The Rise of Intelligence Ownership: a task-trained open source model vs the frontier

Cost vs. quality on catalog integrity

The article in one picture: on the same catalog-review workflow, with the same tools,<br>images, and scorer, our GRPO fine-tune of a 9B open-source model (pink) beats every<br>frontier configuration we tested, at $0.50 per 1,000 listings: 40&times; cheaper than<br>the least expensive frontier setup and ~340&times; cheaper than the most expensive.

Part I

Everyone asked the same question

Since ChatGPT launched in 2022, business leaders have been asking the same question:<br>what can AI do for us? The answer began with low-risk tasks: summarizing<br>documents, drafting emails, producing first drafts that a human would edit.

It quickly moved into higher-value cognitive work, such as software development and<br>content generation, and grew into more ambitious projects, like attempts to build an AI company brain,<br>a system connected to internal knowledge, data, and tools that could coordinate work<br>and eventually operate parts of the business autonomously.

While a lot of time, energy and tokens have been invested in AI adoption, measurable<br>outcomes have barely been achieved at scale. However, some companies<br>embraced being AI-first and saw enormous gains in productivity, revenue, and cost,<br>while others lagged behind or failed to change their organizations enough to reach<br>high ROI.

Recent data from corporate expense management platform Ramp reveals a stark contrast in<br>performance: the top quartile of companies investing in AI saw their revenue more than<br>double between November 2022 and December 2025, while businesses with zero AI<br>expenditure experienced a mere 15% increase.

Top AI adopters outperform across industries

top quartile of AI spenders<br>businesses with zero AI spend

Same three years, same economy: in Ramp's data across its customer base, the heaviest AI<br>adopters more than doubled revenue while businesses spending nothing on AI grew about<br>15% (indexed, Nov 2022 = 100; curve drawn from<br>the reported endpoints). The rest of this article is about what the AI-heavy group<br>actually did.

There are many reasons why AI has done wonders for some companies while others have struggled to see the return on their investment, but research primarily points in five directions.

01<br>Redesign the process, not just the task

Becoming AI-first means rethinking how the work is structured, not dropping a model into a workflow built around people: what gets approved, who reviews what, and which handoffs still need a human. Where the process stays untouched, legacy bottlenecks absorb the productivity gains before they reach the P&L. In McKinsey's 2025 survey of organizations using gen AI, workflow redesign was the attribute most correlated with EBIT impact, and only 21% of them had redesigned any workflow at all.

02<br>Incentivize experimentation

Models, tooling and best practices change weekly, so last quarter's setup is rarely still the right one. That only gets picked up if people are rewarded for trying things and reporting what failed, not just for shipping. Technical teams are the natural place to start, since they see the same problems recur across functions and can tell which of them a model can actually take over.

03<br>Provide tailored business context

Prompt engineering and retrieval can inject business context at call time, but doing it well is its own engineering program: getting to the data, enforcing access controls on what each request may see, building retrieval that surfaces the right evidence, and managing a context window that models use unevenly as it grows.

04<br>Measure usage and impact

Every AI line item eventually meets the CFO question: what did this change, and was it worth it? In most deployments, nobody can answer it: there is no infrastructure to track the model's performance, decision costs, or impact on efficiency, and self-reported time savings are often inaccurate. Without a scored evaluation on your own data, a "vibe evaluation" is the ceiling of what you can claim, and a hard budget to defend.

05<br>Set clear business goals within the AI budget

AI brought a pricing model most companies were not used to. Paying per token instead of per seat makes costs scale with usage, which makes it hard to lay out a cost plan or estimate the capital efficiency gains for internal workloads. Uber went through its annual engineering budget in four months, and Microsoft cancelled most of its Claude licenses to bring costs back under control. Today's prices also understate the problem, since most AI labs are subsidizing token costs to capture market share, and frontier model prices are expected to rise.

In this article we give a detailed overview of the deployment technique the winning group keeps converging on: fine-tuning open-source models with reinforcement learning.<br>We cover how it addresses the last three challenges above, and how it turns knowledge only your organization has (namely data, tools, and...