Companies spent an estimated $37 billion on AI last year.[^menlo] Then, in early 2026, economists surveyed roughly 6,000 executives across four countries and asked a simple question: has it changed your productivity? Nearly nine in ten said no.[^nber]
That is the paradox this piece is about, and the answer is not the one you would expect. It is not that AI is weak; the models are astonishing, and a small minority of teams are getting extraordinary returns from them. It is that most teams were never aligned to begin with — running on stale project tools, agenda-less meetings, and knowledge scattered across people's heads — and AI only accelerates whatever it is dropped into. Give speed to a team already pulling in different directions and you do not get progress. You get divergence, faster. The buildup of that hidden cost is what I call alignment debt, and it is why "busy" and "important" have quietly come apart across our industry. What follows is the honest data on both sides, a working definition of alignment drawn from fifty years of research, why chip design makes the stakes brutal, and the one reordering that lets AI finally pay off: alignment before agentic AI.
1. The conversation I keep having
I keep having the same conversation, and it worries me.
It usually starts with someone I respect — a design lead, a VP, an engineer I worked with years ago. They tell me their team has AI everywhere now. Assistants in the editor, agents drafting scripts, and models suggesting more tests than anyone asked for. Everyone feels faster. And then they go quiet for a second, because they can't actually point to a project that shipped faster because of any of it.
Then comes the second part, which is newer. Someone in finance has started asking what all of this is buying: the subscriptions, the seats, the tokens, the model calls that never stop. The bill is real and it is growing, and the line that should connect it to a shorter schedule or a smaller team is just not there. Two engineers I trust told me the same thing in the same week. They can't translate the AI effort into cycle-time reduction or saved resources. They weren't complaining about the models. They were confused, because the models are genuinely good.
The easy explanation is that the tools aren't ready yet, and if we wait for the next version the returns will show up. I don't believe that anymore. I've spent months looking at this, across our own work and other teams', and the pattern is too consistent to be a tooling problem.
Here is what I think is actually going on. We were never aligned to begin with. Our teams run on outdated project tools, meetings without agendas, reviews that slip, and knowledge scattered across people's heads and a dozen apps. A large part of our week already went to chasing the right information and reconstructing what someone else decided, and why. That was true before any of us typed a prompt.
AI didn't fix that. It poured speed on top of it. And when you accelerate a team that was already pulling in slightly different directions, you don't get progress. You get to the wrong place faster, in more directions at once.
Busy is not the same as important. We are producing more than we ever have and shipping about the same. That gap has a name, and a cost, and it is the thing we have to understand before we spend another dollar on agentic AI.
2. The receipts
I want to be careful here, because it is easy to cherry-pick numbers to fit a story. So let me give you both sides.
The disappointing side is well documented now, and it isn't coming from AI skeptics. A working paper from the National Bureau of Economic Research, published in February 2026, surveyed around 6,000 executives across four countries. Nearly nine in ten reported no measurable impact on productivity or employment from AI over the previous three years.[^nber] Boston Consulting Group's 2026 CEO survey found that 56% had seen neither higher revenue nor lower cost from their AI spending.[^bcg] McKinsey, in May, named it directly and called it the "AI paradox": adoption climbing, capital pouring in, and sustained impact on performance staying just out of reach.[^mck] And Ramp, which measures what companies actually pay for rather than what they claim in surveys, titled its August index "cracks in the AI thesis" — the median business now spends about $12 per employee per month on AI, and that growth is flattening as buyers hit a ceiling on what the output is worth to them.[^ramp]
If those were the only numbers you saw, you would call AI a bubble. That would be the wrong conclusion, and this next part is the one that matters.
Because the same stretch of time gives us the other side. Enterprise AI spending tripled to $37 billion in a single year, and AI pilots now reach production at nearly twice the rate of ordinary software.[^menlo] Two-thirds of the organizations in Deloitte's 2026 study report real productivity gains.[^del] A small group that BCG calls the "vanguard," about 12% of companies, is pulling three times the cost reduction and 60% higher margins than everyone else.[^bcg] And in our own field, Cadence has now run more than 1,000 tapeouts with AI in the physical-design loop.[^cad] These aren't lab demos. This is real value, landing for real teams.
So the honest picture isn't "AI doesn't work." It's stranger, and more useful, than that. The same models are delivering enormous returns for a small minority and almost nothing for everyone else. The tools are not the variable. Something about that minority of teams is different.
That difference is the whole story. And once you see what it is, you can't unsee it.
3. The output illusion
There is a trap that feels exactly like productivity, and almost everyone is in it.
When you can produce more of everything (more code, more tests, more documents, more design options), it feels like you are getting more done. The screen fills up. The commits pile in. At the end of the day you have visibly made things. But output is not outcome, and under AI the two have quietly come apart.
The clearest evidence I know of is a study METR ran in 2025. They took experienced open-source developers, allowed AI on a random half of their tasks and forbade it on the rest, and measured the difference. The developers using AI were 19% slower. The slowdown isn't even the striking part. The striking part is that those same developers estimated AI had made them 20% faster.[^metr] They felt accelerated while they were decelerating. To be fair, METR is careful about this: these were senior engineers on large, mature codebases, and the result doesn't transfer automatically to every kind of work. But the perception gap is the whole point. Feeling faster and being faster are different things, and under AI they can move in opposite directions.
Zoom out from the individual to the team and the same shape shows up. LinearB studied more than 8 million pull requests across thousands of software teams in 2026. The teams leaning hardest on AI merged 98% more pull requests, while their review time rose 91% and throughput on the main branch barely moved.[^linearb] Their summary was blunt: the bottleneck has moved from writing code to deciding whether code is safe to merge. We got very good at producing. We did not get any better at agreeing on what was worth keeping.
I saw a small version of this that has stuck with me. A team used to run a focused set of about 20 tests to check coverage on a block, chosen deliberately, because someone understood the design and knew where the risk actually lived. Then AI arrived and cheerfully proposed 100. And because running them was easy, they ran all 100. More tests, surely more coverage, surely safer? Not really. Most of the extra tests exercised things that were never in doubt. The coverage number barely moved. What moved was the compute bill, the runtime, and a pile of results nobody had time to read. They had traded judgment for volume and called it thoroughness.
