AI amplifies. Five Fundamentals Decide Which Way.

According to the DORA 2025 report, AI adoption amplifies existing engineering strengths and weaknesses, with stability declining for all teams as throughput rises. Developers using AI daily produce roughly four times more code, but value delivered grows only about 12%, and a 2026 Faros AI telemetry study of 22,000 developers found pull requests bloating by 51%, review times multiplying fivefold, and 31% more changes merged without review. The article argues that the key question is not AI adoption rate but whether engineering is ready to extract lasting value, assessed through five fundamentals: sustainable delivery, shared understanding, and others.

I remember a team that had just rolled out a coding assistant across the board. Everyone was happy. Developers talked about saving time. The dashboard showed more commits, more pull requests, more closed tickets. Then we looked closer: Velocity was climbing. Understanding was dropping. And nobody was watching the second curve, because the first one was so reassuring. This wasn’t a team failing. It was a team becoming fragile, at high speed, without noticing. I’ve seen this scene replay often enough to think it isn’t an accident. It follows from a question that’s badly framed. Most organizations ask: “ Are we mature in AI? ” That’s an adoption-rate question: enough tools, used often enough, by enough people? Easy to measure, and dangerous for that very reason. It reassures without revealing anything. Because AI doesn’t get adopted like a neutral tool. It applies to a system that already has its strengths and fragilities, well before the first prompt. The right question is:is our engineering ready to extract lasting value from AI? That shifts the focus from the tool itself to the ground it’s acting on. The idea fits in one sentence: AI doesn’t fix engineering, it multiplies what it finds there: strengths and weaknesses alike. Long a field intuition, this is now documented. The DORA 2025 report nearly 5,000 professionals confirms it: AI is an amplifier , not a fix. It draws the same conclusion as this article: the question is no longer whether to adopt AI, but how to prepare to extract value from it. The clearest signal is an asymmetry. Here’s what DORA observes as AI adoption increases: Effect of AI adoption DORA 2025 : Almost everything improves… on average. But that average hides large gaps: solid teams progress, fragile ones fall further behind DORA 2025 . A rising throughput, on its own, says nothing about the health of the system. And exactly one indicator drops for everyone: stability DORA 2025 . By accelerating, AI exposes whatever wasn’t solid beforehand: tests, architecture, feedback loops. And 2024?DORA observed the opposite back then: declining throughput DORA 2024 . The trend reversed in a year. On a topic moving this fast, numbers go stale almost immediately; what endures is themechanism, not the percentage. Here’s the number that sums it all up. Developers who use AI daily produce roughly four times more code , but the value actually delivered grows by only about 12% GitClear data, picked up by Addy Osmani, who honestly notes that part of that 12% comes from the over-representation of top developers among AI users . Four times more code to review, for a tenth more value. The whole gap lives in the one step that didn’t speed up: review . Osmani puts it well: review used to hold up by a lucky accident of speeds: a senior could read faster than a junior could write. That accident is gone. An agent produces a thousand clean lines in seconds, while human reading speed hasn’t moved at all. The numbers follow. A 2026 telemetry study of 22,000 developers Faros AI, picked up by A. Osmani shows pull requests bloating +51% , review times multiplying fivefold, and 31% more changes merged with no review at all, a pattern dubbed the “acceleration whiplash” Faros AI, 2026 . That’s the story of my team: AI didn’t create its problems, it made them faster, and therefore more invisible. To know whether your engineering is ready, five fundamentals are enough to get a sense of it. None of them is AI-specific: that’s the whole point: what decides what you’ll get out of AI are practices that predate it. I group them drawing on established references DORA, Accelerate , SPACE, DevEx, Team Topologies . Sustainable delivery — Can you accelerate without degrading? Fragility signal: large batches, bloating PRs. Inspired by DORA / Accelerate . Shared understanding — Do people still understand the system? Fragility signal: accepted code nobody could rewrite. Inspired by Team Topologies / DORA 2025. Quality guardrails — Do practices hold under load? Fragility signal: lightened review, rising debt. Inspired by Accelerate / GitClear. Productive experience — Real gain, or hidden load? Fragility signal: time saved = time spent supervising. Inspired