The Impact of AI on Industry Productivity

Last #TeachMeTuesday of 2025 | How will Artificial Intelligence Impact Productivity Growth? This week I read Toby Sytsma’s new RAND Perspective on “The Dynamics Behind AI’s Impact on Productivity Growth.” https://lnkd.in/ecB6NzRn. I came across this work as it references the World Intellectual Property Organization – WIPO work on intangible assets (thank you!). Toby offers a careful explanation of why AI’s economic potential is enormous and yet its economy-wide productivity impact remains modest so far, using work of eminent Erik Brynjolfsson. A useful part of the analysis is its decomposition of how AI could raise productivity through distinct mechanisms. Three vectors stand out: • Augmenting human capital AI can raise the productivity of workers not merely by automating tasks, but through performance enhancement: quicker completion, improved quality, and diffusion of best practices (especially for less-experienced workers). This “skill equalisation” mechanism matters because productivity gains may lift the lower part of the performance distribution—not only frontier firms. • Capital deepening Much of the early effect is through AI-related investment: data centres, chips, etc.. RAND shows that these investments are already contributing measurably to labour productivity, but they mostly reflect more capital per worker rather than deep efficiency yet. • Total factor productivity RAND argues that genuine efficiency gains—reorganisation of production, creation of new tasks, and new business models—arrive only once firms redesign workflows around AI. Historically, this transformation takes time, as with electricity. This framing leads to one of the paper’s key insights: measurement challenges are structurally built into emerging AI diffusion, because the key assets are often intangible (software,, organisational change). It is therefore entirely plausible that the “AI productivity paradox” represents a measurement lag, not a technological disappointment. More conceptually, the analysis asks us to distinguish between (i) the productive capacity AI enables, and (ii) the extent to which economies have reorganised themselves to exploit it. Modern technology makes the first rapid; institutions, organisations, and skills determine the speed of the second. The deeper productivity wave depends on organisational learning and institutional adaptation—a process that historically unfolds over years, not quarters. See also: World Intangible Investment Highlights 2025: https://lnkd.in/eJ4teFNB Global Innovation Index 2022 on productivity: https://lnkd.in/eNGxDFJT #TeachMeTuesday Carl Benedikt Frey

The report titled "Estimating AI productivity gains from Claude conversations" by Anthropic, released in November 2025, provides valuable insights into the impact of AI on productivity. Some highlights: AI is applied to substantial work: The median task handled with Claude would take ~1.4 hours without AI, indicating use on meaningful professional tasks rather than trivial micro-work . Time savings are large but uneven: Median estimated savings are ~80–84%, concentrated in reading, synthesis, and writing tasks; tasks requiring physical presence or quick expert judgment see much smaller gains. Higher-wage roles benefit more: Management, legal, and analytical occupations both use AI on longer tasks and capture higher economic value from time saved, amplifying productivity effects. Productivity gains are highly concentrated: Software developers, managers, marketers, customer service reps, and teachers account for most of the estimated economy-wide impact, while sectors like construction, restaurants, and in-person healthcare see little effect. Acceleration creates bottlenecks: Tasks that AI does not speed up, such as supervision, travel, or enforcement, become the dominant constraints within jobs, limiting overall productivity gains. The 1.8% productivity estimate is an upper bound: It assumes universal adoption, static workflows, and no time spent on validation, likely overstating near-term gains even if long-term effects could be larger. Measurement is the key contribution: The report’s main advance is a scalable method for tracking AI productivity using real usage data, enabling longitudinal analysis as tasks, models, and adoption evolve.

Probably one of the best papers written about the impact of AI on product development, scientific discovery, engineers and scientists to date. 🔁 The paper highlights the dual nature of AI’s impact—boosting overall innovation while introducing challenges related to skill utilization and work satisfaction. 🦾 Increased Productivity: AI-assisted researchers discovered 44% more materials, leading to a 39% increase in patent filings and a 17% rise in new product prototypes. These AI-generated materials showed enhanced novelty and contributed to significant innovations. 🧑🏫 Disparate Impacts: The tool disproportionately benefited the most skilled scientists, doubling their productivity while having minimal impact on lower-performing peers. This exacerbated performance inequality, showcasing the complementarity between AI and human expertise. 🤖 Shift in Research Tasks: AI automated 57% of idea-generation tasks, allowing scientists to focus more on evaluating and testing AI-suggested materials. Top researchers effectively leveraged their expertise to prioritize the best AI outputs, while others struggled with false positives. 😞 Impact on Job Satisfaction: Despite productivity gains, 82% of scientists reported lower job satisfaction, citing reduced creativity and underutilized skills as significant concerns. This underscores the complexity of integrating AI into scientific work. 🚀 Broader Implications: The study's findings imply that AI can significantly accelerate R&D in sectors like materials science, emphasizing the value of human judgment in the AI-assisted research process. It suggests that domain knowledge remains crucial for maximizing AI’s potential.

