Large Language Models (LLMs) are reshaping the field of software performance engineering. They are turning hard, specialized optimization tasks into simple, automated processes. Technologist Dan Luu points to this shift. He pushes back on the idea that LLMs always lead to bloated code. He shows they can unlock new levels of workload-specific optimization. This makes techniques like Just-In-Time (JIT) compilers and custom regex engines affordable for more projects. The result is a future where software is tuned for peak efficiency on specific workloads. This is a big change from the old approach of optimizing for general classes of problems. This guide covers the topic "There's no reason for software to be slow anymore" in detail. It also covers a similar idea from the same source.
There's no reason for software to be slow anymore: How are LLMs Democratizing Performance Engineering?
For a long time, getting peak software performance required deep expertise and major time. The cost limited deep optimization to only the largest or most profitable projects. Now, LLMs are quickly lowering that barrier. Marc Brooker, an AWS Distinguished Engineer, agrees. He says "dynamic custom software, fitted to a particular workload rather than a class of workloads, seems like a very likely outcome" because of these advances. This is similar to the specialized, hardware-specific optimizations seen in old demoscene techniques or libraries like FFTW. Those tools adapt on the fly to hit top performance for Fast Fourier Transforms.
One strong example of this change is the build of JIT compilers. Michael Malis noted that a common saying claims "code was never the hard part." But for complex systems like JIT compilers, the code was the hard part. That is why they have been rare, even though they can speed up software a lot. LLMs have made this much easier, so building a JIT compiler is now more feasible. This is the core idea behind a project led by Michael Malis and Jason Seibel. The project re-implemented PostgreSQL in Rust using AI coding agents. It started in early April 2026. By late June 2026, pgrust passed all 46,000+ tests in PostgreSQL's regression suite. This shows how AI agents can speed up complex infrastructure rewrites that used to take months or years. The project aims to show what PostgreSQL would look like if built in 2026. It features a new vectorized, JIT-compiled executor and a thread-based concurrency model. It hits performance gains of up to 300x for analytical queries in some unpublished test builds.
Beyond compilers, LLMs also do well at writing highly specialized code for narrow tasks. Dan Luu showed this with a regex engine made by an AI agent. The agent looped for a month on improving performance against the rebar regex benchmark suite. At first, it overfit to the benchmark. But when a holdout benchmark was added, the agent learned to apply its fixes more broadly. A native Ahead-Of-Time (AOT) compiled version of FRE showed big performance gains. It ran 2x-4x faster for longer queries when added to ripgrep. Ripgrep is a popular command-line search tool known for its speed and parallel processing. This test involved an agent doing "code surgery" that would be a large task for a human. It shows how fast targeted optimizations can now ship.
What This Means for Developers and Enterprises
The ability of LLMs to write and tune specialized code at high speed has deep effects. For developers, it means less time on the tedious, low-level parts of performance tuning. They can focus more on higher-level problem-solving and design. As Nolan Lawson noted, AI coding agents have made bug detection nearly free. Developers can now "choose how many bugs you want now" and apply the same care to performance issues. This shift helps smaller teams and projects reach performance levels that used to be limited to well-funded teams with dedicated performance experts. Whether you are new to "There's no reason for software to be slow anymore" or already experienced, the sections below have you covered.
For enterprises, this means lower costs and the chance to take on more ambitious software projects. The cost of specialized performance work, which once required rare skill sets, has dropped by "many orders of magnitude" when teams use LLMs. This economic shift makes it worth chasing fixes that used to be too costly. The result is leaner software and better use of hardware. Adding LLMs to performance engineering workflows moves teams from reactive troubleshooting to proactive, autonomous optimization. These systems can study complex system behavior and suggest fixes with speed and accuracy.
What's Next for AI-Driven Optimization
The path forward points to software systems that are not just written, but also tuned and adjusted by AI agents in real-time. This could create self-learning feedback loops. System metrics would be analyzed all the time, and optimization tips would be put in place almost instantly. Systems would evolve with every performance event. Future advances may also see LLMs write mathematically precise specs to drive development. This would ensure correctness from the start and push the engineering role toward problem definition and system design. While projects like pgrust are not yet ready for production, their fast progress shows the huge potential of AI-assisted rewrites of core software. The result will be highly performant, custom-fitted applications.
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