Despite $2.5 trillion in projected global AI spending in 2026, MIT's NANDA Initiative reports 95% of enterprise generative AI projects deliver zero measurable ROI, with a practitioner's first-hand analysis of 14 engagements pointing to misallocated budgets favoring model work over data infrastructure as the root cause.
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](https://thefoundation.limestonedigital.com/p/where-did-2t-go) 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?
Multiple studies show low consumer preference for generative AI, zero productivity impact for most firms, and zero ROI from corporate AI projects, raising doubts about massive AI investments. Data includes Gartner's finding that half of US adults prefer brands without AI, an NBER paper showing 90% of firms see no productivity gain, and an MIT study tracking 95% of corporate AI projects at zero ROI.
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MIT research indicates that AI hyperscalers must achieve a 2.7-fold productivity increase by 2030 to justify $1.1 trillion in infrastructure spending, with risks of capital misallocation if productivity goals are not met.
A research report reveals that the generative AI economy has generated $110 billion in sales over the past 12 months, with a revenue run rate exceeding $175 billion, based on a bottom-up, deduplicated analysis of consumer and enterprise AI spending.
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