{
  "video_id": "reddit_1twupqt",
  "channel_slug": "artificial",
  "channel_handle": "r/artificial",
  "title": "$2.5T in AI spending this year. 95% produces zero P&L impact.",
  "url": "https://www.reddit.com/r/artificial/comments/1twupqt/25t_in_ai_spending_this_year_95_produces_zero_pl/",
  "external_url": null,
  "upload_date": "20260604",
  "published_at": "2026-06-04T17:37:45+00:00",
  "transcript": "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.\n\nI'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.\n\n**1. 73% of the engineering work that gets AI into production has nothing to do with the model.**\n\nData 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.\n\n**2. The budget ratio between projects that ship and projects that stall is almost exactly inverted.**\n\nWe 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.\n\n**3. One client went from 71% Copilot adoption to 34% in six months.**\n\nTwo other AI platform licenses dropped under 12%. Combined licensing: $340K/year. The tools worked fine. Nobody redesigned workflows to actually use them.\n\n**4. The median data error rate across our engagements is 14%.**\n\nTeams 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.\n\n**5. Medtech company. Four concurrent AI pilots. No kill criteria. $920K in engineer salary. Eleven months. Shipped: nothing.**\n\nI've now seen this at six companies now. Nobody defines when to stop spending. So nobody stops.\n\n**6. Individual gains are real. Company-level ROI stays flat.**\n\nHCLTech 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.\n\nWhat 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.\n\nEvery CFO I've shown these numbers to adjusted their allocation. Not sure what that says about the budgets they were running before.\n\nSources: Gartner AI Spending Forecast (May 2026), MIT NANDA \"GenAI Divide\" report, HCLTech Enterprise AI Report (May 2026), Writer Enterprise AI Survey 2026\n\nI 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.\n\nWhat do you think about all this though?\n\n\n\n--- Top Comments ---\n\n\n[9 upvotes] 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.\n\n[5 upvotes] Spot on. The \"individual 5x gain vs. flat P&L\" paradox is actually a classic routing and network bottleneck problem.\n\nThis 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.\n\nIf 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.\n\n[4 upvotes] 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. \n\n[3 upvotes] 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.\n\nEvery 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.\n\nThe 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.  \n  \nIf 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.\n\nMy POV. Open t\n\n[2 upvotes] 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. ",
  "transcript_chars": 6017,
  "ingested_at": "2026-06-05T01:30:10.271968+00:00",
  "source": "reddit",
  "yt_meta": {
    "score": 115,
    "upvote_ratio": 0.89,
    "num_comments": 31,
    "author": "Senior_tasteey",
    "is_self": true
  }
}