Can You Trust AI-Generated Solana Code? Start With the Spec.

In October 2026, Jump Crypto changed the rules on its Firedancer bug bounty. To be eligible for a payout now, a researcher must permanently burn 1 SOL per report, non-refundable even for duplicates, because an up-to-$1 million audit competition this spring drew 809 reports from 162 researchers. Nearly half of the valid submissions were duplicates, and Jump believes nearly every submission was either discovered or written by an AI agent. As Jump's Ishan Bhatt put it:

“AI has made finding bugs cheap, but judging them is still expensive.”

That one sentence captures the state of AI on Solana right now: The cost of generating code, tests, and bug reports has collapsed substantially, while the cost of knowing whether any of it is correct, i.e., the Solana domain skill, has not moved much. Welcome to the world of testing Solana programs vibe-coded from LLMs, where everything that matters now will need to be created in that vacuum between real domain skill and the plethora of costly tests on vibe-coded programs.

We have been circling this point for a while. Earlier this year, we argued that Solana institutions aren't short on AI tools; they are short on engineers who understand the model underneath. This is the follow-up, and it is more concrete: here is exactly how AI-assisted Solana development fails, why the SVM makes it fail harder than anywhere else, and the discipline that closes the gap. It is also, not coincidentally, what we are teaching now.

Why AI-Generated Solana Code Looks Right and Still Fails

The uncomfortable truth, from what we see across the ecosystem, is that much of the Solana code being written today is AI-generated and reviewed by someone who could not have written it themselves. This is what happens when a tool gets good enough to produce output faster than a human can develop the judgment and domain skills that typically take years of experience to build, to evaluate it.

To dig into the problem a bit more, a large language model learned to program from the open internet, which means it reasons like a general-purpose Rust programmer or worse. It is fluent, but it lacks the nuances of the Solana runtime because the runtime is a small, specialized, and comparatively under-documented corner of the internet that models have seen far less of than general Rust.

So the model can and will be confidently wrong in a very specific way: It ignores the compute budget until a transaction fails on-chain, and it can assume state it never validated. The issue is that none of this normally shows up as the AI planned because of missing context, so it behaves differently the moment it hits the actual execution environment. Then throw testing into the mix, and you multiply the false positives and negatives. It really can become a big, costly validation mess.

This is the real risk right now, and it can easily get lost in the debate over whether AI is overhyped because that really doesn’t matter. There is tech underneath that is already changing how people build on Solana. The danger is that it is changing how people build faster than it is teaching them what they are building on, and a verification layer that depends on humans deeply understanding the Solana runtime and transaction lifecycle, Anchor mechanics, etc., is being quietly removed at the exact moment we are seeing code volume explode.

What Should a Solana MCP Server Carry?

There is now a long and growing catalog of Solana MCP servers, editor rule sets, and agent kits. Each one is someone's attempt to connect an AI to the chain. The result is fragmentation and another surface where a developer loses track of what the agent actually knows. The risk is very clear: the Solana MCP must maintain a balance of what is important but also have a pipeline of connectors that keeps developers coming back to the tool.

An MCP is a connector, a pipe between the model and some source of context or action. Adding more pipes does not make the model understand Solana better. It just creates more disconnection in some regards and splits the developer's attention across tools that each know a little, none of which is enough.

What might actually move retention on the tool isn't the number of connectors, but the depth of knowledge that flows through each one, and whether people stay anchored to it instead of scattering across 5 to 10 others. One deep MCP with well-curated context gives better retention and a better developer experience than a proliferation of disparate, shallower ones.

The base Solana MCP has to go in the opposite direction: fewer tools, but deeper, wider connectors into the parts of Solana that matter and that the model doesn't already know. For example, Turbin3 suggests starting with an MCP connection that tracks the entire transaction lifecycle at a low level: the account model, execution semantics, and the LiteSVM testing environment, which several leading devs in the space cited as a core weakness of the current MCP in our research.

The goal is tool retention, not tool count. A developer or an agent should reach for one connector that reliably carries real Solana context, not five or ten that each carry a fragment and collectively carry confusion.

How to Test AI-Generated Solana Code: Write the Spec First

Putting deep Solana context into the pipe is a precondition for meaningful tests, and meaningful tests are among the cheapest and most reliable ways to verify code an AI wrote.

The naive way people use AI and tests together is backward: they let the agent write the code, then ask it to write the tests. Worse than tests passing are the worthless false positives and negatives they produce in disproportionate volume, because they encode the same assumptions as the code, including the wrong ones, and they like to please, i.e., finding errors when testing as its structure. However, if you flip this on its head and write the spec first and turn it into tests, then let the agent write code until the tests turn green, you get the one honest signal of whether the output is correct.

