AI is boosting output but not economic efficiency. We investigate where the productivity gains are going — and who’s really benefiting.

There’s a very specific kind of frustration happening right now that nobody seems to want to name out loud. AI tools are everywhere. Your coworker finished that report in twenty minutes. A developer shipped three features in a day. Entire marketing decks are being produced before lunch. By every individual measure, people are doing more, faster.

And yet — the economy isn’t actually getting more efficient. So where exactly is all that productivity going? Two curious things are happening at the same time in 2026. On one hand, economic expansion is still going strong despite job growth slowing to a trickle, suggesting productivity among those currently employed is rising. But by many measures, productivity growth has barely budged in recent years, and actually slowed in the first quarter of 2026. Those two realities cannot logically coexist — and yet here we are.

There are two primary metrics economists use to gauge productivity, and the two are pointing in completely opposite directions. One is labor productivity, which measures output per unit of labor. The other is total factor productivity (TFP), a broader metric that encompasses how efficiently the entire economy converts inputs into output. Labor productivity has seen solid gains in recent years, but TFP has struggled to post significant growth since a post-pandemic surge. Translation: individual workers are doing more, but the economy as a whole is not becoming more efficient. Something is absorbing all that gain before it reaches the rest of us.

Part of the answer is in the data itself. The “economic fog” is thickening — our statistics struggle to convert nominal spending into “real” output when quality is improving rapidly or when output is a digital good. Government agencies find it difficult to measure the productivity of AI that writes code or diagnoses diseases because these contributions do not fit neatly into existing industry categories or price indexes. In other words, the tools we use to measure progress were built for a different economy. They weren’t designed to track what happens when an entire workday gets compressed into a few prompts.

But there’s a more uncomfortable explanation. The gains may not materialize in aggregate productivity statistics yet because workers are capturing efficiency gains primarily as on-the-job leisure, rather than increasing their output. AI saves you two hours; you use those two hours to breathe, scroll, or pace yourself. You don’t produce more — you just suffer slightly less. Which is honestly valid. But it also means the grand productivity revolution is, for now, functioning more like a collective sigh of relief than an economic engine.

Meanwhile, look at where the real money is going. The “hyperscalers” — the massive tech companies providing cloud and AI infrastructure — are driving an unprecedented spending boom. Analysts revised their 2026 capital expenditure expectations for these tech giants to an astonishing $667 billion, a 24% increase from just the start of the earnings season and representing a 62% jump compared with 2025. That’s not investment in your city’s startups or your country’s youth employment pipeline. That’s Amazon, Google, Meta and Microsoft building the infrastructure of the future and keeping the returns firmly inside their own ecosystems.

Nearly three-quarters (74%) of AI’s economic value is captured by just one-fifth (20%) of organisations, revealing a stark and widening divide between a small group of AI leaders and the majority of businesses still stuck in pilot mode. Think about what that means in practice. The gains are real — they’re just not being shared. Most businesses, most workers, most countries are not in that 20%. They’re running AI tools and seeing moderate wins while a very small group of players captures the structural upside.

Goldman Sachs confirmed it plainly: “We still do not find a meaningful relationship between productivity and AI adoption at the economy-wide level.” Not a lag, not a measurement issue, not a rounding error — no meaningful relationship. And yet the narrative machine keeps telling us the revolution is imminent, that we just need to be patient, that the gains are coming.

IMF Managing Director Kristalina Georgieva described AI as a “tsunami” hitting the labor market, with the potential to transform or eliminate 60% of jobs in advanced economies and 40% globally. While the top tier of workers sees wage growth and the bottom tier sees increased demand for manual or local services, the middle class is getting squeezed. The IMF’s research indicates two primary areas of concern, one being stagnating middle-class wages, as jobs that are not enhanced by AI are beginning to pay less in relative terms. The shape of this is already visible. Not mass unemployment. Not utopia. Just a quiet hollowing out of the middle.

For East Africa, the stakes are particularly sharp. For Kenya, often branded as Africa’s Silicon Savannah, the findings raise urgent questions about whether the country is building genuine AI competitiveness or merely becoming a consumer market for technologies developed in the United States, China and Europe. Much of Africa’s digital economy still depends heavily on foreign-owned cloud infrastructure, imported hardware and externally developed AI systems. That dependence has sparked growing concerns about digital sovereignty, with experts warning African countries could become consumers of AI products without controlling the underlying technology, data or profits. Being productive on someone else’s platform, using someone else’s model, generating gains that flow back to someone else’s shareholders — that’s not transformation. That’s upgraded dependency.

East African business leaders are calling on organizations to urgently scale up AI adoption and workforce training, warning that companies that delay risk losing competitiveness in a rapidly evolving digital economy. The urgency is real. But urgency without direction can lead you straight into a trap. The question isn’t just whether your team is using AI — it’s whether the value your team creates with AI stays anywhere near you.

Workers’ regular AI use increased 13% in 2025, but confidence in the technology’s utility plummeted 18%, indicating persistent distrust. That gap — between adoption and belief — is telling you something. People can feel when a tool is working for them versus when they’re working for the tool. The productivity is happening. The prosperity isn’t. And until those two things reconnect, the AI story is less revolution and more a very sophisticated way for the already-powerful to pull further ahead while the rest of us marvel at how fast we can now do our emails.