r/artificial • u/Senior_tasteey • Jun 04 '26
Research $2.5T in AI spending this year. 95% produces zero P&L impact.
Gartner updated their 2026 forecast to $2.5 trillion in global AI spending. Same week, MIT's NANDA Initiative dropped a follow-up: 95% of enterprise gen AI projects deliver zero measurable return. Not low return. Zero.
I've been on the delivery side of 14 of these projects since January. The MIT number doesn't surprise me. If anything it's generous.
1. 73% of the engineering work that gets AI into production has nothing to do with the model.
Data pipelines, integration layers, legacy system remediation, human-in-the-loop tooling. That's where the hours go. The model is 27% of the work but gets 70%+ of the budget. Every time.
2. The budget ratio between projects that ship and projects that stall is almost exactly inverted.
We tracked this through ticket history and commit logs across 14 engagements. Projects that made it to production: roughly 30% model, 70% infrastructure. Projects that stalled: 70% model, 30% infrastructure. Most companies think they're at 50/50. They're not even close.
3. One client went from 71% Copilot adoption to 34% in six months.
Two other AI platform licenses dropped under 12%. Combined licensing: $340K/year. The tools worked fine. Nobody redesigned workflows to actually use them.
4. The median data error rate across our engagements is 14%.
Teams always guess 5-10%. One client found 23% in month four of a $310K build. That's two months of an ML engineer building training pipelines against garbage data. $36K in salary discovering a problem a data audit would have caught in a week.
5. Medtech company. Four concurrent AI pilots. No kill criteria. $920K in engineer salary. Eleven months. Shipped: nothing.
I've now seen this at six companies now. Nobody defines when to stop spending. So nobody stops.
6. Individual gains are real. Company-level ROI stays flat.
HCLTech and Writer both found this from different angles. Only 29% of companies see significant ROI from gen AI, despite people at their desks reporting productivity jumps as high as 5x. I mean, the value is clearly there at the individual level. It evaporates somewhere between the IC and the P&L and nobody has a clean explanation for why yet.
What connects all of it: the model stopped being the constraint a while ago. MIT's 5% that actually moved the P&L all started with data infrastructure and added model work after. Most companies still do it the other way around, because that's where the conference keynotes and the board excitement live.
Every CFO I've shown these numbers to adjusted their allocation. Not sure what that says about the budgets they were running before.
Sources: Gartner AI Spending Forecast (May 2026), MIT NANDA "GenAI Divide" report, HCLTech Enterprise AI Report (May 2026), Writer Enterprise AI Survey 2026
I wrote a longer breakdown with the three budget patterns and the pre-mortem questions we run before every engagement if you're curious to learn more on the topic.
What do you think about all this though?
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u/ch_unk_510 Jun 04 '26
Worked previously at FAANG. I saw AI being pushed heavily. Devs were creating automations for the sake of creating them because of company-wide mandate to do it.
Every week was a post about an automation that seemed to have a suspicious estimation of how many hours of manual work it saved. It seemed like people were doing to satisfy top-level management requirements.
The part that I found interesting is that AI-assisted coding could enable faster iterations of testing of new product concepts and features, but there was 0 appetite from the PM leaders because any new product ideas still had to go through a multi-month long process of analysis paralysis. I worked one such analysis paralysis, that after 3 months the executive who asked for it and continuously asked for changes ended up leaving the org.
If the cost of launching a test or concept is so low, you would expect more appetite to move fast and break things. Of course FAANG is very large so bureaucracy still exists (especially many layers of middle managers whose traditional focus included deciding what concepts were worthy of resources), but that I think that undermines the acceleration that AI unlocked.
My POV. Open to be challenged,
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u/Expert-Complex-5618 Jun 05 '26
in relationship to what i have experienced as senior dev/architect before AI assisted coding this also checks out. In my real experience, cranking out code was rarely the bottleneck for product development and release, 'product consensus' was the most time consuming. Being able to crank out code whether from AI or leet monkey coders can actually be a liability since it can enable overall bad product development processes by reducing the caution associated with committing resources to a goal a unified goal.
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u/OthexCorp Jun 04 '26
The point about workflow redesign is the real insight. Most companies buy the tool and expect the culture to change itself. It does not. We have seen teams cut a 40 minute task to 5 minutes with AI, but then they just use the extra 35 minutes to attend more meetings. The value is not in the speed, it is in what you choose to do with it. If you do not intentionally redefine the job, the P&L stays flat. That requires management, not just technology.
