The Daily Spark

A new survey by Mavvrik and Benchmarkit of 396 enterprises finds that companies are adopting AI faster than they are building the financial controls to keep its costs in check.

Only 11% of firms forecast their AI spending within 10% of actual, down from 15% last year, see chart below.

The survey also shows that those misses are changing decisions, with 62% of firms saying an unexpected AI cost altered a business decision and one in four delaying or canceling an AI initiative.

The impact is showing up in profitability too, with 81% of companies reporting moderate-to-high gross margin impact from AI costs, including 63% who see a 6 to 15 percentage point decrease.

The bottom line is that we are still waiting for the ROI from AI investments, and this survey shows that while we wait, costs continue to surprise and become harder to predict and control.

For more see here and here.

US 10-year Treasury yields have risen by about 100 bps since the beginning of the year. A model developed by Fed economists D’Amico, Kim and Wei decomposes Treasury yields to show what is driving the move. Updated estimates released point to two main factors:

1. The expected path of real short rates has moved higher. The model estimates that the average real short rate expected over the next 10 years has increased. Lower oil prices or weaker growth could ease some of this pressure and help bring longer-term rates down.

2. The term premium has risen. The term premium is the additional yield investors demand to hold a long-dated bond beyond what the expected path of Fed policy would imply. It can therefore capture compensation for longer-term risks, including concerns around fiscal sustainability or Fed credibility.

The bottom line is that higher long rates this year reflect both the higher expected policy path from the Fed and investors asking for more compensation to hold duration.

Written by Allison Boxer

The US and Europe have fundamentally different roles in the AI economy. One is building it, and the other is using it. The US dominates on every supply-side measure: hyperscaler capex approaching $800 billion in 2026, data center capacity three times that of Europe and the dominant share of global AI models and semiconductor value, see the first three charts below.

Yet on adoption, several European countries are actually ahead of the US, and many European firms are using AI in their businesses (see the fourth and fifth charts). This suggests Europe is an eager user of infrastructure it did not build and cannot easily replicate.

The bottom line is that the US is the dominant AI builder, but both the US and Europe are AI adopters. For more, see our new chart book: The macro implications of the AI investment boom: US and Europe

Written by Huw van Steenis

The gap between the 90th- and 10th-percentile public core bond managers has narrowed from 1.5% a year in the 2010s to 1.1% since 2020, see the first and second charts below. Dispersion now spikes mainly in crises and quickly falls back.

This convergence comes on top of persistent underperformance by public fixed income managers. For example, data from S&P shows that over the past 10 years, the majority of active Treasury fund managers have underperformed their benchmarks, see the third chart below.

The bottom line is that with little difference between top and bottom managers in public fixed income, and almost all of them trailing their index, public core fixed income has become essentially a beta allocation for both institutions and households.

Here are five new interesting papers that are relevant for the ongoing conversation in markets.

1. Decoupling Direct Investment: American Firms' Retreat from China
   • The author documents evidence that US-China linkages are declining across channels. New project announcements and US acquisitions of Chinese companies are down, and American multinationals are shrinking their existing operations. Official statistics understate the extent of the retreat because of how much US investment is routed through Hong Kong.
2. The Early Impacts of AI on Employment among Recent College Graduates
   • Using CPS microdata, the authors analyze the effects of AI on recent college graduate unemployment through August 2026 to argue that AI is still not having a material impact on the US labor market. They find that summer 2026 unemployment trends for recent college graduates follow typical seasonal patterns, and no evidence of any significant displacement of recent college graduates. The authors do find some limited evidence of higher unemployment in telework-focused roles.
3. The Macroeconomic Effect of AI: Sizing the Software Engineering Channel
   • The authors use stock market returns and Revelio Labs data to estimate a market-implied expectation for AI productivity. Between November 2022 and December 2025, market expectations implied the equivalent of a permanent 32.6% increase in software engineering productivity, worth 3.6% of GDP. By mid-2026, amid rapid improvement in AI coding agents, the estimated effect had more than doubled. I am slightly skeptical how well this is capturing productivity versus some other kind of AI market factor, but I thought this was quite an interesting approach regardless.
4. Will AI Help or Hurt Work and the Economy? What People Around the World Expect
   • This paper summarizes the findings of a survey of more than 64,000 adults in 32 countries taken in April and May 2026. The surveys finds that workers are considerably more likely to expect AI to help their jobs than to threaten them, in contrast to the narrative in the US about AI-driven job displacement. Survey respondents who expect AI to help anticipate stronger growth and employment, while those who see it as a threat expect weaker employment and higher inflation.
5. Ratings, Debt, and Deficits: An Exploration
   • The authors compare a simple model of debt sustainability with actual sovereign credit ratings. They find that ratings agencies give far more weight to debt levels than projected fiscal balances, and they rely heavily on country-specific factors. As a result, countries with the same debt and fiscal outlook can receive very different ratings.

Written by Allison Boxer

AI's rate-insensitive boom is triggering "Dutch disease" in the US economy by draining capital, power and labor from vulnerable sectors like housing and autos.

Trapped between inflation driven by the AI boom and weakness in rate-sensitive industries, the Fed will have to keep interest rates higher for longer, see chart below.

Because monetary policy cannot resolve this sectoral imbalance, the only durable fix is expanding the supply of power, chips and infrastructure.

For more discussion, see also here.

We've put together a chart book on France and the ECB, it is available here.

It shows French spreads over Germany at their widest since the 2011 euro crisis, with fiscal pressures building ahead of the October 13 budget debate.

At the same time, the macro backdrop remains solid, European banks are in their best health in decades and the ECB has several options available should financial stability be threatened.

Hyperscalers have raised $48 billion in bonds in European currencies this year, which is already more than triple the entire amount of 2025. This is spread across €27 billion, £13 billion and CHF7.5 billion.

Hyperscalers now account for 3% of euro IG issuance, 10% of sterling IG issuance and 22% of Swiss franc IG issuance this year, compared with 8% of US IG corporate issuance, see the first chart below.

There are few signs of crowding out, yet. Non-hyperscaler IG issuance remains strong and is little changed YoY, and corporate spreads remain tight. Rather, hyperscaler issuance is adding highly rated US borrowers (AA- or higher) to European credit indices that have traditionally had more A to BBB exposure.

Hyperscalers account for 7% of euro issuance with maturities of 10 years or more, compared with 3% across all maturities. They also account for 3% of the 10-year-plus euro IG index, compared with 1% of the overall index. This is adding some additional depth for longer-dated maturities, which has been thin outside of sovereign issuance, and what’s more, some governments (like the UK) have been shortening duration of bond issuance, see the second and third charts below.

The bottom line is that the scale of hyperscaler financing, which is forecast to rise a further 25% in 2027, will have an even bigger impact on European bond markets, with hyperscalers becoming a larger part of European credit indices. The scale also calls for tapping all markets, public and private.

Written by Huw van Steenis

Several forces suggest rates may peak within the next month:

1. Approaching midterm elections raise the odds of a Middle East deal that would lower oil prices.
2. Governments in the US and Europe are adding oil supply to bring prices down before voters go to the polls.
3. Higher rates are biting harder and harder on the interest-rate-sensitive parts of the economy, in particular housing and autos.
4. More questions are being raised about the AI buildout, as seen in the ongoing widening of CDS spreads for AI-related companies.

With 84% of global fixed income now yielding more than 3%, up from just 12% in January 2022, a deal or government action that lowers oil prices would ease the upward pressure on interest rates at a time when yields are already elevated across nearly the entire global bond market.

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.