Episode summary
Inflation Well Above Target. A Market All In on AI. What Does the Market Top Checklist Say Now?
Excess Returns
· · Guests: Tian Yang
Summary
Tian Yang argues that investors should focus less on static macro data and more on causal, adaptive leading indicators that capture turning points before they show up in GDP or inflation prints. He says traditional recession tools have become less reliable after 2020 because the economy is operating under different mechanisms, including fiscal dominance, sovereignty competition, and a more interventionist policy backdrop. Variant Perception’s framework combines growth, inflation, policy and liquidity into a Macro Risk Indicator that updates as relationships change over time.
On the current backdrop, Tian says the signal is still broadly risk-on. He points to resilient growth, broadly supportive liquidity, and policy that is only mildly restrictive in some places, while arguing the Fed should not need to hike unless the data worsen materially. He thinks headline inflation can stay above target because of energy and other supply shocks without causing a persistent core inflation problem, since housing, wage growth and small-business pricing power remain subdued.
A major theme is AI capex, which he sees as a real source of economic support because corporate spending and low household savings create income for others. He thinks the first phase of AI gains was concentrated in hardware and hyperscalers, but the next phase could broaden to adopters and complementary assets. He also says market-top signals are visible only as an amber warning, not a full-blown top, because liquidity has not tightened enough and the economy has not yet rolled over.
Key takeaways
- Tian Yang says Variant Perception’s Macro Risk Indicator still points to a risk-on regime because growth is resilient, liquidity is supportive and policy is only mildly restrictive.
- He argues the Fed is unlikely to hike unless energy-driven inflation persists, because core inflation is still muted in housing, wages and small-business surveys.
- Tian Yang sees AI capex as an economic tailwind today, but says the real risk comes if hyperscalers and corporates suddenly cut spending and start saving more.
- He thinks AI profits are likely to broaden beyond semis and hyperscalers toward adopters and complementary assets as the technology diffuses.
- He says market-top conditions are not fully in place yet: behavioral excess exists, but liquidity has not tightened enough and the economy has not slowed decisively.
Notable quotes
“data is easier to access and more available than ever before. The key now is how creatively you use these inputs and most importantly what you choose to leave out”
“the idea is to go through different parts of the economy and look for these potential shifts”
“The time to worry is precisely when these hyperscalers of people suddenly dial back their capex, right”
“we don't think it's been a big problem”
How the episode opens
Welcome to Excess Returns. I'm Jack Forehand and I'm excited to be joined today by Chan Yang. Chan's the head of research at Varied Perception and also the portfolio manager of the VPX ETF and we're going to talk a little background today.
So, uh Chan, thank you for joining us. Thanks for having me. Looking forward to it.
You guys do some awesome work. Um and you blend quantitative and qualitative frameworks in which is exactly what I like to do as well. So, I'm really interested to dig into we're going to dig into the economy in general, but we're also going to dig into the framework behind it and how you're getting to the conclusions you guys are getting to, but I want to start with a quote.
You have a really great quote here that I think summarize what you guys do, but I think is really, really relevant for what's going on in the market today. And the quote is data is easier to access and more available than ever before. The key now is how creatively you use these inputs and most importantly what you choose to leave out.
And that that last part was really important, I think, to me in the world of noise is what you choose to leave out. So could you talk about what you mean by that scene?
Uh yeah. So I think what we're implicitly trying to say here is that you have to think from first principles. What are like the causal reasons that data is statistically meaningful?
So you know we all want to build models especially in this age of AI. You know pretty soon we'll have like you know superpowered AI to help us build models right? So I think a lot of times though even when you're doing that and processing data you you know it's easy to you know throw things to a black box find something that looks back test really well and want to use it.
From the episode’s captions: roughly the first two minutes, lightly cleaned. Keep watching on YouTube →
Topics
- ai capex
- inflation above target
- fed rate outlook
- market top signals
- portfolio construction
Companies and tickers mentioned
- Variant Perception
- SpaceX
- Anthropic
- OpenAI
Watch the episode
Summary, takeaways and quote selection by Octagon. Quotes are short excerpts; the full conversation is in the episode.
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