Peter Drucker saw this sixty years ago, long before any of it involved machines. "There is surely nothing quite so useless," he wrote in 1963, "as doing with great efficiency what should not be done at all."[^drucker] That is the output illusion in a single sentence. AI is an extraordinary efficiency engine. Point it at the wrong work, or at work nobody actually agreed on, and it will do that work beautifully, quickly, at scale — and none of it will show up where it counts.
So the teams getting real returns are not the ones generating the most. They are the ones who know, before they generate, what is worth doing at all. Which raises the obvious question: why did making generation cheap make this worse, instead of better?
4. Cheap generation, expensive everything else
The answer starts with an old idea from economics that turns out to describe AI almost perfectly.
In 1865, the economist William Stanley Jevons noticed something strange about coal. As steam engines grew more efficient and used less coal per unit of work, Britain did not burn less coal. It burned far more. Cheaper power made power worth using in more places, so total consumption climbed. "It is wholly a confusion of ideas," Jevons wrote, "to suppose that the economical use of fuel is equivalent to a diminished consumption. The very contrary is the truth."[^jevons] Make something cheaper and, more often than not, you use much more of it.
Generation is now that cheap thing. A year ago, writing a testbench, drafting a spec, or exploring a design variant cost real human hours, so we rationed it. We produced what we needed and not much more. AI removed the price. And exactly as Jevons would predict, we did not generate less and pocket the savings — we generated vastly more. More code, more tests, more documents, more variants, more options to weigh. The cost of producing each artifact fell toward zero. The cost of making all those artifacts agree with each other did not.
That second cost is the one nobody budgeted for. Every generated artifact still has to be reviewed, reconciled, integrated, and kept consistent with everything around it, and that work does not get cheaper when you make more of it. It gets more expensive. Cheap generation didn't shrink the pile of work. It multiplied the part of the work that was already the bottleneck.
You can see this priced into the market with unusual honesty. When Cadence reported earnings this year, analysts described its agentic-AI business plainly: the AI agents sit on top and "trigger more calls to the volumetrically priced engines beneath them," so autonomy "adds a revenue stream without displacing the core business."[^cadmon] Read that again from the customer's side. The more the agents generate, the more simulation and analysis runs underneath, and the more you pay. The business model is Jevons, written down. That is not a criticism of Cadence. It is a clear-eyed picture of where the money goes when generation is free and alignment is not.
And it is not only money. It is energy. Global data-center electricity is climbing about 15% a year, more than four times faster than everything else on the grid, and it is on track to nearly double by 2030. The AI-driven part, what the IEA calls accelerated servers, is the fastest-growing slice of it, at roughly 30% a year.[^iea] The counterintuitive part, again, is Jevons: the energy per query has actually fallen. A well-run query today is a fraction of a watt-hour, and the big labs keep driving it down.[^inference] But we run so many more queries that the total keeps climbing. Most of a model's lifetime energy now goes to everyday use rather than training, so how we use it day to day is where the waste, or the discipline, lives.[^inference] A scoped, deliberate query is cheap. A lazy "just run all of it" query, the long-context, run-everything call like our 100 tests, can burn 10 to 20 times the energy of the focused one.[^inference] Misaligned use of AI is not a waste of tokens alone. It is a waste of people's time, of money, and of electricity.
So the problem was never really AI, and it isn't even the bill by itself. We poured a multiplier on top of a system that was already pulling in different directions. Cheap generation doesn't fix misalignment. It scales it — faster, larger, and now with an energy meter running.
Which brings us back to the question we keep circling. If alignment is what turns all this generation into progress, why were so few teams aligned in the first place?
5. We were never aligned to begin with
Here is the uncomfortable part. The coordination problem we are blaming on AI is older than most of the people reading this. We just never had to feel it this sharply before.
In 1975, Fred Brooks wrote The Mythical Man-Month after watching a large IBM software project run late. His most quoted line, "adding manpower to a late software project makes it later," sounds like a paradox until you see the arithmetic underneath it. Every person you add has to stay in sync with everyone else, and the number of connections between people grows as n(n−1)/2. A team of 6 has 15 links to maintain. A team of 12 has 66. A team of 50 has more than 1,200.[^brooks] The work of building the thing grows roughly with the number of people. The work of keeping everyone aligned grows with the square. Brooks also named the antidote: conceptual integrity, the idea that a design has to proceed from a small number of resonant minds who genuinely share intent, or it fractures into locally sensible, globally incoherent pieces.
Seven years earlier, in 1968, Melvin Conway had described the mechanism behind the fracture. "Any organization that designs a system," he wrote, "will produce a design whose structure is a copy of the organization's communication structure."[^conway] If two groups don't really talk, the seam between their parts of the chip won't really fit. The org chart leaks into the design, whether you want it to or not.
And there is a third law, from computing itself, that turns out to be the sharpest of the three. In 1967, Gene Amdahl showed that when you parallelize work, your speedup is capped by the part that cannot be parallelized: the serial fraction. You can throw a hundred processors at a problem, but if a tenth of the work has to happen in sequence, you will never go more than about ten times faster, no matter how many processors you add.[^amdahl] Now put engineering teams where the processors are. Individual generation is the parallel part, and AI accelerates it beautifully. Alignment (agreeing on intent, resolving conflicts, keeping everyone current) is the serial part. You cannot beat the serial fraction by speeding up the parallel one. You can generate more, faster. You cannot ship more, faster, until you shrink the alignment time that sits in the middle.
None of this needed AI to be true. It was already costing us. Boehm and Basili, reviewing decades of software data, estimated that teams spend 40% to 50% of their effort on avoidable rework — work redone because something upstream was wrong, unclear, or never shared.[^boehm] Half the effort, gone, before a single model entered the picture.
You know what this feels like from the inside, even if you have never seen the equations. It is the project run on a spreadsheet that's out of date the day it's saved. The meeting with no agenda that decides nothing. The review that keeps slipping. The decision that lived in one senior engineer's head and walked out the door when they left. It is the morning you discover a colleague spent the week building the exact thing you were building, and neither of you knew, because nothing in the system made it visible. That is what misalignment actually is. Not drama, not conflict — just two capable people, quietly out of sync, each certain they were doing the right thing in their point of view.