This matters more on Solana because AI code here can be confidently wrong about runtime specifics that only a runtime-aware test can catch, and that is what the deep-context pipe enables: if the agent is grounded in the execution model, the tests check the things that actually break, like account constraints, CPI behavior, compute limits, PDA derivation, and state transitions.

The Solana Testing Stack: LiteSVM, Mollusk and Surfpool

Solana already has the testing stack to make this cheap. LiteSVM runs tests in an in-process virtual machine in tens of milliseconds instead of the tens of seconds a local validator takes, and in Anchor 1.0 it is the default test template for anchor init, while Surfpool is the default backend for anchor test. Together they form a clear pyramid: Mollusk for isolated instruction checks, LiteSVM for fast unit tests, and Surfpool for integration against realistic state. The pieces exist. What's missing is the discipline to write the spec and its tests first, and the context to make them real. Put those two together, and testing on Solana gets cheaper, faster, and far more trustworthy in an AI-driven workflow. That is the real value, and it is real regardless of what any token does.

What Solana Builders Say About Using AI

We didn't want this to be another think piece written from the outside, so we went to the source and talked to some of the strongest builders in the Solana ecosystem about how AI is changing their day-to-day.

The through-line, stated plainly, is that the best teams are not the ones with the most AI tools. They are the ones with the discipline and the fundamentals to use AI grounded in a few simple mechanics, not a mess of many different MCPs and skills. That is a learnable skill that almost no one is teaching.

The Jump story is the same lesson at ecosystem scale. When generation gets free, both the bottleneck and the values shift to verification. The individual developer faces the same economics in miniature every time an agent hands them code they cannot fully check.

Turbin3’s Advanced Cohort for Building Solana Programs With AI

Turbin3 exists to build the Solana workforce, and the workforce the next few years actually need is not people who can prompt an agent. It is people who can direct one and then verify it, which takes an understanding of the runtime the agent does not have. That is the entire premise of our new advanced cohort: a six-week intensive program that teaches experienced Solana engineers a disciplined, verified workflow for building with AI. It is the first Turbin3 cohort we are opening to external applicants, aimed at engineers who already have the Anchor fundamentals and are tired of being fooled by their own tools.

The whole program runs on one loop, repeated until it is reflex: specify, build, verify, judge. You write the spec and the acceptance checks first. The agent builds against them. You verify the result with adversarial tests, fuzzing, and evidence-based review. Then you make the call, ship, fix, or escalate. It runs on a single stack on purpose, not a drawer of tools: Claude Code with a real CLAUDE.md, process skills, the Solana Developer MCP, LiteSVM, Surfpool, and GitHub, with the Solana transaction lifecycle threaded through all four weeks as the spine. Week one starts where the models are weakest, reading the LiteSVM pipeline and forcing a transaction to fail at each stage, so students learn the runtime by breaking it.

The part that ties this back to everything above is what the cohort actually produces. The assignments extend the Turbin3 Solana MCP toward the SVM-native, transaction-lifecycle depth the base connector is missing, which is the wider pipe this whole piece has been arguing for, built by the people learning to use it. Graduates also produce a reusable open-source Solana harness they can adapt to their own domain, and the cohort assembles a vetted list of Solana skills and MCP servers for teams to converge on instead of drowning in the catalog.

The capstones are evidence-based, not demos: a pull request to a public Solana repo, a domain harness, an audit of an agent-built program, and a scoped SVM component checked against LiteSVM, presented on Demo Day to a panel of working practitioners. We measure the thing that actually matters, the improvement in defect detection from each engineer's unaided baseline to where they finish, and the harness, the vetted list, and the materials ship open-source so the rest of the ecosystem can build on them.

Earlier this year we wrote that AI does not go deep enough on the SVM, and that your team has to. That post named the gap. This cohort is how you close it, and how the engineers who close it become the ones every serious protocol wants to hire. The first run opens applications now and begins in January 2027.

Why This Holds in Any Market

The value we are describing here doesn’t sit on top of the hype cycle that many people debate until they’re blue in the face. No one really knows. But what we do know is that the technology is real, and in the context of Solana, a wider pipe of real context into the Solana MCP, retention built around that one source instead of scattered across many, and testing that is cheaper and more trustworthy in an AI workflow are worth exactly the same in a bull market, a bear market, or a long grind sideways. They make real code more correct and real engineers more valuable, and that compounds no matter what the chart does.

The hype will fade. What remains afterward is whoever learned to build real value while everyone else argued over the top. That is the bet we are making at Turbin3, and it is the only one worth making.

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