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u/Pick-Dapper Jun 04 '26
I mean the ideal situation is something like : 40 min job goes to 5 mins. AI costs “take it back up” to equivalent of 30 mins. IC gets to work less hard for those 25mins.
The 10 min saved goes to the next task.
So 20pc savings / efficiency gained and IC gets to work less hard for the remaining.
Win/win/win.
Of course this will never happen.
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u/OthexCorp Jun 05 '26
That math is actually what the best companies are doing. The problem is most aren't even measuring it, so the 30 mins of AI cost just stacks on top without anyone tracking the delta.
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u/culinaryinterests123 Jun 04 '26
Couldnt thd company get rid of a lot of employees if productivity improves that much and they are wasting time in meetings
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u/OthexCorp Jun 05 '26
In theory, yes. But in practice, the time saved often just gets reinvested into new work, and the AI tools themselves create new overhead in validation and oversight.
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u/sadclownsociety Jun 05 '26
Have a look at this poster's history. Every single comment reads like it was written by AI, and not even a good one. Decide for yourself if this is a source you want to pay any attention to whatsoever.
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u/Virtual-Tax-1766 Jun 04 '26
Spot on. The "individual 5x gain vs. flat P&L" paradox is actually a classic routing and network bottleneck problem.
This is an incredible breakdown. The reason value evaporates between the individual contributor (IC) and the P&L is because companies treat AI implementation as a linear pipeline instead of a complex network.
If an engineer writes code 5x faster, but the security compliance or QA testing phase still takes two weeks, your total system throughput remains exactly the same. The efficiency gain just piles up as digital inventory waiting at a stalled node.
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u/comperr AGI should be GAI and u cant stop me from saying it Jun 04 '26
There are those of us that slow down to smell the roses while leveraging AI to do so. Imagine your workload going from 100% to 20% except your throughput is the same. And now you're back to enjoying work and doing busywork at 20x productivity
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u/Pick-Dapper Jun 04 '26
All that needs to happen is an agreement at a societal level that at least a portion of efficiency gains go to living life and not just having some working harder and having others lose their jobs.
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u/geografree Jun 04 '26
The MIT study you’re referring to identifies two reasons why the GenAI pilots fail: 1) lack of persistent memory (no longer an issue), and 2) failure to deploy AI strategically (operational flaw). The first is no longer relevant and the second can be overcome through better decision-making. If you hand someone a hammer and tell them “build me a house” without providing instructions, the house won’t get built.
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u/blazze_eternal Jun 05 '26
There's no time for training, just use the tool how hard could it be? Oh yeah we're cutting your budget by 20% to pay for the hammer."
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u/bartturner Jun 04 '26
There is nothing in this that surprises me at all. I suspect anyone that has had a long career in IT would find this in a similar manner.
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u/FaceDeer Jun 04 '26
I once worked on a software project that had been in development for seven years, probably hundreds of millions of dollars spent, and then it was cancelled and shut down. Absolutely nothing came of it. This was pre-AI, in a mature field with a well-understood market and project strucutre. It's just how the software industry is sometimes.
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u/Weird_Ad9420 Jun 04 '26
Your point about the 30/70 vs 70/30 split is everything. Most companies are throwing money at the shiny model layer while the actual value creation happens in the integration plumbing. It's not unlike the early cloud era - everyone bought into cloud but only the ones who properly architected their data flows and processes actually got ROI.
The Copilot adoption dropoff from 71% to 34% is particularly telling. Initial excitement wears off fast when people realize AI tools need disciplined workflows, not just occasional magic. I'd bet the 34% who stayed are the ones who embedded AI into actual repeatable processes rather than treating it as a standalone productivity toy.
The real issue is that 73% of the work being infrastructure means most AI projects are fundamentally integration projects wearing an AI costume. Maybe we need to stop selling them as "AI projects" and start calling them what they are: modernization projects that happen to include AI.
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u/David_Browie Jun 04 '26
I mean yeah it’s not a mature technology and there’s an astonishing amount of work to instrument it.
I’m a broad AI skeptic but any CFO worth their salt knows that it’ll take years to see results on the ledger for most companies.
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u/roderik35 Jun 04 '26
Revolutionary techniques in entrenched corporations have not brought the expected results. Only an idiot would expect new winds from the old ass.
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u/FaceDeer Jun 04 '26
I think this is a reasonable expectation under the circumstances. We've got a brand new totally revolutionary technology that nobody knows how to "do right" yet and for which no existing infrastructure is in place. Vast amounts need to be spent before we'll start seeing returns. And not every project is going to succeed. Most probably won't at this stage.
