How To Trade The AI Productivity Boom | Weekly Roundup
Friday, 29 May 2026 · 4 min read · Listen to the episode ↗
This episode centers on how to position around the AI productivity boom while navigating a macro environment defined by persistent inflation, a dovish Fed, and deteriorating consumer conditions. Speakers argue that AI is driving a real productivity expansion but is widening wealth inequality and straining the social contract, making redistributive political outcomes increasingly likely.
The macro backdrop combines persistent inflation, a Fed characterized as unbelievably dovish, and structural forces that make rate hikes unlikely despite Taylor rule models suggesting policy is already too loose. Inflation has been above the Fed's target for more than 60 months, yet roughly one trillion dollars in annual interest expense and large debt rollover needs push policy toward fiscal accommodation rather than tightening. Negative real rates are expected to persist indefinitely as the implicit strategy is to grow out of the debt problem. Balance sheet repo operations supporting the long end are described as having been more stimulative in many cases than rate cuts, and suppressing oil prices, yields, and currencies raises the risk of an energy price shock feeding into core inflation. US oil inventories are now below the five-year seasonal range, which speakers argue gives Iran an incentive to drag out geopolitical tensions because time works against US inventory buffers.
Consumer conditions are deteriorating in ways that constrain policy options. Personal disposable incomes have turned negative for the first time since 2022, forcing consumers to tap savings, a condition that historically coincides with growth shocks or recessions. Large tax refunds from the Big Beautiful Bill temporarily supported spending but are described as running out within a couple of months. Without trade deal resolution, speakers predict stagflationary conditions emerge in that same window. One speaker predicts Trump will cut a trade deal worse than Obama-era agreements because he cannot sustain another months-long conflict given current gas prices, consumer health, and the US being roughly four months from midterm elections.
Equity market positioning is described as dangerously stretched. The VIX fell from approximately 40 to sub-16, semiconductor call positioning is in the 98th percentile with no downside protection being bought, and the S&P one-month 25-delta implied vol spread for puts relative to calls is in the fourth percentile, meaning put protection is extremely cheap. Single stock implied volatility is high relative to index volatility because momentum and retail investors are buying calls on individual names while quant funds short index volatility via the VIX. Speakers warn this dynamic can unwind sharply, as seen in the carry trade unwind the prior year. Looking ahead, speakers anticipate a sectoral rotation involving deleveraging of frothy sectors and potential short squeezes elsewhere, with semiconductors passing the baton to other sectors without necessarily requiring the broader market to fall. Current low credit spreads make a volmageddon-type event unlikely.
The AI productivity boom is described as real and contributing to a massive productivity expansion, but speakers argue it is breaking the social contract during a transitional period. Policies propping up stock markets come at the direct expense of small businesses and lower and middle income people, and the K-shaped economy is characterized as a deliberate policy choice to benefit asset owners. Speakers predict that prolonged suppression of volatility combined with wealth inequality will eventually transmit to the social contract, making redistributive political outcomes more likely than laissez-faire conservative government. Younger generations are being forced into leveraged products and options because conventional saving does not keep pace with centralized wealth accumulation. Mike Green's argument is cited that the shift from defined benefit to defined contribution pension plans has made markets more synthetic and flow-driven, removing the duration hedging and liability matching that previously kept markets more rational.
On crypto, speakers argue that strong nominal GDP growth driven by AI productivity creates a clear bid for productive assets that challenges crypto's positioning. Productive crypto applications such as stablecoin adoption show real adoption but no value capture in related tokens, and Ethereum network usage is described as not correlating to the price of the ETH token. David Hoffman from Bankless is cited as having recently sold his Ethereum, stating he is bullish on the network but considers the asset itself uninvestable. Entities described as DATs bought 15 billion dollars of Bitcoin and ETH this year yet both assets are down significantly. Capital flowing into AI infrastructure is described as pulling investment away from crypto, analogous to Silicon Valley tech growth pulling capital from gold miners in 2012.
On thematic investing, speakers outline Mark Hart's framework using access, awareness, patina, total addressable market, and use as collateral as key indicators, with concentric circles of adoption tracking when new pools of investors enter a theme as access expands. The Bitcoin ETF launch is cited as a concrete access-change event, while regulatory restrictions that reduce access are treated as exit signals. Fundamental investors focused on earnings changes tend to miss these access signals entirely. The AI data center infrastructure trend is expected to remain significant for the next two years. One speaker held Nebius from approximately April to May 2025 before selling for small gains ahead of what became a large winning move, and reflects that over-trading AI memory positions reduced potential gains, though early risk management avoided severe drawdowns during sharp pullbacks. Mentally separating short-term swing trades from multi-year thematic positions is described as very difficult, with the practical solution being separate trackers and sometimes separate accounts for each timeframe.
This summary was generated from the episode transcript and can contain mistakes.