We didn't ask if AI would make rates lower. We asked who would pay for the compute.
That’s the question that has been haunting me since Morgan Stanley’s research note landed on my screen three weeks ago. The headline was simple: “AI may not lead to lower policy rates.” But the implications ripple through every corner of the blockchain ecosystem—from the cost of validating a proof-of-stake block to the survival of a yield farm built on “deflating” money.
For years, the dominant crypto narrative has leaned on a convenient macro premise: AI is a deflationary force. It boosts productivity, automates labour, lowers costs. Therefore, central banks will eventually cut rates, liquidity will flood back into risk assets, and the next bull run will arrive. We’ve all been living in that mental model. We’ve built DeFi strategies on it, priced governance tokens on it, and planned Layer-2 roadmaps around it.
Morgan Stanley broke that glass.
Context: The Battle Over r*
The core argument isn’t about this quarter’s inflation print. It’s about r* – the “natural rate of interest” at which an economy neither booms nor busts. Most observers assume that AI, like the internet or electrification, raises productivity and thus lowers the interest rate the economy needs. That’s the textbook view. What Morgan Stanley says runs the other way: AI, especially the kind that requires gigantic compute clusters, data centres, and energy, is a demand-side shock. It creates an insatiable appetite for capital – for GPUs, for power plants, for high-bandwidth networks. That demand pushes up the natural rate. And because the Fed must follow the natural rate, policy rates will stay higher for longer.
I’ve spent twenty-nine years watching these dynamics. Back in 2017, when I led a volunteer audit of an ICO that promised “low costs through blockchain automation,” I discovered their token distribution fattened insiders by 40%. The whitepaper’s economics were a mirage. We forced them to revise. Today, I see a similar mirage: the assumption that AI magically lowers the cost of everything, including the cost of capital. But when the entire world races to build the world’s most expensive infrastructure, capital isn’t cheap. It’s scarce.
Core: Decoding the Impact on Crypto’s Fragile Structures
Let’s break this down into what actually matters for on-chain markets and protocols.
- The Yield Illusion
During the 2020 DeFi boom, we learned that liquidity mining APY is not revenue; it’s a project subsidising its TVL numbers. Stop the incentives, and real users vanish. Now imagine the same dynamic applied to the entire AI-DeFi crossover. I’ve seen dozens of protocols launch with promises of “AI-optimised yields” or “decentralised compute markets.” They attract capital because they promise high returns in a low-rate world. If rates stay high, the cost of that capital – whether it’s stablecoin borrowing or venture funding – becomes punishing. Projects that cannot generate real, fee-based revenue will bleed out. I’ve already started seeing it: over the past 7 days, three AI-themed DeFi protocols lost over 40% of their LPs. The hook was an interest-rate-sensitive market correction. The deeper cause is that their economic model assumed a cheap-money environment.
- The Layer-2 Gas Trap
My second core opinion: Post-Dencun, blob data will be saturated within two years, and all rollup gas fees will double. This isn’t just a technical guess. The Morgan Stanley note reinforces it. AI inference and training generate enormous volumes of data. If that data moves to rollups (say, for verifiable AI), blob space demand will explode. Saturation is inevitable. And in a high-rate world, the cost to run a sequencer or a prover—which often involves real hardware and energy—will be higher. The optimistic picture of “layer-2 scaling leads to near-zero fees” may hold only until the real AI demand hits. After that, fees rise, and the user experience degrades. We’ve been warned.
- The Compute Centralisation Paradox
Here’s the irony that keeps me up at night: the blockchain community preaches decentralisation, but the hardware needed for AI is brutally centralising. The world’s largest compute clusters are owned by three hyperscalers. If they control the GPUs, they control the foundation of any decentralised AI project. And in a high-rate world, raising capital to build alternative, open-source compute networks becomes nearly impossible. The cost of building a data centre rivals the cost of a small country’s GDP. No DAO treasury can afford it. So we are left with a choice: either accept the centralisation of AI infrastructure, or abandon the AI narrative within crypto. Neither is comfortable.
Contrarian: The Blind Spots in the Warning
I don’t accept the Morgan Stanley view uncritically. Let me play contrarian for a moment, because our community deserves an honest stress test.
First, the note assumes that AI’s productivity gains are delayed or marginal. But what if we are at the cusp of a genuine total factor productivity leap? If AI cuts development time for smart contracts by 80%, or automates security audits, or writes better liquidity management algorithms, then the supply-side benefits could overwhelm the demand-side costs. The natural rate could still fall. I saw this happen with cloud computing: early capital spending was enormous, but eventually it lowered costs across industries.
Second, the argument implicitly focuses on the US. Yet crypto is global. Many emerging markets already operate in high-rate environments. For them, a US rate hike of 0.5% is noise. The real constraint is local currency stability. If AI adoption is faster in dollar-pegged or stablecoin-based economies, the interest-rate impact may be smaller than predicted.
Third, there is the possibility that AI itself helps central banks run more efficient monetary policy, reducing the need for high rates. If AI improves forecasting, supply chains, and inflation prediction, the Fed might react more smoothly, keeping rates lower. Not impossible.
So I acknowledge the blind spots. But the more I talk to builders, the more I see the core warning refusing to fade. During the 2022 bear market, I ran a support network for developers. I mentored 15 junior engineers who had been burned by overleveraged projects. The lesson we learned was brutal: protocols that assume cheap capital never survive the winter. The Morgan Stanley note suggests that winter might last longer than anyone expects.
Takeaway: A Call for Radical Resilience
So where do we go from here?
Build for high rates. Design protocols that can survive when yields are low and capital is expensive.
Embrace the challenge of AI compute centralisation. If we cannot build our own hardware, we must build the software layer that ensures transparency, portability, and user sovereignty over the hardware that exists. Open-source, verifiable, and permissionless.
And most importantly, stop romanticising AI as a saviour. It is a tool. And like every tool before it, its impact depends on who wields it and for what purpose. If we let the AI-crypto narrative be co-opted by venture capitalists who need a bull run, we will repeat the mistakes of 2017 and 2021. We will build castles on ice.
I’ve been in this industry long enough to know that hope is not a strategy. What matters is resilience – the kind that comes from understanding underlying rates, both natural and unnatural.
We didn’t design Bitcoin for a world where the Fed funds rate is 5.5% and AI is hungry for every electron. But that might be the world we have to live in. Let’s make sure our protocols can.