01-j-curves

Something is showing up in the macroeconomic data that hasn't in over a decade. US labor productivity growth is accelerating. In a February 11, 2026 Wall Street Journal interview Stanford economist Erik Brynjolfsson made a striking claim:

2.7% doesn't sound dramatic until you see that the 2014–2018 average was roughly 1.2%. At 2.7%, growth is more than double the trough, a return to pre-slowdown rates not seen since the early 2000s.

The best way to understand productivity is as a ratio of output to work:

That's exactly what happened in 2020: COVID wiped out millions of jobs, and the workers who remained were disproportionately in higher-output roles. The 5.5% spike isn't a technology story, it's a composition effect. But look at 2024 and 2025. Employment hasn't collapsed, yet productivity is running near 3%. That suggests something other than workforce composition is driving output higher, and Brynjolfsson is arguing that it's technology.

Now look at the mid-2010s: 0.9%, 1.4%, 0.8%, 1.3%. For half a decade, productivity growth barely broke 1%. That's the trough period, when massive technology investment produced no visible return. The contrast with the last two years is striking.

Brynjolfsson calls this the "J-curve," and he's been studying it for thirty years.

To understand why he's so confident, and why 2.7% isn't a fluke, you need to understand the pattern he's been tracking since the 1990s.

The Productivity Paradox

The story starts with a quote from an economist that might sound familiar to anyone watching the AI debate today:

This represented the dominant thinking at the time. In the 1970s and 1980s, economists puzzled over a productivity paradox: massive investment in digital technologies wasn't showing up in productivity statistics. Weren't computers supposed to make us more productive?

The J-Curve Pattern

Erik Brynjolfsson spent the next two decades proving they did. His firm-level panel studies analyzing years of Compustat data showed that IT investment increased productivity, but only when paired with complementary organizational changes.

Brynjolfsson & Hitt (2003) View article

Sample: Large US firms (Compustat panel) Period: 1987–1994 Intervention: IT investment + organizational change

IT investment pays off, but only when paired with complementary organizational changes, and with a 3–5 year lag.

5–6%

Higher Productivity

Firms that combined IT capital with new processes and organizational structures saw 5–6% higher productivity than those that didn't.

2–3×

IT Market Value

The market value of IT capital was often 2–3 times its replacement cost, reflecting the intangible complements built around it.

3–5yr

Lag to Payoff

Returns didn't show up at purchase. They lagged by 3–5 years, arriving only after organizational change.

Every time, it played out the same way: a company adopts a new technology, productivity actually gets worse for a while, then slowly recovers, and eventually takes off. Brynjolfsson called it the J-curve:

Technology adopted

New capabilities deployed across the firm

→

Productivity trough

Returns lag 3–5 years; early adopters underperform

→

Back to baseline

Processes, roles, and structures rebuilt around the technology

→

Compounding returns

Complementary changes unlock accelerating productivity gains

Anyone who has implemented a new system recognizes this pattern: new technology often hurts before it helps. Learning a new tool or rebuilding a process always slows you down before it speeds you up. What is striking is seeing that same curve in national statistics. And now we're watching it unfold again with AI.

The AI J-Curve

In the 2010s and early 2020s, AI had its own productivity paradox. Despite the incredible advances of machine learning around computer vision, prediction, etc, the macro productivity numbers were low.

But after tracking tens of thousands of US manufacturers, McElheran, Yang, Kroff & Brynjolfsson's 2025 Census demonstrated the J-curve at the firm level. The study tracked adoption of machine learning and data science systems (what they term "AI") across manufacturing from 2017 to 2021. The firms that recovered fastest were those already digitally mature before adoption.

McElheran et al. (2025) View article

Sample: Tens of thousands of US manufacturers (Census) Period: 2017–2021 Intervention: AI adoption

AI adoption initially cuts productivity, but adopters outperform non-adopters within four years, especially those already digitally mature.

−1.33pp

Initial Productivity Drop

AI adoption initially reduced productivity by 1.33 percentage points as legacy routines collided with new tools.

