AI spending can run negative. Qodo’s CEO built an ROI equation to fix it.

The AI Spending Cap

Flush with the proceeds of a $70 million Series B raised earlier this year, you might expect Qodo to spend freely on internal AI. After all, the startup uses artificial intelligence to ensure AI-generated code meets customer quality and governance requirements. An upstart technology company using AI to improve AI outputs is AI-pilled by definition. Instead, the company has limits on AI consumption.

Qodo CEO Itamar Friedman tells The New Stack that his engineers can access $10,000 worth of tokens per month, a cap that he described as "generous." Most Qodo developers never reach it. The ceiling wasn’t enacted to "restrict usage," Friedman says, but instead to drive "visibility and efficiency" at the startup so that it can "scale without runaway costs." Put another way, the cap exists to make somebody answer this question: "Which path of automation or usage will be the best [use] of our money?"

Qodo’s AI footprint is larger than its developer token budget. The startup’s AI infrastructure spend - the cost of running the product for customers rather than the cost of its own engineers using AI - is growing at "roughly 5x year over year," the company tells TNS in an email, reflecting both "increased user adoption" and its agents taking on more, and longer tasks as they mature. Qodo says it is pushing the other direction at the same time, driving down the cost of reviewed pull requests through routing and inference efficiency.

What Qodo Runs On

The company also dogfoods heavily, running its pull requests through Qodo. Friedman said the product powers its entire software development life cycle (SDLC). Around that sits a stack most engineering organizations would recognize: Slack and Notion and their constituent "bots," a centralized knowledge base built to be agent-readable, AI inside Google Workspace, and models from several providers including Google.

Qodo’s own product sits alongside Claude Code and other leading coding assistants rather than replacing them. Claude Code still holds the crown internally, but OpenAI’s Codex has been taking share, with staff "shifting quickly towards Codex." Friedman tracks this two ways. He polls his 130-person staff, spread across offices in several countries, on the tools they prefer, and compares those answers against what the usage data shows.

Friedman reports that Qodo sees "roughly double" the number of PRs "every couple of months" alongside "a decreasing amount of bugs and incidents."

The Bottleneck Moved

More PRs and fewer bugs indicate that Qodo is onto something with its focus on software testing and governance. Its technology helps developers deal with an increasingly common issue: What do with all the code that AI agents generate? Companies that adopt AI coding tools often find that they create more code with machines than their humans can assess. As a result, the SDLC bottleneck simply shifts one step down the process.

"We solved the speed of writing code," Friedman argues, "we didn’t solve the velocity of creating software." The difference between accelerating one part of a task and its entire arc is the difference between AI hype and AI ROI. The software development example shows that when we consider AI costs and benefits, we need to think broadly. If we focus too much on a single metric, we might spend our entire budget on Claude Code credits while shipping no more software than before. Alongside a massive bill.

The AI ROI Equation

Friedman recommends an equation-based approach. The Qodo perspective on AI ROI is similar to a popular equation for happiness: Personal joy is the distance between your expectations and reality. The greater the expectations, the harder it is to be happy. The lower the expectations, the greater the chance of being content. This can be expressed as either simple subtraction or as a ratio:

Reality / expectations = Happiness, where larger results indicate greater joy

Take the same mathematical approach to AI ROI, per Friedman: Compare the positives against the negatives, add up all the good, and set it over all the bad.

AI benefits / AI costs = AI ROI, where larger results indicate greater return

Friedman found the shape of the equation in The Phoenix Project, the 2013 DevOps novel that contrasts types of software development work and sorts them into good and bad buckets. Plug those terms in:

(Features + Infrastructure) / (Incidents + Bugs) = Software development velocity

Now, those two numbers from earlier. Qodo has seen more PRs and fewer bugs thanks to AI. In DevOps terms, it’s shipping more and fixing less, so the equation returns a larger, better result. Feed the same terms into the AI ROI version, and it produces more benefits over fewer costs, and a larger final calculation.

The fraction is not a thought experiment at Qodo. It’s the shape of what the company says is already happening to it. Terms that have nothing to do with software development work too. Qodo runs AI inside Google Workspace, Slack, and Notion, and those benefits and costs go into the same calculation.

The Qodo approach to measuring total AI ROI is less specific than The Phoenix Project’s DevOps equation, but the difference is acceptable. Friedman argues that you have to start somewhere: "I know [the equation is] a simplification," the CEO tells TNS. "But what you can’t measure, you can’t improve." His argument is that imperfect beats absent. "Don’t think about it too much," he says. "Try to put any number [in the AI ROI equation] and start tracking." Being told not to overthink an equation is a great soundbite, but the benefit is real: A rough calculation on paper beats holding the same information in your head without form. In this case, the journey is a large part of the destination. Friedman reckons that startups should pick no more than six or eight terms for their own calculations. That’s an afternoon’s work. A start on what will prove to be an ongoing exercise.

Negative ROI

The fraction runs backward, too. Recall Friedman’s point about a company writing more code faster but not accelerating its software development speed. Stuff those terms in:

(Faster code generation + other AI benefits) / (Slower code review and approval + agentic coding costs + other AI costs) = Smaller AI ROI

That’s how a company spends a king’s ransom on AI credits and winds up nowhere or nonexistent. Which is not hypothetical at Qodo either. AI doesn’t excel everywhere, and Friedman named email automation as an example. The company went all in on automating it, then pulled back, "mov[ing] from AI automation to AI enhancement" after discovering that AI struggled to match writing tone and intelligently extract tasks from messages.

The retreat is the interesting part: Qodo’s stated approach to any task is to "go all in on complete automation," and then "take a step back to human judgment." The CEO says that automation falls short today in two areas:

• When human judgment is required
• When context is missing

The second cuts across everything from software development to personal productivity to answering customer questions. Without timely context, what can AI do other than filibuster?

Qodo’s service helps answer the context issue for software development, but collecting a company’s data and making it accessible, timely, and well-governed for general agentic usage is a massive undertaking, and one that a host of startups want to help solve. If they can, everyone’s AI ROI math should improve.

No Mandate, High Expectations

Qodo doesn’t require its staff to use AI. As Friedman puts it, you won’t get fired simply because you’re "not AI all the way," or "eating AI for breakfast." The company expects staff to complete their work as efficiently as possible and leaves the method to them. Employees make their own decisions and execute their own work. If they start to fall behind on assigned tasks, they’re expected to reach for automation. It’s a balanced approach with high expectations: An employee who isn’t as efficient as they could be with AI could find themselves at risk.

Friedman has been on the unpopular side of an AI argument before. When he was building Qodo in 2023 and talking up agents, "agents" was a "bad word," dismissed as little more than "fluff." Three years and a $70 million Series B, the bet has paid off. His advice to founders starting now looks like his past. Predict "what’s going to happen two years from now," he says, then solve for it immediately, because whatever looks like two years tends to arrive inside of twelve months. The future "is coming faster" than you think, he says. It’s a lot to ask of anyone working from an incomplete picture. Predicting the future is hard, he admits, "but you have to