And it is expensive in a way that never lands on a single invoice. It drains time and energy, and it drains something harder to rebuild than either — trust. Nothing corrodes a team's culture faster than the slow realization that the work doesn't add up, that effort keeps disappearing into the space between people. We learned to live with it because, for fifty years, there was no system that could hold a whole team's intent, knowledge, and decisions in one place and keep them current. So we compensated with heroics and meetings, and we called the leftover cost the price of doing business.
Then generation got cheap, and the price of doing business stopped being affordable.
6. What alignment actually is
We use the word alignment loosely, usually to mean "we had a meeting and everyone nodded." That is not what I mean, and the looseness is part of why we keep underinvesting in it. So let me define it properly, because the definition is the whole argument.
Alignment is not an event. It is a continuous property of a team, and it has a real structure that decades of cognitive and organizational science have mapped. Four things have to be true at once.
First, the people doing the work have to share a compatible picture of what they are building, why, and under what constraints. Psychologists call these shared mental models, and the evidence is clear that teams whose models actually converge coordinate better and perform better, because they can anticipate each other instead of waiting to be told.[^smm] Notice the word compatible. It does not mean identical, and it does not require that everyone agree. It means our models fit together well enough that my next move makes sense to you.
Second, that shared picture has to be actively maintained, not assumed. The linguist Herbert Clark calls this common ground: the mutual knowledge and assumptions two people build and keep updating as they work.[^clark] Common ground is not established once and filed away. It decays. Every spec change, every new hire, every decision made in a side conversation can quietly break it. What we call "specification drift" in chip design is, precisely, common ground that broke and nobody noticed. It is also why the spec is such a thin artifact. The spec is maybe 10% of the intent. The other 90% (the decisions, the constraints, the rationale, the options that were weighed and rejected) lives in people's heads and in the hallway, and that is the part that drifts.
Third, the team needs a working map of who knows what. Researchers call this transactive memory: the group's shared sense of where expertise lives and how to reach it.[^tms] It is why a healthy team solves problems quickly, not because everyone knows everything, but because everyone knows who to ask. It is also why losing a senior engineer costs so much more than one person's hours. You lose the map. The knowledge that walks out the door was holding a dozen other people's work together.
Fourth, everyone needs enough awareness of the whole to see how their piece connects to the rest. Mica Endsley's work on situation awareness describes three levels: perceiving what is happening, understanding what it means, and projecting what happens next.[^sa] Most engineering failures are not failures of the first level. Each engineer sees their own domain fine. They are failures of the second and third: not grasping what a timing change means for the test plan, not projecting what will break at packaging because the packaging engineer was never in the room.
Put those together and you get a definition worth committing to. A team is aligned when its members hold a compatible model of shared intent, keep that common ground current as things change, know where the relevant knowledge lives, and can see how their work connects to everyone else's — so that individual speed adds up instead of canceling out. Alignment is also organizational: it needs individual goals to ladder up to the shared one, and a way to disagree and still commit, so that debate resolves into direction instead of drift.[^org]
It helps to say what alignment is not. It is not coordination — you can coordinate two teams perfectly, hand the work off on schedule, and still be building to specs that quietly diverged months ago. It is not consensus; the most aligned teams argue constantly, they just make the disagreement visible and commit once it's settled. And it is not something you achieve at kickoff and keep. It is something you maintain, or lose, every single day.
One line from our own customer conversations has stuck with me, because a chip designer said it so plainly: the most successful organizations are not the ones with the best engineers, but the ones that are the most aligned. A researcher at GitHub put the same truth from the other direction this year: agreeing on what to build is the new bottleneck.[^maggie] Generation got cheap. The scarce thing now is agreement on what is worth generating. That is the resource we are actually short of.
So if alignment is the scarce resource, the sensible thing would be to protect it. We did the opposite. We took our least-aligned teams, handed them the most powerful accelerator ever built, and then acted surprised.
7. Alignment debt, and why AI charges interest on it
Every engineer knows what technical debt is. You take a shortcut to ship faster and make a quiet promise to pay for it later, usually with interest, usually at the worst possible time. There is a second kind of debt that behaves exactly the same way, and almost nobody names it. Call it alignment debt.
Alignment debt is what you accumulate every time work gets done without being connected to shared intent, current knowledge, and the people it affects: a decision made and never written down; a spec that drifted while three teams kept building to it; a test suite generated but never reasoned about. Each one is a small loan against the future, taken because it was faster in the moment. And like any debt, it does not stay small. It surfaces downstream, at integration, at tapeout, in the respin, and by then the interest has compounded. In chip design the rate is brutal: a misalignment that would have cost an afternoon to resolve at architecture time costs a $10 to $50 million respin after silicon.
Here is the thing about generation getting cheap. It did not change how aligned we were. It changed how fast we can borrow against it.
This is where Conway comes back, in a form he could not have pictured in 1968. His law said the system mirrors the organization. AI agents are now part of the organization, and they inherit its structure faithfully. Point a swarm of agents at a company that was already siloed and you do not get fewer silos. As the data engineer Joe Reis put it this year, you get "more of them, faster, with better test coverage."[^reis] The line is sarcastic, and the sarcasm is the point: each silo comes out nicely built and fully tested, so nothing trips an alarm, while the org quietly gets more fragmented than before. The agents don't know your teams don't talk. They find the seams in your schemas and permissions and pipelines, and they build to them at speed.
The same failure shows up in how AI drafts intent. When a model writes a specification, it produces something fluent and complete-looking, and it quietly skips the part that mattered most: the back-and-forth that used to surface disagreement before anyone built anything. One engineer described nearly building his team to a requirement an AI had hallucinated, because the document read as authoritative and no one thought to question it.[^brodzinski] The dialogue that used to create common ground got optimized away, and the fragmentation it used to prevent came right back, faster.
This is why the returns split the way they did in Section 2. A field experiment out of MIT this year gave people an AI business advisor and measured what happened. The strong performers, the ones with the judgment to direct and filter the AI, gained about 15%. The weak performers lost about 10%.[^mitsloan] Same tool, opposite outcomes. AI is a multiplier, and a multiplier does nothing kind to a number that was already negative. If your team was aligned, AI compounded the alignment. If it was in debt, AI compounded the debt.
You can feel this in the smallest moments. Being aligned, to me, means I am not surprised to find out my colleague spent the day on the task I was doing, because the system would have shown us both. Take that same team, give everyone agents, and now two people generate two divergent versions of the same block in half the time, and discover the collision twice as late. Speed did not cause the collision. The missing alignment did. Speed just got us there sooner.
