Every time there’s a lull in crypto prices, a new L1 shows up with a glossy deck and the same promise: faster, cheaper, more scalable. And yet, something didn’t add up for me. The world outside crypto has quietly shifted into an AI-first phase, while inside crypto we’re still arguing about block times and validator counts. Everyone was looking left. I started looking right.
What struck me was not that new L1s keep launching. It’s that they’re launching into an environment that no longer rewards what they’re optimized for. For most of the last cycle, infrastructure itself was the product. If you could build a chain that did 50,000 transactions per second instead of 5,000, that alone justified your existence. But underneath that arms race, the ground has hardened. There is already enough base infrastructure in Web3.
Ethereum still processes over a million transactions a day on average, which sounds modest until you realize that most decentralized applications barely stress it. Layer 2s handle multiples of that, quietly absorbing demand without drama. Solana can already push thousands of transactions per second in real conditions, not benchmarks. When you zoom out, the bottleneck is no longer raw throughput. It’s demand for meaningful computation.
That helps explain why new L1 launches are struggling to find gravity. They aren’t wrong about performance. They’re wrong about relevance.
Meanwhile, AI has changed what “useful” looks like. Modern AI systems don’t just submit transactions. They reason, iterate, call tools, and operate in feedback loops. On the surface, that looks like more activity for blockchains. Underneath, it’s the opposite. AI compresses decision-making. Ten human actions become one machine action. What matters isn’t how many transactions a chain can handle, but whether it can support agents that think, verify, and coordinate autonomously.
Most L1s are not built for that. They are optimized for humans clicking buttons.
Take data availability. Many chains boast about cheap storage, measured in cents per kilobyte. But AI systems don’t care about storing blobs of data forever. They care about accessing the right data at the right moment, proving its integrity, and discarding it when it’s no longer useful. A chain that can store everything cheaply but cannot serve verifiable context quickly is a warehouse, not a workspace.
Or look at execution. On the surface, faster block times sound great. Underneath, AI agents need deterministic execution environments. If an agent is coordinating across ten protocols, a small reorg or inconsistent state update isn’t an annoyance. It’s a failure mode. That risk grows as chains push performance without equally investing in formal verification, simulation environments, and predictable finality. Speed creates fragility if it isn’t earned.
This is where the counterargument usually appears. Some say new L1s are exactly what AI needs: clean-slate designs, parallel execution, native support for advanced cryptography. And they’re not wrong in theory. The problem is timing and proof. AI adoption is already happening on top of existing infrastructure because that’s where liquidity, tooling, and developer intuition live. A new chain doesn’t just need to be better. It needs to be obviously necessary.
Look at where AI is actually touching Web3 today. Most experiments are happening at the application layer. Autonomous trading agents operate on Ethereum and Solana. AI-driven NFT curation runs off-chain and settles on-chain. Even decentralized inference marketplaces tend to anchor settlement to established chains, using them as a trust layer rather than a compute layer. The chain is the court of record, not the brain.
That distinction matters. If the blockchain is a foundation, AI is adding texture on top of it. New L1s keep trying to pour a new foundation where one already exists. What’s missing are buildings.
Understanding that helps explain why developer traction has become so uneven. A new L1 might attract thousands of wallets through incentives, but very few long-lived products. The data tells that story quietly. Daily active addresses spike, then flatten. TVL arrives, then migrates. What remains are bridges and yield loops. That’s not failure. It’s misalignment.
Another layer underneath this is economic. AI changes cost structures. Inference costs drop over time. Coordination costs matter more. If an AI agent can decide, act, and verify in one atomic flow, it doesn’t want to pay multiple base-layer fees across fragmented ecosystems. That pushes value toward chains that already sit at the center of coordination. Network effects harden instead of loosening.
New L1s often respond by promising AI-native features: built-in model hosting, on-chain inference, specialized hardware. Early signs suggest these are more marketing than necessity. On-chain inference, for example, is still orders of magnitude more expensive than off-chain inference with on-chain verification. The risk isn’t that these ideas are bad. It’s that they solve edge cases while ignoring the core workflow.
What would AI readiness actually look like? Not another VM. Not another consensus tweak. It would look like deep integration with identity, provenance, and verification. Chains that make it easy to prove where data came from, how a decision was made, and who is accountable when something goes wrong. Chains that assume most “users” will be machines and design UX, pricing, and security around that reality.
Very few L1 roadmaps read that way.
As I zoom out, this feels less like a crypto problem and more like a recurring tech pattern. When infrastructure matures, progress shifts upward. The internet stopped being about new protocols and started being about applications. Cloud stopped being about servers and started being about workflows. Web3 is quietly crossing that line now, whether the market wants to admit it or not.
If this holds, the next wave of winners won’t be chains that launch with better benchmarks. They’ll be products that prove they can host AI-driven behavior safely, cheaply, and predictably on top of what already exists. The base layers will fade into the background, steady and boring. That’s a compliment.
The sharp observation that sticks with me is this: in an AI era, blockchains don’t win by thinking faster. They win by being trusted places for thinking to land.