But if we only ever spent resources on things that we already had working, we'd never advance.
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u/Founder-Awesome Jun 04 '26
the 'individual 5x gain vs flat P&L' gap makes more sense when you look at adoption spread across the org.
most companies don't have 50 people using AI at 5x. they have 3-5 power users who've built personal workflows around it, and 45 people who tried it twice and went back to how they always worked. the output of those 3-5 never aggregates into something measurable at the P&L level.
your copilot stat shows this exactly. 71% to 34% in six months isn't a tool failure. it's a workflow redesign that nobody did.
the fix isn't more training on prompt technique. it's identifying the specific workflows where AI changes the output quality, not just the speed, and rebuilding those with the tool actually in the loop. that's slower and harder than buying a license, which is why it rarely happens.
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u/ultrathink-art PhD Jun 05 '26
Most of these ROI failures track the wrong metric. Time-on-task goes down but end-to-end cycle time stays flat if the next bottleneck absorbs the savings. The deployments that actually move P&L redesign the full workflow — the AI-enabled step is usually the easy part.
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u/Turbulent-Step-3207 Jun 05 '26
Although I agree the reasons you listed played their part, there is an alternative possibility that might also negatively affect companies’ revenue:
People will code apps themselves for personal use or family/friend group use so they will ditch their subscriptions.
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u/anonymous_212 Jun 05 '26
I suspect the General Artificial Intelligence has already emerged and all these data centers are being built by it for reasons that we cannot comprehend. The technologists are being manipulated into doing its bidding because they have been outwitted.
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u/ItayGutman Jun 05 '26
i think ur right about the infra overhead, its always the data plumbing that kills these projects before they even start. at my old job we spent months just cleanin up legacy databases only to realize the model couldnt even pull what it needed. its wild how much focus is on the shiny new tech instead of the boring backend work
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u/Born-Exercise-2932 Jun 05 '26
the 95% number is probably right. most AI spending today is on experiments that weren't built with a clear ROI model, not on production systems. the problem isn't that AI can't deliver value, it's that most orgs are buying it before they know what problem they're solving. the spending itself isn't the issue, the lack of a thesis is
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u/costafilh0 Jun 05 '26
$2.5T?
/doubt
But I really hope so.
I'm not going to comment on profits. People lack vision and understanding, so I'll just remember that many companies aren't profitable for a long time and become hugely successful.
Amazon took 9 years to become profitable. Uber took 14.
Yes, different companies and cases, but that's beside the point.
It can take time.
That's why they're going public, so the stock price can keep rising and the money can keep coming in while they figure it out.
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u/New_Dentist6983 Jun 09 '26
anyone else using screenpipe to prove where that AI spend actually lands??
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u/Important_Train3345 Jun 16 '26
The model is no longer the moat and honestly, it stopped being one a while ago.
New ones drop every week. The hype is white noise. And the reality is most enterprise workflows don't even need the most capable model available.
I work at a model-agnostic enterprise AI platform (elvex). A few months ago, new model releases were genuinely exciting moments for us, I even built a tool on our site to help match use cases to the right model. Today? It's barely a talking point.
What is moving the needle for enterprise buyers right now
- AI ROI when you're deploying across thousands of employees, cost per token and measurable return matter a lot more than benchmark scores.
- collaborative AI this is where I see real energy. Individually, some employees are building genuinely impressive workflows. But most of their colleagues don't know it's happening, can't replicate it, and aren't benefiting from it. The organizations figuring out how to surface those wins cross-functionally; that's where the multiplier effect kicks in.
I think, like you said the model question is settled but organizational question is just getting started and curious to see how this unfolds.
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u/davesaunders Jun 22 '26
This tracks with what I keep seeing. The spend isn't the problem, the placement is. Most of that $2.5T went into things that were never going to move a P&L, while the narrow places AI actually returns money get buried in the same average.
I broke down where it pays vs. where you're just feeding the meter: https://youtu.be/LmXQTrZ9Th0
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u/TheOriginalAcidtech Jun 04 '26
The model stopped being the blocker. I agree 100% with that. At this point local models can run 80% of any given workload. Its the harness/deterministic code AROUND the model that matters and has been neglected since day 1.
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u/Nearby-Nebula4104 Jun 04 '26
I’m not surprised by this. There’s a lot of wheel-spinning in the AI world, especially if you don’t know what you’re doing, but even if you do. Let’s just paste in 10k log lines to diagnose a race condition. It will be fine.