Anthropic just released a fascinating study analyzing 100,000 real Claude conversations to estimate AI's impact on labor productivity. The headline numbers: Tasks that take 90 minutes without AI get done in about 18 minutes with it. Average time savings: 80%. Median task value: $54 in equivalent professional labor. Projected impact: 1.8% annual boost to US labor productivity - double the recent growth rate. Examples of acceleration: Curriculum development that would take teachers 4.5 hours completed in 11 minutes (estimated labor cost: $115). Financial analysts save 80% of time on tasks like interpreting investment data. Executive assistants save 87% of time drafting invoices, memos, and documents. Where the gains are concentrated: Management and legal tasks show the longest time savings (nearly 2 hours per task). Software developers contribute the most to overall productivity gains (19%), followed by operations managers and marketing specialists. The nuance that matters: Time savings vary dramatically: healthcare assistance tasks see 90% speedups while hardware troubleshooting shows only 56%. This creates potential "bottlenecks" where tasks AI can't accelerate become a larger share of the workday. What I appreciate about this research: Anthropic is actually trying to measure what so many of us have felt - that moment when you realize something that used to take 2 hours just took you 2 minutes. https://lnkd.in/ercpDeA7

Just out: Quantifying the impact of #genAI on job performance, by Erik Brynjolfsson & team: "Access to AI assistance increases worker productivity, as measured by issues resolved per hour, by 15% on average, with substantial heterogeneity across workers. The effects vary significantly across different agents. Less experienced and lower-skilled workers improve both the speed and quality of their output, while the most experienced and highest-skilled workers see small gains in speed and small declines in quality. We also find evidence that AI assistance facilitates worker learning and improves English fluency, particularly among international agents. While AI systems improve with more training data, we find that the gains from AI adoption are largest for moderately rare problems, where human agents have less baseline experience but the system still has adequate training data. Finally, we provide evidence that AI assistance improves the experience of work along several dimensions: customers are more polite and less likely to ask to speak to a manager." Open access: https://lnkd.in/d4UecpnQ

AI-assisted tasks completed using Claude show an estimated 80% reduction in task time, which could potentially double US labor productivity growth over the next decade if widely adopted. 1️⃣ Claude analyzed 100,000 real user conversations to estimate how long tasks would take with and without AI. 2️⃣ Without AI, tasks averaged 90 minutes; with Claude, that dropped by 80%, typically to around 18 minutes. 3️⃣ The biggest time savings occurred in tasks like compiling information from reports (up to 95% faster) and writing standard documents (87% faster). 4️⃣ High-wage occupations like management and legal showed the greatest potential for AI-driven gains, with estimated task costs around $130 per task. 5️⃣ Some fields (such as healthcare support and education) also saw strong gains, with up to 90% time savings on specific tasks. 6️⃣ However, AI had smaller impact on quick tasks like image diagnostics or hardware troubleshooting, where humans are already efficient. 7️⃣ Across all sampled tasks, Claude handled work equivalent to $54 in labor costs per session. 8️⃣ If adopted across the US economy, current-generation AI could raise annual labor productivity by 1.8%, implying a 1.1% boost in total factor productivity. 9️⃣ These gains are concentrated: software developers, managers, and marketing specialists account for most of the projected productivity growth. 🔟 Time savings may shift job dynamics, speeding up some tasks while making others (like in-person inspections or supervision) new bottlenecks. ✍🏻 Alex Tamkin, Peter McCrory. Estimating AI productivity gains from Claude conversations. Anthropic Research. 2025.

Is AI delivering real productivity gains? What's the ROI so far? Hot takes abound, but data have been scarce. Noam Segal and I took it upon ourselves to find out what’s actually happening on the ground by running one of the largest independent, in-depth surveys on how AI is affecting productivity for tech workers (1,750 respondents). We surveyed product managers, engineers, designers, founders, and others about how they’re using AI at work. tl;dr: AI is overdelivering. 1. 55% of respondents say AI has exceeded their expectations, and almost 70% say it’s improved the quality of their work. 2. More than half of respondents said AI is saving them at least half a day per week on their most important tasks. We’ve never seen a tool deliver a productivity boost like this before. 3. Founders are getting the most out of AI. Half (49%) report that AI saves them over 6 hours per week, dramatically higher than for any other role. Close to half (45%) also feel that the quality of their work is “much better” thanks to AI. 4. Designers are seeing the fewest benefits. Only 45% report a positive ROI (compared with 78% of founders), and 31% report that AI has fallen below expectations, triple the rate among founders. 5. Engineers have accepted AI as a coding partner and now want it to handle the more boring (but necessary) work of building products: documentation, code review, and writing tests. 6. n8n is currently dominating the agent landscape, though actual adoption of agentic platforms in 2025 has been slow. 7. A whopping 92.4% of respondents report at least one significant downsides to using AI tools. There’s definitely room for improvement. Here's the full report: https://lnkd.in/gR5G88yA Inside: - What exactly AI is doing for people, function by function? - Where are the biggest opportunities for AI startups? - Which AI tools have product-market fit? - The downsides of AI productivity - Bonus: The state of agentic AI: promise outpaces practice - What this all means - Appendix: Who took this survey

