Capital and Distribution in AI, and the Rise of Neoclouds with Kevin Zhang

Kevin is an investor at a family office, where he leads AI investments across asset classes. His career has spanned roles as a venture capitalist, startup founder, and software engineer, with experience in both Silicon Valley and New York, before moving to Asia. He brings deep technical and product expertise across domains from machine learning to enterprise software. In his spare time, Kevin writes East Wind, which is focused on technology investing.

In this conversation, Kevin Zhang shares his insights on the evolving landscape of AI investments, the implications of hyper-scaler capital expenditures, and the future of AI model training. He discusses the cultural differences between investment ecosystems in the US and China, the valuation of private market companies, and the role of neoclouds in the AI sector. Kevin emphasizes the importance of capital and distribution in determining the success of AI companies and reflects on the future of work in the context of AI adoption.

In today’s world, there’s no shortage of information. Knowledge is abundant, perspectives are everywhere. But true insight doesn’t come from access alone—it comes from differentiated understanding. It’s the ability to piece together scattered signals, cut through the noise and clutter, and form a clear, original perspective on a situation, a trend, a business, or a person. That’s what makes understanding powerful.

Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently.

For more information on the podcast series, see here.

Chapters

00:00 Kevin Zhang's Journey From Software Engineering to VC to Equity Investment

02:04 The Hyper-Scaler Capex Debate

04:31 The Capital-Intensive Nature of AI Models

07:49 Future of AI Capex and Market Dynamics

11:43 Understanding Private Market Valuations

14:49 Consensus Capital and Investment Strategies

17:10 Cultural Differences in Investment Ecosystems

21:50 The Future of Chinese AI Companies

23:50 Capital and Distribution in AI

27:47 Open Source vs. Closed Source Models

32:22 The Role of Neoclouds in AI

40:40 Investment Opportunities in AI and Beyond

Transcript generated by AI

Grace Shao (00:01)

Hi everyone, this is Grace Shao. Joining me today is Kevin Zhang. Kevin is an investor at a family office where he leads AI investments across asset classes. His career has spanned roles as a venture capitalist, startup founder, and software engineer, with experience in both Silicon Valley and New York, before moving to Asia. So he’s now based in Asia. He brings deep technical and product expertise across domains from machine learning to enterprise software. And in his spare time, Kevin writes a blog on Substack called East Wind. Go check it out.

It’s focused on technology investing. So Kevin, thanks so much for joining us today.

Kevin Zhang (00:33)

Hi, great pleasure to be here.

Grace Shao (00:36)

Yeah, tell us about yourself. You’ve had quite a journey, you know, from Silicon Valley and now into your base in greater China. You’ve worked as an engineer and now an investor. You know, that’s quite unique. Tell us about your professional journey.

Kevin Zhang (00:50)

Great. So I guess going all the way back to my college days, ⁓ studied computer engineering, both in Canada and then states for grad school. And then spent most of my career in the States ⁓ in Silicon Valley and New York. So started as an engineer at a company called Salesforce. So they make CRM software before ⁓ transitioning to a couple of venture funds, ⁓ one in the Bay Area, one in New York. So primarily the focus has been on

early stage software, AI investing. And in these days, I look primarily at public equities, specifically focusing on the companies that have exposure to AI or ⁓ businesses who ⁓ will see a re-acceleration of growth because of AI.

Grace Shao (01:39)

Awesome, so we’re gonna go straight into this. I think we have a lot to cover. ⁓ First, let’s get into the hyper scalar capex kind of story. We’ve been seeing jaw dropping capex commitments, alphabet raising, AI capex too, 85 billion, meta committing hundreds of billions, ba-ba 10 cent I think over the next couple years, also committing like 300 billion upwards. It’s just these crazy numbers.

Do you see this as a necessary investment or do you think they’re absolutely overspending right now and they’re creating a bubble?

Kevin Zhang (02:11)

I think if we look at past ⁓ technology cycles, there will always be, or there has always been ⁓ some overspend across the industry. ⁓ So I think ⁓ bring it down to on a per company level, I think things get more nuanced, right? So looking at, for example, alphabet, right? How much of that is internal workloads for search, right? For their Gemini answers, ⁓ looking at open AI where how, ⁓

whatever their, can we maybe start this again? Let’s do a rewind. Okay, great. ⁓ So I think across the industry, if we look at past ⁓ technology waves, ⁓ most famously in the ⁓ initial tech kind of a dot-com bubble, the industry has overspent, right? But as we zoom out, ⁓

Grace Shao (02:49)

Just go ahead, just restart your answer.

Kevin Zhang (03:11)

the spend becomes more normalized and then the demand ⁓ ends up catching up. And so I think the real question is on a per company basis, right? Whether it’s Alphabet, Meta, Azure, with Microsoft, AWS, how much of that is overspending? How much demand can they generate, right? With their ⁓ whale customers? And then if they end up overspending, how many of these players can survive, right? So for some of the smaller players,

who are comparatively more leveraged, who don’t have the cash flows to support some of the CapEx, right? They’re maybe in a little bit more of a dangerous territory than one of the Meg-7, where they’re generating a significant cash flow to ⁓ fund their operations.

Grace Shao (04:01)

And I think this kind of goes into also one of your writings recently. You were saying that, like, look, it’s all these big tech that are able to afford ⁓ spending on, like, LLM training and inference, as well as whatever infrastructure that’s needed to really build out, like, sophisticated LLMs. And you said it’s basically foundation models are a rich company’s game. Why is it the model layer is so capital intensive? And do you think that means we’re going to see the startups just kind of

in this field kind of just die out one by one or acquired or what’s trajectory going forward?

Kevin Zhang (04:38)

I think there are two paths that the industry can take. So the default path is if we look at ⁓ the progression of ⁓ costs for model training, whether it’s pre-training or post-training, ⁓ each generation has been significantly more expensive. So ⁓ many years ago, it might have been ⁓ several hundred thousand dollars. Then it went to the millions, tens of millions, hundreds of millions to train a model.

So assuming that trend holds, ⁓ we’ll see kind of billion dollar training runs, right? So taking a billion dollars to train, let’s say GPT-6 or GPT-7. And so if that is kind of where the world goes, then these companies will need to raise more and more capital to fund their training. They will raise more and more capital to fund their inference, right? So once you train a model, how do you serve it to...

⁓ and users, right? That’s also very, very expensive. ⁓ However, if you’re of the view that there will be, or transformers, which is kind of the models that are used in ⁓ LLM or ⁓ used in things like ChatGPT these days, ⁓ if you believe that there will be other model architecture paradigms ⁓ that are going to be significantly cheaper, then maybe there will be another kind of startup that comes and disrupts.

the entire business model of an OpenAI or Anthropic or any of these labs.

Grace Shao (06:07)

and was DeepSeat one of those that kind of disrupted the whole model.

Kevin Zhang (06:11)

⁓ I think not necessarily. think ⁓ going a little bit into the weeds, the, I think, five, six million dollar ⁓ final training run toute…

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