On July 19, 2024, a security software update from CrowdStrike caused 8.5 million Windows systems worldwide to crash. Flights were grounded, banks went offline, and hospitals were paralyzed, resulting in economic losses exceeding 10 billion dollars. The cause of this disaster was a mere C++ Out-of-bounds Read memory error.
Imagine if this line of code hadn't been written by a human engineer, but generated by an AI predicting the next token based on probability.
As AI begins to take over infrastructure code, the paradox we face is obvious: We have handed the steering wheel to a novice driver whose "vision is sometimes blurry," yet we demand they drive more steadily than a veteran.
To untie this knot, I believe a long-neglected "veteran" is bound to play a crucial role—Formal Verification.
The Limitations of Traditional Testing
Every programmer has written unit tests: Input A, expect B. This works well in traditional software development because human-written logic is relatively linear; covering typical boundary cases is usually enough to ensure system stability.
But AI-generated code breaks this assumption.
Let's take a concrete example: A high-concurrency ticket booking system.
Suppose an AI writes a logic for inventory deduction: "Check if inventory > 0, then deduct 1, otherwise report error."
Human testers run 100,000 tests. Single-threaded? No problem. 100 concurrent requests? No problem. Deploy!
But hidden here is an extremely elusive bug:
If two users click "Buy" within the same millisecond, Request A checks the inventory (sees 1) but hasn't deducted it yet; Request B immediately follows and also checks the inventory (still sees 1).
The result: Two tickets are sold, but the inventory only decreases by 1. This is the disastrous "overselling" scenario.
Routine testing can almost never catch this kind of bug because it relies on extremely coincidental microsecond-level timing.
This is exactly why tech giants mandate the use of formal verification (like TLA+) to design the implementation of core distributed algorithms.
Formal Verification takes a different approach: Instead of "running code" to verify it, it exhausts all possible execution sequences.
Like an observer of parallel universes, it deduces every possibility and then throws a counterexample directly in your face:
"Attention: When Thread A pauses at line 3, and Thread B inserts execution right there, the inventory constraint is violated."
It is not looking for bugs; it is proving the system's logical completeness.
Testing can only prove "bugs exist"; it can never prove "bugs do not exist."
When autonomous agents start controlling databases, calling payment APIs, or even commanding physical devices, a 0.01% error rate is unacceptable. What we need is a 100% mathematical proof.
This is the value of formal verification: It doesn't run test cases, but proves through mathematical logic: "No matter what the input is, the system will never violate safety rule X."
From Ivory Tower to Industrial Battlefield
The core reason why formal verification hasn't been widely adopted for a long time is its extremely high cost.
It used to be the exclusive domain of chip design and aerospace control systems. Take the famous seL4 microkernel project as an example: to mathematically prove that its 10,000 lines of C code had zero bugs, a top research team spent nearly 11 person-years.
Moreover, the scale of verification code is often more than 10 times that of the functional code, and it requires experts proficient in TLA+ or Coq to write it by hand. For internet companies pursuing "small steps and fast running," this "aristocratic technology" was obviously too extravagant.
Interestingly, AI, the very initiator of this "chaos," happens to be the key to lowering verification costs.
In the AI era, the supply and demand logic of formal verification has flipped:
On one hand, the speed at which AI generates code far exceeds the speed of human review. If we cannot automatically verify accuracy, AI's high output will become a high risk. We need an objective mechanism to audit AI-generated code.
On the other hand, the formal specifications that used to be the hardest to write are now being written quite well by AI. Large Language Models (LLMs) excel at translating natural language requirements (e.g., "transfer amount cannot be negative") into rigorous mathematical logic code.
Google DeepMind's AlphaProof is a prime example. It uses AI to generate formal mathematical proofs, solving three extremely difficult algebra and number theory problems. Coupled with AlphaGeometry 2, which solved a geometry problem, AI achieved a silver medal level at the 2024 International Mathematical Olympiad. Proofs that used to take human mathematicians weeks can now be completed by AI in hours.
We are entering a new development paradigm:
Human defines rules (Natural Language) → AI writes code → AI writes proofs → Checker validates proofs.
If the checker passes, it means the code is mathematically safe.
2026: No Longer Just an Ivory Tower Skill
By 2026, I see that formal verification is no longer a "dragon-slaying skill" in the ivory tower, but is becoming a standard for top tech companies and a direction for frontier exploration.
Besides Google DeepMind's continued investment, Microsoft and AWS are also doubling down on integrating formal methods into the core development processes of their cloud infrastructure. Even more exciting is that a batch of emerging forces are committed to democratizing this "aristocratic technology":
- Theorem (backed by YC) is using AI to speed up formal verification by 10,000 times, attempting to make it accessible to every ordinary web developer.
- Infineon's Saarthi is dubbed the "first AI formal verification engineer," capable of autonomously completing the entire process from planning to generating assertions (SystemVerilog Assertions).
- Tools like Qodo (formerly CodiumAI) are embedding verification capabilities directly into IDEs, allowing developers to perform math-level security checks while writing code.
The industry is evolving from "Test-Driven Development" (TDD) to "Proof-Driven Development". We are building not just code, but software systems that are Trustworthy-by-default.
Tying the Red String to the AI Kite
In my previous article "Anchor Engineering: The Last Mile of AI Landing," I compared AI to a kite in the sky. It flies high and free, but the string can break at any moment.
Formal verification is that strongest red string. It is the absolute safety boundary explicitly defined for AI.
Within this boundary, AI can freely unleash its creativity, optimizing algorithms, generating copy, and refactoring architecture. But once its behavior touches the red line—such as attempting to bypass permission checks or generating deadlock code—the formal verification mechanism will intercept it immediately.
This is no longer a simple "test failed," but "mathematically impossible to hold."
In the AI era, formal verification is bound to become infrastructure. It is the absolute line of defense built by human rationality for AI's wild imagination.
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