The AI productivity paradox is real — and the data is in. A new study from the Federal Reserve Banks of Atlanta and Richmond, Duke University, and NBER surveyed nearly 750 CFOs about AI's actual impact on their businesses. The findings challenge both the hype and the doom narratives. Adoption is accelerating: 58% of firms invested in AI in 2025. By end of 2026, that jumps to 85%. But here's the paradox: CFOs perceive 3% productivity improvements, while revenue-based measures show just 1.8%. We feel the impact before it shows up in the numbers. The most striking finding? The strongest productivity gains aren't coming from cutting headcount or costs. They're coming from developing new products and reaching customers more effectively. On jobs: aggregate employment impact is just -0.4%. But the composition is shifting — routine clerical roles down 2 percentage points by 2028, skilled technical roles up 1.4 points. The study's authors — Salomé Baslandze, Zachary Edwards, John Graham, Ty McClure, Brent H. Meyer, Michael Sparks, Sonya R. Waddell, and Daniel Weitz — summarise it well: "The revenue productivity gains associated with AI investment operate largely through innovation, product-market and demand-side channels." If your AI strategy is primarily about cost reduction, you may be leaving the bigger opportunity on the table. What's your experience — where are you seeing AI gains that haven't yet shown up in the metrics? Source: NBER Working Paper 34984 (March 2026) #AIProductivity #FutureOfWork

The AI productivity paradox is wild. I'm digging through Lightspeed's annual people trends report for 2025-2026. As will be shocking to no one reading this, it's full of AI data. AI tools are delivering 27% productivity gains for teams. Small startups are seeing 33%. Companies that actually mandate AI skills? They're hitting 38% gains—that's 11 points higher than everyone else. So naturally, companies are cutting headcount to save money, right? Nope. 69% are maintaining or growing their teams. That's smart. 🧠 Yet, it runs counter to all the AI-fueled layoffs we're reading about. Here's my key takeaway: We're at an inflection point where AI isn't replacing jobs—it's redefining what jobs look like. The companies maintaining headcount despite massive productivity gains understand something critical: the goal isn't efficiency for efficiency's sake. It's about what you do with that newfound capacity. The 28% of entry-level roles disappearing? Those are the routine, task-based positions that AI handles better than humans. But companies are reinvesting that capacity into work that requires judgment, relationship-building, and strategic thinking—the things AI still can't do well (today, at least). Only 15% of companies mandate AI skills for new hires, leaving 85% on the table with an 11-point productivity advantage. The smartest move right now isn't choosing between AI and humans. It's figuring out how to use AI to amplify what makes your team uniquely valuable—whether that's customer relationships, creative problem-solving, or strategic decision-making. Are you using your productivity gains to do the same work with fewer people, or to do better work with the same people? Productivity gains are cool. But what you do with them is what actually matters. https://lnkd.in/ebATyXh6

This paper on AI from the Fed is the single best thing to read for a snapshot of: 1) The recent innovations in generative AI 2) How firms are using AI 3) The likely macro implications of AI I can't often can bring myself to fully read long reports, but this one is well worth it. Abstract: With the advent of generative AI (genAI), the potential scope of artificial intelligence has increased dramatically, but the future effect of genAI on productivity remains uncertain. The effect of the technology on the innovation process is a crucial open question. Some inventions, such as the light bulb, temporarily raise productivity growth as adoption spreads, but the effect fades when the market is saturated; that is, the level of output per hour is permanently higher but the growth rate is not. In contrast, two types of technologies stand out as having longer-lived effects on productivity growth. First, there are technologies known as general-purpose technologies (GPTs). GPTs (1) are widely adopted, (2) spur abundant knock-on innovations (new goods and services, process efficiencies, and business reorganization), and (3) show continual improvement, refreshing this innovation cycle; the electric dynamo is an example. Second, there are inventions of methods of invention (IMIs). IMIs increase the efficiency of the research and development process via improvements to observation, analysis, communication, or organization; the compound microscope is an example. We show that genAI has the characteristics of both a GPT and an IMI—an encouraging sign that genAI will raise the level of productivity. Even so, genAI’s contribution to productivity growth will depend on the speed with which that level is attained and, historically, the process for integrating revolutionary technologies into the economy is a protracted one. #AI #Fed #rates #macro #economics