How AI Is Changing the Way Teams Build Products

Something shifted quietly over the last couple of years, and it's starting to show up loudly in the products you use every day.

Teams that used to take six weeks to ship a feature are now shipping in six days. Engineers who used to hand-write every line of code are now reviewing code their AI tools generated. Product Managers who once spent their days writing detailed specs are starting to wonder what their role looks like in a world where engineers can build almost anything on demand.

This isn't hype. It's happening right now, inside real companies — and the effects are rippling into how every future product builder needs to think.

LinkedIn Is Shipping Faster Than Ever

In late 2024, LinkedIn published internal data showing that AI-assisted development had meaningfully accelerated how fast their engineering teams could ship. Engineers using GitHub Copilot and similar tools were completing tasks significantly faster than before — in some cases, cutting development time nearly in half on certain types of work.

What made this remarkable wasn't just the speed. It was what the speed revealed about bottlenecks that nobody had really paid attention to before.

When you can write the code faster, the constraint shifts. Suddenly, the slow parts are the spec document, the design review, the stakeholder alignment meeting, and the QA process. LinkedIn, like many large tech companies, had to rethink what actually took time — and why.

The engineers didn't become twice as fast. The entire process around engineering had to catch up.

The Code Review Crisis Nobody Saw Coming

If engineers are writing code 100x faster with AI assistance — a number that's been thrown around by investors and researchers, though real productivity gains vary significantly by task type — then something breaks.

You can't review code that fast.

Traditional code review is a human process. A senior engineer reads through a pull request, understands the intent, looks for edge cases, checks for security vulnerabilities, and approves or pushes back. That takes time. It takes expertise. And it assumed that the code was written deliberately, line by line, by someone who understood exactly what they were building.

AI-generated code doesn't work that way. A developer can prompt an AI tool to write hundreds of lines of functional-looking code in seconds. That code might work perfectly. It might also contain subtle security issues, outdated patterns, or logical errors that only surface in production under specific conditions.

This is driving a new category of tooling: automated code review and security scanning that operates at AI speed. Companies like Snyk, Semgrep, and others have been building tools that analyze code for vulnerabilities before it ever reaches a human reviewer. Some companies are now requiring AI-generated code to pass automated security scans before a human even looks at it.

The irony is rich: AI writes the code, AI checks the code, and then a human decides whether to ship it.

What this means is that the bar for what gets deployed is going up, not down — even as the volume of code being written skyrockets. Security and reliability are becoming automated gates, not afterthoughts.

The Product Manager's Role Is Being Reinvented

For the last decade, the Product Manager's job was largely about managing constraints. Engineering time was scarce. Features had to be prioritized ruthlessly. A good PM was someone who could communicate clearly enough to get engineers to build exactly the right thing — no more, no less.

That constraint is loosening.

When a competent engineer with AI tools can prototype a feature in an afternoon that would have taken a week before, the PM's job of "translating business needs into engineering tasks" becomes less load-bearing. Engineers can build more. They can experiment more. They can ship more.

So what does the PM do now?

The clearest answer emerging from teams that have wrestled with this question is: the PM becomes the person who decides what's worth building. Not what's possible — AI is expanding what's possible faster than anyone can track — but what's valuable.

That's a harder job, not an easier one. It requires deeper customer understanding. It requires sharper intuition about what moves the needle on retention, revenue, or trust. It requires knowing how to run a good experiment and how to read the results honestly.

Some PMs are thriving in this environment. They're using AI tools to do their own analysis, prototype their own mocks, and move much faster between idea and validation. They're becoming more technical without needing to write production code.

Others are struggling. If your value was primarily in being the organized person who managed the backlog and ran the sprint, that role is genuinely under pressure.

The PMs who will be most valuable in the next five years aren't the ones who can write the best spec. They're the ones who best understand the customer, make the sharpest bets on what to build, and move fast enough to learn before the window closes.

What This Means for How You Build

If you're a high school student thinking about building products — or a company founder watching this unfold — here's what to take from all of this:

Speed is now a baseline expectation, not a differentiator. The tools that let LinkedIn ship faster are the same tools available to a two-person team building their first product. Speed alone doesn't win. What you build and why it matters still does.

Quality has to be intentional. When you can generate a lot of code quickly, it's tempting to ship things that aren't quite right and fix them later. Companies are learning that AI-assisted speed without AI-assisted quality checking creates new kinds of technical debt. Build the review habits early.

The thinking is the work. The part of product building that AI doesn't accelerate — yet — is the part where you sit with a real user, understand their frustration, and figure out what would actually make their life better. That skill is becoming more valuable, not less.

PMs and builders who can do both will win. The future belongs to people who understand the customer problem deeply and can move fast enough to test solutions before the market moves. That combination used to be rare. Now it's accessible to anyone willing to develop both sides.

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At Loona, students don't just learn about this shift — they work inside it. Every team in Impact Academy uses AI tools as first-class members of the process: for research, prototyping, user testing, and iteration. Not as shortcuts, but as leverage for people who know what they're building and why it matters.

The companies adapting fastest to this moment aren't the ones with the most AI tools. They're the ones whose people can think clearly about what to build — and use AI to build it faster than anyone thought possible.

That's the skill worth developing now.