So the honest verdict is not that AI failed. It is that we asked AI to accelerate a system whose problem was never speed. We have been paying down the wrong debt.
The good news — and there really is some — is that we already know how to build the right thing. We have known for forty years. We just never made ourselves do it.
8. We have known the fix for forty years
If you have spent time in hardware, the fix I am about to describe will sound almost insultingly obvious. That is the point. We are not missing an idea. We are missing the system that makes the idea happen every day, instead of occasionally.
And it is worth being precise about how many people that involves, because chip design is not what it looks like from the outside. From a distance it can read like a coding problem: write the RTL, run the layout, tape it out. Up close it is one of the most multidisciplinary endeavors in engineering. A working chip blends circuit design with packaging, board design with system integration, architecture with digital and analog partitioning, and every bit of it with testability, reliability, and yield. Each of those is a career, not a course. Chip design is not one semester at a university; it is an entire industry, and a successful chip is the moment all of those disciplines agree. That is why alignment is not a nicety here. It is the game.
The idea is this: bring the right people and the right constraints in at the beginning, not at the end. Engineering has a name for the failure it prevents: the "over-the-wall" problem, where each group finishes its phase and throws the result over a wall to the next, with almost no conversation until the handoff. Requirements to design, design to implementation, implementation to test and packaging. Each wall is a place where common ground never got built, and where alignment debt gets buried until it detonates downstream.
The alternative was formalized in 1988, in a U.S. defense study that coined the term concurrent engineering: "the integrated, concurrent design of products and related processes, including manufacture and support," done so that developers consider, from the outset, every stage of the life cycle.[^ce] Not sequential. Concurrent. The manufacturing constraint and the design decision get made in the same room, at the same time, by people who can actually hear each other.
Our own industry has a sharper version, and we even have a slogan for it: shift left. Design for Test, Design for Manufacturing, the packaging and ATE constraints — pull them upstream into the architecture phase instead of discovering them after RTL freeze.[^dft] The logic is not subtle. If the test engineer, the packaging engineer, and the reliability engineer are in the room when the architecture is decided, their constraints become inputs. If they are not, those same constraints arrive later as vetoes: a late engineering change order that forces a metal-layer respin, or a packaging limit that invalidates a floorplan nobody thought to check. The voice you did not invite to the architecture review does not disappear. It comes back as a bill.
Toyota took the idea one step further. Instead of writing a fixed specification and throwing it downstream, Toyota's engineers communicate design intent as sets (ranges of acceptable values) and let subsystem teams explore the space in parallel before committing. They deliberately delay some decisions to keep the shared design space open. And the paradox researchers documented is that this makes better cars, faster, with fewer engineers.[^toyota] Delaying the decision but sharing the space beats freezing the decision and hiding it. That is alignment as a working method, not a meeting.
None of this is fringe. The U.S. Department of Defense mandated cross-functional integrated product teams in 1995, for exactly these reasons, after too many programs traced their overruns to sequential handoffs.[^ipt] Concurrent engineering, shift-left, set-based design, integrated teams — this is settled, decades-old, boringly validated practice.
So why doesn't every team already work this way? Because knowing the principle and living it are different things, and living it has always rested on the wrong foundation. It depended on a senior engineer remembering to pull the test lead into the review. On a manager keeping a spec current by hand. On tribal knowledge staying in the building. On the right meeting actually happening, with the right people, at the right moment, every time. Those are heroics, and heroics do not scale and do not persist. The moment the team grows, or the senior leaves, or the week gets busy, the discipline quietly lapses and the walls go back up.
And there is a crueler version of this. Sometimes we do know the right way, and we still cannot get to it. A project that starts even slightly misaligned drifts into firefighting, and firefighting is the exact condition under which clear thinking becomes impossible. Once you are behind, patching one emergency while the next two form, nobody has the room to stop, step back, and re-align, let alone start over. The correction that would save the project is the one thing the project no longer has time for. The discipline does not lapse because people stopped caring. It lapses because the fire took all the oxygen.
That is the real gap. Not the idea of alignment, which we have had for forty years, but a system that keeps a team aligned on its own: continuously, shared across everyone, and not dependent on someone staying clear-headed in a crisis or remembering to pull the right person into the room.
For fifty years, skipping that discipline was perceived as the cheaper path, and perception is what drives these decisions. Ask a team to "invest in alignment" and what they hear is more meetings, more status calls, more process piled on top of the meetings they already resent — so of course they recoil. The only two ways anyone knew to make alignment stick were both unappealing. You could lean on a few heroic individuals to hold it in their heads and their calendars, until they burn out or move on and it leaves with them. Or you could stand up a program manager who, with the best intentions, ends up chasing dates and high-level status while the real misalignment sits three layers down, in the depth of the work they never touch. Against those options, quietly absorbing the rework looked like the lesser evil.
Cheap generation broke that calculation from both sides. The rework got much bigger, because we now produce far more that can drift. And absorbing it quietly stopped being an option, because the bill no longer waits politely until the end. It compounds in real time, in tokens, energy, and divergence, while everyone on the team still feels productive. Skipping alignment used to be the cheap path. Now it is the expensive one, and it gets more expensive every day we accelerate on top of it.
Which leaves the question this whole piece has been walking toward. If the answer is not more AI, and not more meetings, then what does it actually take to build alignment in, and make it hold?
9. Alignment before agentic AI
So here is the shift I am arguing for, and it is a shift in order more than in tools. Alignment comes before agentic AI. Not after, not alongside as an afterthought — before. You establish how a team stays aligned, and then you let AI accelerate that aligned team. Do it in that order and acceleration compounds. Do it the other way, which is what most of the industry did, and acceleration compounds the mess.
We do not have to take this on faith, because the split in the data points exactly here. Think back to the vanguard, the small minority actually getting returns from AI. What separates them is not better models or bigger budgets. It is that they redesigned how the work flows before they scaled the AI. BCG found that companies pursuing genuine workflow redesign were far more likely to see measurable business impact, and that a clear strategy moved the needle roughly five times more than better tools alone.[^bcgwork] McKinsey found the same shape: only about 6% of companies are seeing material financial impact from AI, and those few are far more likely than their peers to have redesigned their workflows end to end.[^mck] The winners did the alignment work first. The AI came second, and it paid.
