October 05, 2026

Total factor productivity (TFP) measures how much output the economy produces from a given quantity of labor and capital. It is what remains of growth after accounting for more hours worked and more machines installed. When firms produce more without adding inputs, TFP rises. For that reason it is the closest available proxy for technological progress and for genuine improvements in how inputs are combined. Labor productivity, or output per hour, is a different concept. It can rise for three reasons: workers are given more or better equipment (capital deepening), the composition of the workforce shifts toward higher-skilled labor or TFP improves. Only the third reflects true innovation. Giving every employee a second monitor lifts output per hour without making the firm any smarter about how it operates. That distinction is central to the AI debate. Hundreds of billions of dollars are flowing into data centers, chips and model training, and that capital deepening should mechanically lift output per hour. The harder question is whether AI is also raising TFP, meaning whether it is making the economy fundamentally more efficient. So far, the data says no. The chart below shows utilization-adjusted TFP from the San Francisco Fed, and it is currently sitting slightly below zero with no sign of acceleration since the AI capex cycle began. Output per hour, by contrast, is running near 2.5%, comfortably above its post-2005 average, and that strength is exactly what gets cited as evidence that AI is already working. But strong output per hour alongside flat TFP is the signature of capital deepening, not of a technology shock. TFP has swung between roughly -3% and +4% over the past 40 years with no discernible trend, so the current sub-zero reading is not in itself unusual. Electricity and IT both took a decade or more to show up in aggregate numbers. The bottom line is that the AI boom is clearly visible in investment data and in equity valuations, but it is not yet visible in the productivity statistics, which means the productivity payoff from AI remains a forecast rather than an observation.

October 04, 2026

When rates are high, builders build less, and when fewer homes and apartments get built, rents go up, which pushes inflation higher and keeps rates high. Call this the "higher rates, higher rent doom loop." With owners' equivalent rent alone making up roughly a quarter of the CPI basket, this re-acceleration in rents is a problem for the Fed because it puts upward pressure on inflation driven by higher rates.

October 03, 2026

This new paper from Anthropic finds that robots will only be able to replace 300,000 jobs in the US economy. For comparison, total employment in the US economy is 160 million. Even under aggressive projections, widespread displacement of physical labor will take decades, and historical cost decline rates suggest it would take 40 years to reach cost parity for even 10% of jobs. Consider nursing and general repair, where present-day robots can do almost none of the work, or the electrician threading cable through a finished wall and the home health aide lifting a frail patient, tasks that look routine to an outsider and remain close to untouchable in practice. In fact, much of the automation that will arrive this decade has nothing to do with large language models. Car washes that scan vehicles to aim their sprayers, warehouse sortation lines and autonomous vehicles all descend from sensing and control work that was well underway before large language models arrived and would almost certainly have happened anyway. The bottom line is that the hardest physical work is harder to automate than the consensus assumes, and the automation we do get will owe more to decades of mechanical engineering than to the current moment in AI. Almost all jobs are bundles of simple and complicated tasks, so robots that can handle the simple part still cannot do the job, which is why, for the vast majority of workers, the mess is the moat. For investors, the conclusion is that job losses in the economy will be modest, held back by cost, by the fine manipulation robots cannot manage, by regulation and by a simple human preference for human hands. Displacement of workers is also only one side of the ledger. US business formation stands at the highest level in the nation's history, new firms are where new jobs come from and the net effect on employment is likely to be positive by a wide margin. The conclusion is that robots are not coming for your job, because very few jobs are a single automatable task. In other words, your job is a mess.