The AI Product That Wins Has the Best Data, Not the Best Algorithm

01 /The Concept

Most developers already write code with AI platforms like OpenAI Codex, Google Antigravity, or Claude Code, and reach for ChatGPT or Gemini when they hit technical questions. These tools work fine for well-trodden problems.

But some problems come with an asterisk – the ones where you need not just a working solution, but the best known solution right now. General-purpose AI platforms often fall short here: their knowledge has a cutoff date, and cutting-edge approaches can get blurred against older, less effective ones in their training data.

When that happens, developers still resort to the same workflow they always have: manually hunting down recent papers and articles, evaluating new approaches, running benchmarks, iterating – inching toward the state-of-the-art answer. The bottleneck, as ever, is human time.

Aemon is built to eliminate that bottleneck. It's an AI software engineer purpose-built for finding and implementing the most current, most effective solutions to hard technical problems.

The need for this kind of work usually surfaces in two situations: after shipping a first version that just needed to work, or when refactoring old code that's overdue for improvement.

The workflow starts by connecting Aemon to the existing codebase, describing the current bottlenecks or the metrics that need to improve, and providing test datasets. Aemon then analyzes the architecture, identifies the algorithms already in use, and searches for papers describing more effective approaches to the same problem.

From there, Aemon autonomously iterates through candidate algorithms, combines them, and evaluates their performance against the provided test sets – discarding those that underperform.

Progress is visible in real time on graphs tracking accuracy, speed, and any other target metrics across iterations.

Crucially, a human engineer can step in at any point during the process – adding constraints on algorithm selection, reordering metric priorities, or sharpening the objective – based on what is or isn't working.

The output is what the research community calls State-of-the-Art (SOTA): the best currently known solution to the defined problem – fully implemented, tested, and accompanied by all the performance metrics achieved in the process.

Aemon is currently going through Y Combinator acceleration, and published details about its platform for the first time just yesterday.

02 /Why It Matters

Interestingly, about three hours before Aemon's announcement, another YC alumnus – Wizwand – published its own launch post. Wizwand covers narrower ground: it finds recent papers describing SOTA algorithms in AI and machine learning, but stops short of actually implementing or benchmarking them.

Wizwand's coverage is impressive, surfacing papers published literally hours earlier across a wide range of topics. But Aemon's ability to not just find better algorithms but test them against real code is where the real value gap opens up.

Two startups digging in the same direction on the same day is a signal worth paying attention to – especially when both are YC-backed, and YC is notoriously selective.

The underlying problem is real: AI and machine learning is a fast-moving field where the state of the art shifts constantly. Neither humans nor general-purpose AI coding tools can reliably track and evaluate the frontier. And new algorithms, even promising ones, often carry edge cases and failure modes that only emerge during practical testing – which is exactly what Aemon does.

The same "keep up with the frontier" problem shows up in other fast-moving fields. Medicine is the clearest example. A generation ago, an experienced physician could maintain an up-to-date mental model of their specialty by reading the literature. Today that's not just practically impossible – it's theoretically impossible. Specialized medical knowledge now doubles roughly every 73 days. No individual can process that volume while also seeing patients.

That gap is what OpenEvidence ([related review](/review/v-kazhdoj-teme-pojavitsja-svoj-analog-chatgpt)) was built to close – a specialized "ChatGPT for physicians" that answers clinical questions sourced from verified, current professional medical literature. OpenEvidence passed the US medical licensing exam with 100% accuracy, outperforming leading general-purpose AI at the time. Within a year, 40% of all US physicians were visiting the platform daily.

The result was a sustained funding barrage: $75M in early 2025, $210M that summer, $200M in the fall, and another $250M the following January – bringing the valuation to $12 billion.

The same pattern holds in less obvious domains. XOi ([related review](/review/tema-v-kotoroj-mozhno-i-horosho-zarabatyvat-i-horosho-prodatsja)) raised $230M in a single round for an app that lets appliance repair technicians point their phone camera at a piece of equipment and instantly receive step-by-step diagnostic and repair instructions – powered by a continuously updated database of professional technical knowledge. Half that funding went toward acquiring a competitor, Specifx, whose database was absorbed into XOi's. The data itself had a price tag.

03 /Opportunities

The quality of any AI product is determined less by its algorithms than by the quality, volume, and freshness of the data it uses.

OpenEvidence uses fairly standard RAG (Retrieval-Augmented Generation) techniques and established medical classifiers. What makes it exceptional is that its AI engine was trained exclusively on curated professional medical data – that specificity is the entire moat.

The implication: as AI platforms proliferate across every domain, the value of high-quality, domain-specific data keeps rising. AI's rise has reminded everyone that "data is the new oil" – and that's not a cliché right now, it's a live investment thesis.

The practical direction this opens up: building domain-specific databases in areas where authoritative, current knowledge is hard to aggregate. At minimum, such databases can be licensed or used to drive traffic. The more ambitious path – and the one with the highest ceiling – is to layer proprietary search and reasoning on top of the database and package it as a specialized professional product, in the mold of OpenEvidence, XOi, or Aemon.

So: "the specialized ChatGPT for which type of professional" could you build – and would you actually want to?