⅓

From Management Decline

For older firms, nearly a third of losses came from declining management practices during the transition.

4yr

Recovery Window

Over a four-year window, AI-adopting firms outperformed non-adopters in both productivity and market share.

This whitepaper mainly examines machine learning and data science, things like predictive analytics, computer vision, NLP classification. But as Erik Brynjolfsson noted in that February 2026 interview, we're already into the next wave: agentic AI systems capable of reasoning, planning, and autonomous execution. While Brynjolfsson doesn't make it explicit in the interview, the productivity upturn could be a combination of two overlapping curves: the slower J-curve from 2010s machine learning adoption finally turning upward, and a highly accelerated J-curve from agentic systems that began more recently.

What Brynjolfsson showed in Compustat data in the 1990s, and what the Census now shows in manufacturing plants, may now be visible in aggregate GDP statistics.

Four Lessons for the Mid-Market

They say if you ask ten economists a question you get eleven opinions. I think the same is true for software developers - I have many opinions on how to get the most out of technology so it's fascinating to see what actual comes out of the data. That's why studies like McElheran et al. are so valuable. It adds a layer of objectivity that can only come from studying 10,000 firms. The study identifies four factors that flatten the J-curve for mid-market firms, plus a fifth lesson for multi-location companies.

1. Commit to Growth Strategy

What the study found

"Firms with more strategic emphasis on growth through market expansion and innovation ('new markets strategy') tend to exhibit significantly lower initial productivity losses."

What this means for you

If you're treating AI as a cost play, you're missing the point. Growth creates the volume that makes AI's scale-free economics pay off. Expand your addressable market, invest in product innovation, and use AI to scale faster than competitors, not just to cut headcount.

2. Act Like a Young Company

What the study found

"After controlling for the interaction of firm age and AI adoption, the main effect of AI adoption on productivity is actually positive and statistically significant. These losses are unevenly distributed, concentrating among older businesses."

What this means for you

Old firms bore the worst losses; young, growth-oriented firms barely dipped at all. If you're an established company, you need to deliberately act young: move faster, experiment cheaper, rebuild processes without being constrained by legacy decisions. If you're already a young mid-market company, leverage your natural advantage. You can outmaneuver larger competitors still trapped in their own bureaucracy.

3. Maintain Management Discipline

What the study found

"Among older establishments, abandonment of structured production-management practices accounts for roughly one-third of these losses... more intensive AI adoption causes a de-adoption of structured management practices, concentrated in old establishments."

What this means for you

Do not let AI adoption become an excuse to stop measuring what matters. KPI reviews, production targets, and frontline feedback systems are not "old school." They're how you maintain control during the transition. The firms that kept their management cadence intact recovered fastest. This is an avoidable loss.

4. Invest in Digital Infrastructure First

What the study found

"AI adoption initially cuts productivity, but adopters outperform non-adopters within four years, especially those already digitally mature."

What this means for you

If you're still running on-premise servers, manual reporting, and spreadsheet-based planning, fix that before you pilot AI. Firms already using predictive analytics and cloud infrastructure experienced smoother adoption. Digital readiness reduces the co-invention burden. You cannot skip steps.

Research References

Study	Years Covered	Population	Effect Size
Brynjolfsson & Hitt: Computing Productivity (2003)	1987–1994	Large US firms (Compustat panel data)	5–6% higher productivity; market value of IT capital often 2–3× replacement cost; long-run returns up to 5× short-run estimates
Brynjolfsson, Rock & Syverson: The Productivity J-Curve (2019)	1995–2016	US economy	General purpose technologies require intangible complements; measured productivity dips then rises
McElheran et al.: The Rise of Industrial AI in America (2025)	2017–2021	Tens of thousands of US manufacturers (Census data)	Initial productivity drop of −1.33pp; adopters outperform over 4 years; growth strategy, digital maturity, and management discipline flatten the curve

AI Is Making Us More Productive and the Macro Economy Is Noticing

Jackson Rudd

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