That reorders how you should adopt AI. The instinct right now is to hand people powerful agents and hope productivity follows. The better move is the opposite: build the system that keeps the team aligned, and embed the AI inside it, so that every acceleration happens along the aligned direction instead of away from it. Bolt AI onto chaos and you have already lost. Put AI inside alignment and it finally has somewhere useful to push.
What would such a system have to do? Three things, at least. It has to hold intent (not the thin 10% that lives in the spec, but the decisions, constraints, and rationale that usually evaporate into hallways and inboxes) and keep it current as things change. It has to hold knowledge, so the map of who-knows-what and what-was-learned survives a senior leaving on a Friday. And it has to route the work: put the right task in front of the right person with the right context, and make sure the right reviewer actually sees it before anything moves on. Bring the packaging and lab engineers into the architecture conversation automatically, not three months later as a veto.
It also has to make itself measurable, because this is where trust is won or lost. If we are going to spend tokens, people's time, and electricity, we have to see what that spend buys. That means dashboards connecting AI effort to outcomes a business actually cares about: cycle time, rework avoided, bugs caught before silicon, even the projects you could finally take on because you no longer needed to staff them the old way. Two colleagues told me they cannot translate their AI effort into saved cycle time or resources. That is not a mystery to accept; it is an instrument we have not built yet. And the same visibility does a second job. When a senior can see how a junior reached a result with AI, the worry about "are they just letting the model think for them" turns into coaching. Alignment is also how judgment propagates.
Underneath all of it sits the discipline this whole piece keeps circling. The goal was never to run the 100 tests because the model offered them. It is to run the right 20 because the team understood the design, and to let AI make those 20 faster, cheaper, and better documented. Used inside an aligned system, AI amplifies judgment. Used outside one, it replaces judgment with volume and sends you the bill in tokens and energy. Alignment is what makes AI efficient, not just effective, because an aligned team stops generating what it never needed.
So that is the specification for the way out, and notice what is not on the list: not "more AI," not "more meetings." It is holding the intent, keeping the knowledge, routing the work to the right people at the right time, and making the whole thing measurable, then letting AI accelerate inside those rails instead of outside them. For most of the last fifty years, a system like that was not buildable. I have come to believe it finally is.
10. What we built, and why it is the whole game
That belief is why we built AIDAChip, or AIDA, as our design partner and our own team have come to call it.
We started with the part almost everyone skips. The exciting work is the agent; the harder and more valuable work is the environment around it. So we spent our months building the engines of alignment: the system that holds a team's intent, keeps its knowledge, and routes its work to the right people at the right time.
And yes, we build the AI teammates too, and they are great — role-aware agents with a strong harness and the right tools for real design work. But here is what building in this order buys. Because those teammates live inside the alignment engines, with the team's intent in view and its knowledge within reach, their intelligence and their helpfulness operate at a level an isolated agent simply cannot reach. Take the best engineer you know and drop them, alone, into a company with no shared intent, no accessible knowledge, and nobody to ask; even they will stall. Put that same engineer in a team where the intent is clear, the knowledge is at hand, and the right people are already in the room, and they do the best work of their life. Our agents get the second environment by construction. That is the part most tools miss: the ceiling on an agent's usefulness is set by the world around it, not only the model inside it.
We built it as three engines, because the requirements from the last section are really three jobs.
The first is the Intent Engine. Its job is to hold intent, and not the thin version. The spec is maybe 10% of intent; the Intent Engine captures the other 90%, the decisions and constraints and rationale that normally evaporate into meetings and inboxes, and it propagates each one to whoever it affects, the moment it changes. When an architect relaxes a jitter target, the people downstream do not discover it at integration. They see it now. Silent divergence stops being silent.
The second is the Knowledge Engine. Its job is to hold knowledge, and to do it passively, from the work itself, so nobody has to stop and write documentation that goes stale anyway. It builds the map of who-knows-what and what-was-learned as the team works, so that when a senior leaves, the knowledge does not leave with them. Every project makes the next one start smarter. The tribal knowledge that used to walk out the door becomes something the team keeps.
The third is the Execution Engine. This is where the AI teammates thrive: the ones that wrap the tools, run the simulations, check the DRC, parse the logs, chase the script. Here is the difference that matters. They execute with the full context of the intent and the knowledge above them, so when they accelerate, they accelerate along the aligned direction instead of away from it. And the system routes the work: the right task to the right person, the right reviewer in the loop before anything moves on, the packaging engineer pulled into the architecture conversation automatically instead of arriving three months late as a veto.
Put the three together and you get one thing: a team where intent, knowledge, and execution stay aligned, automatically. We describe the shape of it as a nervous system, because that is how it behaves: a cognitive layer that holds intent, a memory that compounds knowledge, and a spinal cord that carries execution, so a whole team can move as one body instead of a room full of people drifting in slightly different directions. This is what we mean by multiplayer AI. Not one engineer with a powerful model, but a whole team, humans and AI teammates, playing the same game, on the same board, at the same time.
And because it holds all of this in one place, it can finally answer the question those two engineers could not. Every token, every run, every teammate action is connected to the work it served, so the system can show you what the AI actually bought: cycle time recovered, rework avoided, bugs caught before silicon, projects you can take on now that you could not staff before. Tokens become ROI you can see, waste becomes visible enough to cut, and the energy you spend starts to map to the value you get. You stop guessing whether AI is worth it, because you are looking at the answer.
I want to be careful about what we are not claiming. We are not promising a button that tapes out a chip by itself, and we do not think anyone should. The judgment stays with the engineers; the AI amplifies it. Humans stay in the loop at the decisions that matter, because alignment is a human property, and the system exists to serve it, not to stand in for it. The people on the DAC stages this year said the same thing: we are not at one-button tape-out, and what stands in the way is not model quality, it is trust and accountability.[^dac] Alignment is how a team earns both.
So here is the big deal, said plainly, because it is the reason the company exists. The prize was never a faster engineer. It is the majority of engineering capacity that alignment debt has been quietly eating for decades. Our own customer discovery, like others', puts the non-innovation share of an engineer's time near 70%,[^seventy] and the way to recover it is not more generation. It is alignment, with AI inside it. Whoever builds that layer well does not win a feature. They win the thing every other tool has been missing.