by SPACE / DevEx. Intentional adoption — Deliberate, or imposed? Fragility signal: “shadow AI”, no measurement. Inspired by the DORA AI Capabilities Model. Two of these do the most damage when missing. Quality guardrails separate AI that accelerates from AI that propagates debt. Generated code is often well-presented, written with confidence… and subtly wrong. Without a safety net tests, review , this misleading plausibility goes straight into the codebase. Shared understanding degrades silently, and it’s the most overlooked angle. Everyone watches technical debt; almost nobody watches cognitive debt. Lionel Chaine, CIO of Bpifrance, made it the title of an article: writing is solved, understanding isn’t https://www.linkedin.com/pulse/l%C3%A9criture-est-r%C3%A9solue-la-compr%C3%A9hension-elle-ne-lest-pas-lionel-chaine-vggme/ , from his own ground: 600 developers, dozens of agents in production. His observation: even in a mature, regulated organization, producing code has become an almost-solved problem, while understanding it still costs just as much Chaine, 2026 . The mechanism is insidious: the agent reasons, then discards its reasoning when it produces the code. The reviewer no longer judges an intention: they have to reconstruct it. And by lowering the barrier to entry, AI removes the “productive struggle” that learning requires. The risk is acute for juniors, whose mentorship erodes when AI does the work in their place DORA 2025 . The trap of “maturity models” is the single score. But the level of each point, taken in isolation, says almost nothing. A team strong on delivery but weak on understanding is in more danger than an average but balanced team. What’s revealing isn’t the heights: it’s the tensions : the gaps between a strength and a weakness. The clearest one pits delivery speed against system understanding : The dangerous corner is top left: shipping fast without understanding is cognitive debt , the one my team from the start fell into. But it’s only one tension among others. Eight recur often enough to deserve a name, each pointing to a profile: The first four share the same root: speed outrunning another capability understanding, guardrails, people, or intent . The next four are governance and adoption tensions. And when a marked gap doesn’t carry an established name, it’s flagged anyway, as a marked imbalance : nothing gets filed as “nothing to see”. Three profiles aren’t tensions but overall states : ● Fragile system : understanding and guardrails both at their lowest. A system nobody understands anymore, with no net protecting it: the riskiest of all. ● Flat adoption : experience and intention both at their lowest. Neither appetite nor direction: AI simply isn’t taking hold. No point pushing the tool until the why is established. ● Sustainable accelerator : all five points high and balanced. The only case where AI truly delivers on its promise. Reading your own situation, then, isn’t about adding up points. It’s about looking at a shape : which strengths, which weaknesses, and above all which ones are in tension. The implication is direct, and a little uncomfortable: One caveat, in fairness: foundations aren’t a comprehensive insurance policy. DORA concludes they amplify AI’s benefits; but the Faros telemetry shows that even very mature teams see their review capacity saturate under the generated volume. Foundations turn speed into value: but you still need to build up, downstream, the capacity to review and test. AI is neither good nor bad for your engineering: it’s an amplifier . The more robust your system, the more value it creates. The more fragile it is, the faster it accelerates its decline, often behind a perfectly reassuring velocity curve. The real question was never AI. It was always your engineering. AI has simply made it urgent. This framework has become an online self-diagnostic: ERA, for Engineering Readiness for AI https://anischaabani.com/en/tools/ai-diagnostic . Ten questions, about three minutes, 100% in the browser: nothing is stored or sent. Two entry doors: dev / tech lead , answering for their team, and executive , choosing their scope one specific team, part of the organization, or all of it . With one particular demand on the executive side: the top level requires a verifiable claim “I can quote our numbers in a board meeting” , not an impression. Two details matter as much as the profile: The figures in these reports change with every new edition. Verify them at the source before any public reuse. Originally published at https://anischaabani.com on June 25, 2026. AI amplifies. Five Fundamentals Decide Which Way. https://pub.towardsai.net/ai-amplifies-five-fundamentals-decide-which-way-8701421ed6b9 was originally published in Towards AI https://pub.towardsai.net on Medium, where people are continuing the conversation by highlighting and responding to this story.