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Posted September 30, 2025 at 12:13 pm
AI is reshaping global markets, but where should investors place their bets — with agile startups or established tech giants? In this episode, Andrew Wilkinson speaks with Max Chen of Etna Capital and Derek Yan of KraneShares about the risks, returns, and portfolio strategies for navigating the AI revolution.
The following is a summary of a live audio recording and may contain errors in spelling or grammar. Although IBKR has edited for clarity no material changes have been made.
Good morning. This is Andrew Wilkinson. Welcome to today’s IBKR Podcast. I’m your host, and I’m joined today by Max Chen, a partner and portfolio manager at Etna Capital in Hong Kong, and Derek Yan, CFA, a senior investment strategist at KraneShares. Welcome, Max. How are you?
Good. How are you?
Doing well, thank you. And Derek, how are you? It’s been a while.
Thank you. Yeah, I’m great. Thanks for having me.
We’re going to be talking all about artificial intelligence and some of the companies within the sector. So let’s start with you, Derek. Can you introduce the idea behind investing in both public and private artificial intelligence companies within a single portfolio?
Sure. Yeah. When we look at AI, it’s probably the biggest investment opportunity for the coming decades. But when we look at the marketplace, a lot of investment is really done in a traditional way. There are two problems. First, a lot of investment is not really situation-aware. They’re not at the forefront of investing in foundational models. This industry is so dynamic and evolving, and many investors don’t know what the latest model capability is that’s going to transform business models — and that creates a problem.
The second problem is that the most critical and important companies in AI are actually private. Companies like Anthropic or xAI — these foundational model companies are so critical to driving the value chain. So at KraneShares, when we are trying to invest in AI, we want to create a solution that addresses those two problems. That’s why we partnered with Etna Capital, a team founded by AI-native researchers, to put both public and private exposure into a single portfolio. That way, we can capture the whole AI value chain.
So Max, when investing in private AI companies, how do you get access, and why does it matter for investors?
We’ve been private investors for a long time and know the private investment world quite well. The fund actually sits directly on the cap table of those private companies — meaning the ETF is a direct shareholder of those assets. The structure matters a lot for two reasons.
First, it avoids multiple layers of fees. When you’re on the market, you’ll be introduced to many private access opportunities by brokers. But if you look deeper, you’ll find multiple layers embedded in those structures — not only management fees but also carried interest, which eventually compromises the investment return for clients.
Second, the structure provides transparency of ownership and allows us to build long-term relationships with the companies. For example, if Anthropic or some other private companies grow even larger, we want to keep supporting them through to IPO. That’s a strategy often used by incumbent ETFs — doubling down on the winners — and it’s only possible when you’re directly on the cap table of a private company.
Derek, what’s the mechanism for a portfolio manager when including private exposure? How is liquidity for investors maintained?
At KraneShares, we manage ETFs. An ETF provides intraday liquidity, or at least daily liquidity, for the NAV. So in a portfolio with both public and private assets, the ETF itself is actually a 40 Act fund. The SEC requires any 40 Act fund to hold less than 15% in private exposure. At KraneShares, our fund targets about 10% in private assets. It’s not the majority of the fund, but it’s a meaningful allocation.
Second, like any mutual fund, we have a fair value committee to determine fair value for those private exposures so we can calculate our daily net asset value. That means investors can trade the ETF around NAV daily, which provides liquidity while still giving access to private exposure.
Max, Derek mentioned earlier that AI is probably a huge investment opportunity over the next decade. What’s your outlook for the AI industry and where is it heading right now?
I would say the future is here, but it’s unevenly distributed. We are still at a preparation stage for this AI wave, and it’s still early. For example, data centers are not ready — there are massive buildouts underway. The data isn’t ready, and enterprises aren’t ready to prepare their data for AI to digest and transform into actual business operations.
In the private sector, there’s certainly hype. Some coding agent companies have already shown signs of product-market fit, but many lack true business viability. They’re operating at negative 200–300% gross margins, subsidizing model companies like OpenAI while burning investor money. That’s an area we are cautious about.
But in other sectors, valuations are still attractive. We are still in a preparatory stage. AI companies that not only have the AI capability but also understand specific industries — and can implement the technology within enterprises — will be valued much more highly than some of the startups we see right now.
Derek, the performance of AI companies has been very strong recently. Should investors be concerned about the AI market?