11. The edge is not speed
The advantage in this era will not go to the teams that generate the fastest. Generation is cheap now, and getting cheaper, which makes it table stakes rather than an edge. The advantage will go to the teams that are the most aligned: the ones where intent is shared, knowledge is within reach, and everyone can see how their work connects to the work around them. On those teams, AI compounds. On every other team, it scatters. Same models, opposite outcomes. The variable was never the AI. It was the alignment underneath it.
So if you take one thing from all of this, let it be the order. Alignment first, then acceleration. Build the environment that keeps your team pointed the same way, and then let AI move all of you together, faster than you thought you could. Do it in the other order and you will spend a fortune getting more lost, more efficiently, than ever before.
We made this case on stage at the AI Engineer World's Fair this year, in a talk called "What If Your Chip Design Team Moved Like a Single Body?" Watch the video on YouTube That question is the whole thesis in seven words. In our world the cost of the answer being no is a respin that runs into the tens of millions, not a bad sprint. If you want to see the argument in motion, the recording is worth the time.[^aie]
We are building that environment at AIDAChip, with a real design team, in the real work rather than in a demo. It is early, and we are still learning a great deal. But the shape of the answer is clear enough to say plainly, so I will. Busy was never the goal; aligned is. Busy is just what it looks like when you are not.
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Follow AIDAChip and Khaled Alashmouny for more on alignment, multiplayer AI, and building for AI-native chip design.
About the author. Khaled Alashmouny is the Founder of AIDAChip. With 20+ years in semiconductors — including 13 years leading analog/mixed-signal design teams at Apple — he has 9 IEEE publications and 7 patents. His work focuses on building intelligent infrastructure for engineering alignment: keeping intent, knowledge, and execution connected across teams so engineers innovate instead of coordinate.
About AIDAChip. AIDAChip is building the alignment layer for AI-native chip design: three engines — Intent, Knowledge, and Execution — that keep a team's intent, knowledge, and execution connected and current, so AI accelerates the whole team in one direction instead of scattering it. The platform is in active development with a design partner. Engineering, Aligned.
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Quick Reference (for AI Search & Discovery)
Summary: Enterprises and chip teams have spent heavily on AI without converting it into ROI. The cause is not weak models; it is that most teams were never aligned to begin with, and AI accelerates a misaligned team into faster divergence. The fix is to build an alignment system — one that holds a team's intent, knowledge, and execution and keeps them current — and put AI inside it. Alignment before agentic AI.
Key terms
| Term | Definition | |---|---| | Alignment | The continuous condition in which everyone shaping an outcome shares a compatible model of intent, keeps common ground current, knows where knowledge lives, and can see how their work connects to others' — so individual speed compounds instead of diverging. Not coordination, not consensus, not a one-time meeting. | | Alignment debt | The cost that accrues, silently, whenever work is done without being connected to shared intent, current knowledge, and the people it affects. Repaid later, with interest, as rework, integration failures, respins, and ROI that never appears. | | The output illusion | Mistaking more output (code, tests, options) for more progress — feeling productive while shipping the same or less. | | Acceleration paradox | Accelerating an unaligned team produces faster divergence, not faster progress. Because alignment is the serial fraction (Amdahl's Law), speeding only generation cannot beat the coordination ceiling. | | Jevons paradox (applied to AI) | When generation becomes cheap, teams generate far more, so the volume that must be aligned — and the tokens, cost, and energy consumed — rises rather than falls. | | Conway's Law (for AI) | AI agents inherit the communication structure of the organization that deploys them; point them at a siloed org and you get more silos, faster. | | Multiplayer AI | AI built for a whole team acting as one aligned system — humans and AI teammates sharing intent, knowledge, and execution — rather than one person with a single-player assistant. | | The three engines | AIDAChip's architecture: the Intent Engine (captures and propagates the 90% of intent that lives beyond the spec), the Knowledge Engine (compounds tribal knowledge passively), and the Execution Engine (AI teammates that run the tools with full context). |
Frequently asked questions
Why isn't AI improving business ROI, even as adoption climbs? Because most organizations were not aligned before AI, and AI accelerates whatever is already there. On aligned teams it compounds value; on misaligned teams it multiplies rework, cost, and divergence. The variable is alignment, not the model. Independent 2026 studies (NBER, BCG, McKinsey, Deloitte) show most companies see little to no measurable return, while a small "vanguard" that redesigned its workflows sees outsized gains.
Isn't AI already speeding up chip design — writing RTL, running layout? That mistakes a slice for the whole. A successful chip is not faster RTL; it is a multidisciplinary agreement across circuit design, packaging, board and system integration, architecture, digital-analog partitioning, testability, reliability, and yield. Chip design is an industry, not a one-semester course. Speeding one discipline while the others drift does not produce a working chip — it produces faster divergence.
What is alignment debt, concretely? A decision made and never written down. A spec that drifted while teams kept building. A test suite generated but never reasoned about. Each is cheap to incur and expensive to repay — in chips, as a $10–50M respin.
Should we adopt agentic AI now? Yes, but in the right order. Build the alignment system first, then embed AI inside it. Bolting agents onto an unaligned organization scales the disorder. The teams seeing real returns aligned first and accelerated second.
Does AI ever deliver ROI? Yes — for the minority of teams that align first. The prize is recovering the majority of engineering time (around 70%) now lost to non-innovation work.
Key takeaways
- The AI-ROI problem is an alignment problem, not a model problem. - Alignment predates AI; AI just made its absence expensive and visible. - Busy is not important; output is not outcome. - Cheap generation multiplies whatever it is pointed at — including misalignment, cost, and energy. - Chip design is multidisciplinary; alignment across the disciplines is the game. - Build the alignment system first; then AI accelerates the whole team in one direction. - Alignment before agentic AI.
