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GenAI Coding Tools Leadership

Should We Be Worried About Adoption of AI Coding Tools in 2025?

Editor’s Note: This article first appeared in The Current, Jellyfish’s LinkedIn newsletter. You can subscribe for monthly updates and articles like this one here.

As we move into 2025, the conversation around AI coding tools is dominated by loud voices on both sides of the argument. On one side, you have the true believers: the AI evangelists who preach we should all be using AI coding tools all day, every day, and anyone who isn’t is going the way of the dinosaur. On the other side, you have the rationalists: those who argue AI tools haven’t demonstrated nearly enough value to justify the investment – yet.

Who should we follow?

It’s important to remember that innovation isn’t linear, and the best technology doesn’t always win (right away, anyways). Bill Gates famously said that people tend to “overestimate the change that will occur in the next two years and underestimate the change that will occur in the next ten,” meaning significant change happens gradually, rather than in quick bursts.

When it comes to AI coding tool adoption, if buyers don’t see enough value from their AI investments, will they fail to fund the last mile before these tools have the chance to demonstrate their true value? When we look at the market landscape for 2025, it’s important to remember two important factors:

We’re still in the early innings for this technology.

While we’ve all read plenty of opinions and hot takes about AI over the last two years, adoption moves on a much slower timeline. Large enterprises are testing the waters with proof-of-concept projects – in some cases, trialing multiple AI coding tools at once – but they’ve yet to fully invest. The shape and trajectory of the market won’t become clear until these major economic players start making long-term decisions.

Most organizations still haven’t figured out how to measure the impact of AI coding tools.

If we expect adoption of AI coding tools to broaden, it’s incumbent upon us – the industry – to provide better strategies, best practices, and blueprints for that adoption. We need to convey to the end consumer how to best harness the power of AI coding tools rather than waiting for adoption to happen organically. Then, we need to give these users robust data about that adoption and, more importantly, the impact and ROI these tools provide. Without access to proper data, adoption will stall.

The opportunity here is bright, but we still have a long way to go. Instead of making dramatic changes or declaring winners and losers during the first quarter of the game, we should focus instead on measuring and proving where these tools are actually delivering value. In 2025, the engineering organizations that use metrics to inform their AI strategies will start to separate themselves from the pack – realizing the benefits and competitive advantages of AI.