Todd Fisher, CEO and co-founder of CTM, explains why speed is only part of the story.
The "10x engineer" idea has been around for a long time. It's the notion that the best developers don't just work harder — they work at a fundamentally different level than everyone else. AI coding tools have brought that concept back into sharp focus. But Todd Fisher, CEO and co-founder of CTM, thinks the framing needs an update.
"If an engineer becomes 10x more productive because of AI, their competitor's engineer is probably becoming 10x more productive too," Fisher said. "In a competitive market, that doesn't suddenly mean you need fewer engineers. It means you're competing at a different level."
That's a grounded take in an industry that tends toward hype. The baseline is shifting — not just for a few high-performing teams, but for everyone. And the organizations that adapt to that new reality fastest are the ones most likely to pull ahead.
Beyond Code Generation
Most developers have tried an AI coding assistant at this point. Many have also hit a familiar wall: the suggestions are decent, but they don't feel transformative. Fisher says the gap between "okay" and "genuinely useful" comes down to context.
"The more context you can provide, the more capable the system becomes," he said. "It can help navigate information, call additional tools, and surface answers that would otherwise take time to find."
The tools that are delivering real value, in his view, are the ones connected to the actual environment where work is happening — not standalone chat interfaces, but assistants embedded in the workflow itself.
At CTM, that plays out in error reporting. When a defect occurs, AI begins analyzing the issue immediately, traces it back to the relevant code, and suggests a potential fix — sometimes before a developer has even been notified.
"The reaction I sometimes have is, 'How did we survive without this?'" Fisher said. "It eliminates the repetitive work that used to slow teams down when something went wrong."
The Technical Debt Question
One concern that comes up consistently in developer conversations: does AI-assisted coding just accelerate the accumulation of technical debt? Write faster, break things faster.
Fisher sees it differently. As AI tools get more deeply integrated into software products, they develop a more detailed understanding of how those products actually work. That creates the conditions for better support, earlier defect detection, and higher-quality outcomes overall.
"Hopefully we'll see the quality of support go way up, and the number of defects go way down," he said.
That's not guaranteed, of course. But it points to a more optimistic trajectory than the "move fast, clean up later" pattern that has defined much of software development for the past decade.
What to Actually Measure
For engineering leaders trying to evaluate whether AI tools are paying off, Fisher recommends keeping the metrics simple.
Feature activation rates. Error rates. Time from defect detection to proposed fix. These are the numbers that reflect whether engineers are actually accomplishing their goals more effectively — not just shipping code faster.
"The aim is to measure whether people are accomplishing their goals more effectively and with fewer errors," he said.
Human Judgment Still Matters
The skills question is real. If AI can generate code, write tests, and debug errors, what does that mean for the engineers doing those tasks today?
Fisher draws a useful analogy: "A doctor might be able to evaluate possibilities faster with AI, but deciding which possibility is actually correct still requires judgment."
Engineering is no different. The ability to evaluate tradeoffs, understand context, and make decisions remains valuable. AI surfaces options — humans still have to choose.
The Bottleneck Nobody's Talking About
Here's where Fisher gets candid. AI tools can generate code fast. Very fast. But someone still has to review it.
"I have multiple projects that are nearly completed and largely written with AI assistance," he said. "But the bottleneck is finding enough time to individually review everything."
This is the underreported challenge of AI-assisted development. The creation bottleneck is largely solved. The review and validation bottleneck is not. Teams that figure out how to structure human oversight of AI-generated output — without slowing everything back down — will have a meaningful edge.
Where This Is All Headed
Fisher's final point is the one that should get the attention of anyone in the software industry.
"Some SaaS businesses will be disrupted because AI-assisted development makes it easier to build alternatives," he said. "We're going to see dollars move around the industry in ways that are difficult to predict."
That's a significant signal. AI coding tools aren't just changing how software gets built. They're changing the economics of building software at all — lowering the barrier to entry, accelerating feature development, and making it easier to challenge incumbents.
For developers who haven't fully committed to AI coding tools yet, Fisher's advice is practical: start using them and see where they fit naturally into your workflow. Treat them like having a knowledgeable colleague available at all times.
"You can ask questions, explore ideas, and get help working through problems," he said. "You don't have to treat them as a replacement for how you work today."
That's a reasonable starting point. The competitive landscape may eventually make it a necessity.