A lot of people say AI is a bubble, or just a theme cycle. But we see AI as structural growth compared to short-term themes. In the past, there have been several structural growth eras — the PC era, then the internet, then cloud and SaaS. Just look at the past 10 years: cloud and SaaS transformed the enterprise software industry and created decade-long performance runs. Those companies led the benchmark performance.
We see AI as the next wave of technological breakthrough, a structural growth story that can last decades. We’re only in about the third year — think about how people only started to widely recognize ChatGPT about three years ago. It’s still early, and the industry is so dynamic. That’s why investors and the global investment community should be aware of this transformational structural growth.
So let’s talk about two things now: the risks and returns of investing in AI companies, and also the integration of both public and private. Max, let’s start with you. How do you assess the risk and return?
For some of the private AI companies, especially the model companies, we’re seeing some of the strongest growth we’ve ever encountered. These companies form the infrastructure of the automated society we’re building, and they will certainly deliver investment returns for investors. They’re the beachhead of this revolution.
If you look at OpenAI today, it already carries a $300 billion valuation — larger than some public companies. From a portfolio construction perspective, not including private exposure would underrepresent the AI beta. The competition is still uncertain. It’s too early to say private companies will disrupt public ones, given the advantages public companies have in data, workflow integration, and capital access. But this is a new race, and it’s a fair race — one driven by execution speed. We’ll keep betting on AI winners, whether public or private.
And Derek, the challenges of managing an investment that integrates both public and private companies into one product — how do you get around that?
We’re one of the first firms doing this in the ETF industry, so we get a lot of questions and misunderstandings from investors. Many don’t realize what we’re actually doing. The first challenge is communication and education. No one has previously put private companies like Anthropic or xAI into an ETF. Traditionally, ETFs are seen as simply index-tracking public equities. There are a lot of nuances. First, exposure management: we want to target a dedicated weight in private companies. That means maintaining a strong pipeline to keep adding new names when new money comes in.
Second, fair valuation. Investors need a fair NAV, so we partner with third-party vendors. Private companies don’t trade daily, but many late-stage tech companies have active secondary markets. With the help of vendors, we get access to secondary transaction prices. Combined with data from the last primary round, this gives our fair value committee the information needed to value private companies accurately, even though they’re not public.
With these nuances, we’ve created a solution that brings private exposure into ETFs, allowing any investor access to these companies.
Max, within the AI sector, are there specific industries at the vanguard of innovation, and how does a portfolio manager aim to capitalize on them?
Right now, a lot of attention is on model companies because, as the saying goes, learning is always faster than feature engineering. Models can overwhelm startups in almost any vertical, given enough data. That means model investment is not finished yet. We already have access to Anthropic and xAI, and we’re considering adding more model companies, including sovereign AI players in different regions, to capture the momentum.
We’re also at the stage of scaling data — whether through reinforcement learning environments, synthetic data, or deploying AI models into real-world workflows. We want to focus on service and growth companies across different verticals — essentially, finding the AI-native Palantir that can systematically embed itself into enterprise workflows. We strongly believe the eventual “killer app” will be robotics, so we’ll increasingly focus on robotics and foundation model companies down the road.
Derek, things are moving at quite a pace in artificial intelligence. How does an investor stay on top of emerging technologies and the latest trends?
The most important thing is: investors should use the technology. If you don’t use it, you don’t realize how disruptive it is. Many people already use ChatGPT to write emails or do research, but there are far more tools available.
Coding is the most important AI application right now. Even people who don’t know how to code can start learning through what’s called “white coding.” Tools like Anthropic’s Claude and other AI coding technologies make it possible for anyone to build apps. That’s a game changer for investors, who can create their own AI agents to automate workflows in their investment process.
From there, you can build systems that feed you daily updates on what’s happening in AI. And of course, investors can follow KraneShares.com to get the latest information. We’ve already published research articles on how to invest in AI in both the public and private spaces. Partnering with Etna Capital, we’ll continue providing white papers and knowledge sharing through our platform.
Derek Yan, CFA, Senior Investment Strategist at KraneShares — thank you so much for joining me.
Thank you.
And Max Chen, Partner and Portfolio Manager at Etna Capital in Hong Kong — thank you for joining me as well.
Thank you.
And thanks to the audience for taking time out of your day to listen to today’s episode. You can see more at KraneShares.com, and you’ll also find their videos and articles on Traders’ Insight at the IBKR Campus. Don’t forget to subscribe wherever you download your podcasts. Bye for now.
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