive model training, we allow the models to think more. When we combine this reasoning with tool use and interagent communication protocols, this lets foundation model providers get pretty damn close to the application layer. And the race is on. (This confluence of technology and market factors also creates what Sequoia calls the agent economy, which I won’t address in this article).

What to do with this as an AI startup?

Sequoia believes, as do I, that 90% of building an AI company is just building a company. The rest comes down to the Leone Merchandising Cycle and building moats around the stages in the cycle.

As a tech startup we can compete with foundation model providers (and other businesses, for that matter) at each step:

1. Vision – Customers often don’t know exactly what they want, we can have an opinion were foundation models probably won’t.
2. Product – We can provide an end-to-end solution to a customer problem, rather than throwing a tool over the fence and hoping the customer will find a way to use it.
3. Engineering – We can build data flywheels with product usage data, this is really the only information that absolutely no one else will have.
4. Marketing – We can be of the industry, for the industry, by the industry, we can know out market better than anyone else. And certainly better than a foundation model provider.
5. Sales – we can speak the language of the customer natively. We send mortgage people in to talk with mortgage companies.
6. Support – and we can put a “big ole bear hug” around our customers. Sequioa kind of scoffed at this but said that what Palantir has done with forward deployed engineers is certainly value and foundation model providers are very unlikely to do this.

The last thing I’ll cover is the stochastic mindset shift in the AI future.  This is a departure from traditional deterministic thinking, born out of traditional software development where you program a system to do a thing and it will always do that thing. Given a set of inputs A, you will always get B. We love that. It’s so comforting to live in this world. It’s binary. There’s not grey. But isn’t the world full of grey? Can’t two opposing concepts be true at the same time? I can be awesome just the way I am and also have serious room for growth and improvement? This is the stochastic shift away from deterministic thinking and into probabilistic thinking. Sometimes, given a set of inputs A, you might get output C even when perhaps the answer should be B. And this is the widow for uncertainty, dialectic thinking, human creativity. Oh how I love the grey.

There was so much more covered, but this article is already too long so I’ll leave you with my parting thoughts. As I started my journey with these 28 minutes, I was, honestly, overwhelmed at what I just didn’t understand. They were not even speaking my language. But after having had a day or so to process what I learned, I find that much if it is intuitive. I have a lot that I will take away and have already started to implement with my teams, but I am comforted by how natural much if this feels. That doesn’t mean that I’m ahead, however. Only that I have to run a lot faster to keep up. I’m so grateful that these materials were made available to the public.

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