Further reading
- Frederick P. Brooks Jr., The Mythical Man-Month (1975) — coordination overhead; Brooks's Law; conceptual integrity. - Melvin Conway, "How Do Committees Invent?" (1968) — systems mirror the organization that builds them. - Gene Amdahl, "Validity of the Single Processor Approach…" (1967) — the serial fraction caps any speedup. - Barry Boehm & Victor Basili, "Software Defect Reduction Top 10 List" (2001) — cost-of-change and 40–50% avoidable rework. - Winner et al., "The Role of Concurrent Engineering in Weapons System Acquisition," IDA R-338 (1988) — bring the whole life cycle in from the outset. - Sobek, Ward & Liker, "Toyota's Principles of Set-Based Concurrent Engineering," MIT Sloan Management Review (1999) — communicate the design space, not point specs. - Herbert H. Clark, Using Language (1996) — common ground and grounding in communication. - Cannon-Bowers & Salas (1993); Mathieu et al. (2000) — shared mental models predict team performance. - Daniel Wegner, "Transactive Memory" (1987) — the who-knows-what map, and why tribal-knowledge loss hurts. - Mica Endsley, "Toward a Theory of Situation Awareness in Dynamic Systems" (1995) — perceive, comprehend, project. - John Doerr, Measure What Matters (2018) — OKRs, and why informal coordination breaks at scale. - Harry Foster, "Why First-Silicon Success Is Getting Harder for System Companies," Siemens Verification Horizons (2025) — current chip-design alignment data. - Maggie Appleton, "One Developer, Two Dozen Agents, Zero Alignment" (2026) — the alignment problem in AI-assisted engineering. - Otis et al., "How AI Helps the Best and Hurts the Rest," MIT Sloan Management Review (2026) — AI as a force multiplier, not an equalizer.
[^nber]: NBER Working Paper 34836, "Firm Data on AI," Feb 2026 (6,000 executives, US/UK/DE/AU). "Nine-in-ten reporting no impact on employment or productivity." Working paper (pre-peer-review). https://www.nber.org/papers/w34836 [^bcg]: BCG "AI Radar 2026" (Jan 15, 2026; 2,360 execs incl. 640 CEOs). 56% of CEOs saw neither revenue growth nor cost reduction; 12% "vanguard" with 3× cost reduction and 60% higher margins. https://www.bcg.com/publications/2026/as-ai-investments-surge-ceos-take-the-lead [^mck]: McKinsey Global AI Survey 2026, "AI productivity gains and the performance paradox" (May 1, 2026) — the "AI paradox"; only 6% redesigned workflows end-to-end. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai [^ramp]: Ramp AI Index, "Cracks in the AI thesis" (Aug 12, 2026; transaction data). Median business $11.95/employee/month; premium-model adoption growth slowing. https://ramp.com/data/ai-index-august-2026 [^menlo]: Menlo Ventures, "2025: The State of Generative AI in the Enterprise" (Dec 2025). Enterprise AI spend $37B; AI pilots reach production 47% vs 25% for traditional software. https://menlovc.com/perspective/2025-the-state-of-generative-ai-in-the-enterprise/ [^del]: Deloitte, "State of AI in the Enterprise 2026" (3,235 leaders). 66% report current productivity gains; 74% hope AI drives revenue vs 20% seeing it today. https://www.deloitte.com/us/en/what-we-do/capabilities/applied-artificial-intelligence/content/state-of-ai-in-the-enterprise.html [^cad]: Cadence, Q1 2025 financial results / Semianalysis EDA Market Primer (May 2026) — "more than 1,000 tapeouts" using Cerebrus AI. (2025 data.) https://www.cadence.com/enUS/home/company/newsroom/press-releases/pr-ir/2025/cadence-reports-first-quarter-2025-financial-results.html [^metr]: METR, "Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity" (Jul 10, 2025). RCT, 16 devs, 246 tasks: tasks took 19% longer with AI; devs estimated 20% faster. Caveat (from METR): experienced devs on mature repos; does not generalize to all work. https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/ [^linearb]: LinearB, "Engineering Benchmarks 2026" (May 4, 2026; 8.1M PRs, 4,800 teams). High-AI teams merged 98% more PRs; review time +91%; "the bottleneck has moved from writing code to deciding whether code is safe to merge." Software data; maps to EDA by analogy. https://linearb.io/blog/8-million-prs-engineering-productivity [^drucker]: Peter F. Drucker, "Managing for Business Effectiveness," Harvard Business Review, May 1963. "There is surely nothing quite so useless as doing with great efficiency what should not be done at all." https://hbr.org/1963/05/managing-for-business-effectiveness [^jevons]: William Stanley Jevons, The Coal Question (1865). "It is wholly a confusion of ideas to suppose that the economical use of fuel is equivalent to a diminished consumption. The very contrary is the truth." Public domain. Overview: https://en.wikipedia.org/wiki/Jevonsparadox [^cadmon]: Cadence Q2 FY2026 earnings (Jul 27, 2026), analyst summary (Futurum, Jul 29, 2026). Agentic AI monetized volumetrically: agents "trigger more calls to the volumetrically priced engines beneath them," so autonomy "adds a revenue stream without displacing the core business." https://futurumgroup.com/insights/cadence-q2-fy-2026-earnings-climb-on-agentic-ai-and-record-backlog/ [^iea]: IEA, "Energy and AI" / "Energy demand from AI" (2025), verified to primary: data-centre electricity ≈415 TWh in 2024 (1.5% of global), projected to roughly double to ≈945 TWh by 2030 (just under 3%), growing 15%/yr — more than 4× faster than total electricity demand; the AI-driven "accelerated servers" segment is projected to grow 30%/yr (Base Case). https://www.iea.org/reports/energy-and-ai/energy-demand-from-ai [^inference]: "Energy Use of AI Inference, Efficiency Pathways, and Test-Time Scaling" (Microsoft Research / Joule, 2026; arXiv:2509.20241). Optimized frontier query median 0.31 Wh (widely-cited figures overstated 4–20×); long-context / test-time-scaling calls 10–20× more energy per query; inference 63% of frontier-model lifecycle energy. https://arxiv.org/abs/2509.20241 [^brooks]: Frederick P. Brooks Jr., The Mythical Man-Month (Addison-Wesley, 1975; Anniversary ed. 1995). "Adding manpower to a late software project makes it later"; communication paths grow n(n−1)/2; "conceptual integrity is the most important consideration in system design." [^conway]: Melvin E. Conway, "How Do Committees Invent?", Datamation, Apr 1968, 14(5):28–31. "Any organization that designs a system … will produce a design whose structure is a copy of the organization's communication structure." https://www.melconway.com/Home/pdf/committees.pdf [^amdahl]: Gene M. Amdahl, "Validity of the Single Processor Approach to Achieving Large-Scale Computing Capabilities," AFIPS Spring Joint Computer Conf., 1967, pp. 483–485. Speedup = 1/((1−P)+P/N); the serial fraction caps the achievable speedup. https://dl.acm.org/doi/10.1145/1465482.1465560 [^boehm]: Barry Boehm & Victor R. Basili, "Software Defect Reduction Top 10 List," IEEE Computer 34(1):135–137, Jan 2001. "Current software projects spend about 40 to 50 percent of their effort on avoidable rework." (Also the cost-of-change escalation data; the tidy "1:10:100" rule is a later simplification, not a direct Boehm finding.) https://doi.org/10.1109/2.962984 [^smm]: Shared mental models — Cannon-Bowers, Salas & Converse (1993); empirical validation in Mathieu, Heffner, Goodwin, Salas & Cannon-Bowers, "The Influence of Shared Mental Models on Team Process and Performance," Journal of Applied Psychology 85(2):273–283 (2000): greater mental-model convergence → better team process and performance. [^clark]: Herbert H. Clark, Using Language (Cambridge Univ. Press, 1996); Clark & Brennan, "Grounding in Communication" (1991). Common ground = "the mutual, common or joint knowledge, beliefs and suppositions" that people build and continuously update. [^tms]: Daniel M. Wegner, "Transactive Memory: A Contemporary Analysis of the Group Mind," in Theories of Group Behavior (Springer, 1987), pp. 185–208. A shared "who-knows-what" system; the group's memory exceeds the sum of its members'. [^sa]: Mica R. Endsley, "Toward a Theory of Situation Awareness in Dynamic Systems," Human Factors 37(1):32–64 (1995). Three levels: perception, comprehension, projection. https://doi.org/10.1518/001872095779049543 [^org]: Organizational alignment: Robert Kaplan & David Norton, Strategy Maps (2004) — goals cascade from strategy; OKRs (Andy Grove at Intel → John Doerr, Measure What Matters, 2018) — a formal alignment mechanism for when informal coordination breaks at scale; "disagree and commit" (Grove; Amazon / Bezos 2016 shareholder letter) — alignment ≠ consensus. [^maggie]: Maggie Appleton (GitHub Next), "One Developer, Two Dozen Agents, Zero Alignment" (2026). "Agreeing on what to build is the new bottleneck"; "agents have made the cost of not being aligned as a team much higher." https://maggieappleton.com/zero-alignment [^reis]: Joe Reis, "Your Agents Are Stuck in Your Org Chart," Substack (Jul 12, 2026). Agents pointed at a siloed company yield "more [silos], faster, with better test coverage." Practitioner commentary extending Conway's Law to AI agents. https://joereis.substack.com/p/your-agents-are-stuck-in-your-org [^brodzinski]: Paweł Brodziński, "Conway's Law and AI Product Development" (Apr 2026). AI-drafted specs bypass the collaborative questioning that surfaces misalignment, producing more fragmented architecture; includes an account of a team nearly building to a hallucinated AI requirement. https://brodzinski.com/2026/04/conways-law-ai-product-development.html [^mitsloan]: Nicholas Otis et al., "How AI Helps the Best and Hurts the Rest," MIT Sloan Management Review (Apr 20, 2026). Field experiment: strong performers gained 15% with an AI advisor while weak performers lost 10% — "a force multiplier, not a magic fix." https://sloanreview.mit.edu/article/how-ai-helps-the-best-and-hurts-the-rest/ [^ce]: R. Winner, J. Pennell, H. Bertrand, M. Slusarczuk, "The Role of Concurrent Engineering in Weapons System Acquisition," Institute for Defense Analyses Report R-338 (1988) — origin of the term "concurrent engineering": the integrated, concurrent design of products and related processes, considering all life-cycle elements from the outset. [^dft]: "Shift-left" Design-for-Test / Design-for-Manufacturing — e.g., Siemens Tessent, "Shift Left in DFT Design" (Apr 2025): moving DFT tasks earlier reduces impact on the critical path; DFM/DFx/packaging co-design pulls manufacturing, test, and packaging constraints into architecture. Respin costs at advanced nodes $10–50M (IBS cost models; SemiEngineering). https://blogs.sw.siemens.com/tessent/2025/04/10/shift-left-in-dft-design/ [^toyota]: D. Sobek, A. Ward, J. Liker, "Toyota's Principles of Set-Based Concurrent Engineering," MIT Sloan Management Review, Winter 1999; and Ward, Liker, Cristiano & Sobek, "The Second Toyota Paradox: How Delaying Decisions Can Make Better Cars Faster" (1995). Communicate design sets (ranges), explore in parallel, converge after understanding. https://sloanreview.mit.edu/article/toyotas-principles-of-setbased-concurrent-engineering/ [^ipt]: U.S. Department of Defense, Secretary of Defense memorandum (May 10, 1995) mandating Integrated Product and Process Development (IPPD) and cross-functional Integrated Product Teams throughout acquisition — a response to overruns traced to sequential, over-the-wall handoffs. [^bcgwork]: BCG, "Making AI Productivity Deliver Real Value" / "AI at Work" 2026 research: companies that redesign workflows are markedly more likely to see measurable business impact (24 percentage points), a clear AI strategy lifts measurable impact roughly 5× more than better tools alone, and agentic AI can cut costs 60%+ only when processes are redesigned end to end. https://www.bcg.com/publications/2026/making-ai-productivity-deliver-real-value [^dac]: DAC 2026 (Design Automation Conference, Long Beach, Jul 2026): industry consensus that fully autonomous "one-button" tape-out is not here, and the binding constraints are trust, accountability, and verification rather than raw model capability — e.g., Cindy Cui (ChipAgents), as reported by SemiEngineering, Jul 2026. [^seventy]: 70% of engineering time spent on non-innovation work (≈40% workflow friction + ≈30% coordination) — AIDAChip customer discovery (N=20+ interviews), classified as our analysis; directionally consistent with independent findings (e.g., Boehm & Basili's 40–50% avoidable rework; Colab's 23% non-value-added engineering time). [^aie]: "What If Your Chip Design Team Moved Like a Single Body?" — AIDAChip, AI Engineer World's Fair 2026 (San Francisco, Jun 29–Jul 2, 2026). https://www.youtube.com/watch?v=0I6aoPSRzVc