Chi-Hua Chien saw Facebook coming; now he says the real AI winners won’t be selling AI

Chi-Hua Chien has spent more than two decades as a venture capitalist, but he thinks like a cultural anthropologist. As a co-founder of Goodwater Capital, a firm focused exclusively on consumer and prosumer technology, he has built a portfolio spanning entertainment, healthcare, fintech, and live experiences — with investments in companies like MIDI Health, Fever, and Monzo. He was also, as a 27-year-old associate at Accel, the person who initially found a six-person company launched from Harvard called The Facebook.

That ability to read human behavior at scale informs everything from his view that Americans will never trust a single app with both their social lives and their finances, to his belief that the gap between the most advanced AI model and what you can run on your phone — once as wide as two years — will shrink to three months within the next year.

These days, he is also willing to say out loud what many in venture capital are only thinking: that the commoditization of the model layer is already underway and that the biggest winners of the AI era won’t be the companies selling AI at all.

We talked last week. This interview has been edited for length and clarity.

More founders and investors have been publicly sharing their grievances about VCs lately. What’s changed?

It’s part of the meme-ification of everything — you’re seeing what’s happening in the political realm bleeding over into the business side, and it’s probably also the sign of some peakiness in the market. The reason you’re seeing some of these outspoken investors talking more publicly is because venture firms have largely vertically integrated, so the really big ones have enough capital that they’re not necessarily looking for syndicate partners. There used to be decorum around wanting to preserve good relationships with other co-investors, because you got to work with them at different points along the line. As the firms have gotten bigger and vertically integrated, there’s less of that need.

What about the “fast follow” rounds — where firms invest a large chunk at one valuation and a smaller amount weeks later at a much higher one, making the headline number look more impressive than it really is? Is this really new? How pervasive is it?

I think it’s been going on for quite some time. The best companies raise successive rounds very quickly — there might only be three to six months between rounds now, and valuations change really quickly … Valuations are being marketed very aggressively as a way of demonstrating market leadership, attracting talent, potentially blocking out competition. There’s probably some element of frothiness, because what these fast financings are most illustrative of is there’s way more demand than there is supply. An investor can come in, set a price, complete a financing, and then a couple of weeks later there’s still excess demand — and the company can immediately price a new round at a higher price.

You’ve argued that infrastructure companies get commoditized and that applications capture most of the value over time. Are we already seeing that play out in this cycle?

If you look at the PC cycle, the web cycle, and the mobile cycle, they all follow fairly consistent patterns. Infrastructure market caps actually peaked in the year 2000 — but you fast-forward 25, 26 years later, and in nominal dollar terms, the market cap of those infrastructure companies has not surpassed the 2000 peak. In the web era, infrastructure new entrants produced $400 billion of new market cap. Application companies created $3.1 trillion — 88% of the new value. In the mobile era, it’s very similar: Infrastructure produced about $700 billion, while application companies produced $3.7 trillion. Companies like Netflix, Spotify, Meta, Uber, Airbnb.

And [last week] you saw something pretty interesting: Google announced that their subscription AI product is dropping price from $7.99 a month to $4.99 a month and doubling the storage. We’re already in the era of price competition — and companies like Google, with structural advantages in vertical integration and distribution, can start bundling and price competing for the average consumer.

You keep coming back to personalization as a through line. Is that what separates the next wave of winners?

Hyper-personalization definitely is a key through line, because what does personalization give you? If done right, it gives you higher customer satisfaction, deeper engagement, and higher ARPUs over time.

We have entertainment companies in our portfolio — companies like Triumph and Ritten and Flow GPT — where the customer is not saying, “This is an AI application.” They’re saying it’s an entertainment application. These companies are going into 100 million, 400 million, 600 million of ARR very quickly, at great margins, because AI makes the experience more customizable and more personalized — but it’s not the fundamental capability they’re selling.

We also have a women’s health company called Midi Health. One of the fundamental constraints in women’s health is that there aren’t that many providers well trained in hormone replacement therapy for perimenopausal women. By using AI, they’re able to substantially expand the supply of care and treat hundreds of thousands of patients that otherwise couldn’t be reached. And they can do it cost effectively, which expands access to a market that was previously supply constrained. You can play that forward across every supply-constrained category where human expertise is the bottleneck.

How far away are we from AI that feels truly personal and ambient?

I don’t think we’re very far away at all. You can run locally now on your phone AI models that are as good as the best models were about six months ago — and that lag is shrinking. You go back two years ago, the lag between what you could run locally and what was in the cloud with the frontier models might have been 18 to 24 months. It’s now six months. It’s probably getting down to three months by this time next year.

What we don’t yet have is the use cases very well defined. You saw this in mobile — when the iPhone launched in 2007, people largely thought it was going to be all of the web applications ported over to mobile. It takes time for entrepreneurs to percolate around what is now possible.

What LLMs do, if you extrapolate away from how they work to what they do, is basically two things: They make it possible for you to process large amounts of context and make sense of it all, and they allow you to do personalization down to the individual, cost effectively, with a feedback loop that makes the product better and better over time.

You’ve watched Facebook try and fail for years to build a super app. Why is it so hard to blend financial services and social entertainment for American consumers?

They’ve taken multiple shots on goal — Facebook Credits, which launched in 2009 … Facebook Pay, Libra … They’ve never been able to realize a true super app. I think people have an intuitive perspective on trust, and there is a trust gap between entertainment and social products, and commerce, banking, financial services — particularly in the Western world.

There is a seriousness to financial transactions that is very different from the triviality of social media. And don’t get me wrong — that triviality has created a trillion-plus-dollar company. But financial services is actually the complete inverse: While audience has very high time and relatively low monetization, financial services transactions are very high monetization and relatively low time. You don’t want to hang out in your banking app. You want to transact and be done — but with extremely high confidence in the security and reliability of that transaction. That psychological expectation from customers is a very tough one to bridge.

Are you placing bets on people craving in-person connection as a counterreaction to all of this?

I think we’re swinging back in the other direction from pure online consumption, and AI as enabling technology, knowing where you go, who you hang out with, where you tend to spend time, can extrapolate a ton of relevant interests that make that real-world experience more useful and more personal. That’s super exciting to us.