The Daily Spark

Treasury issuance in 2026 has a weighted average maturity of just 1.7 years. For US corporate bonds, it’s almost 10 years, see chart below.

One reason companies are issuing long is the scale of today’s buildout in long-duration assets like data centers, power generation, grid infrastructure and manufacturing facilities, which are financed over the long term, not quarter to quarter. For investors with obligations that stretch out for decades, like insurers and pension funds, the search for duration leads naturally to different types of credit.

The bottom line is that when the government issues short and companies issue long, the investors who need duration most will go where the duration is.

With AI disruption potentially around the corner, there is a lot of discussion about moats at the moment.

Professor Aswath Damodaran’s framework looks at the sources of competitive advantage: brand strength, switching costs, network effects, cost advantages and legal protection, see chart below.

The key distinction is not simply whether a company has a moat, but how wide and durable that moat is, and how it shows up in the numbers: pricing power, customer retention, market share and profitability.

For investors, understanding the source and sustainability of a company’s competitive advantage is critical to assessing its long-term value.

With the proliferation of AI, the moat discussion is becoming even more important. Here is a simple checklist of three questions to ask.

The AI moat checklist: Data, Workflow, Pricing

1. Data: Does the company own data its rivals can’t get?
2. Workflow: Is the company’s product part of how customers do their jobs every day?
3. Pricing power: Can the company charge more without customers walking away?

Social Security is financed mainly by a 12.4% payroll tax on wages up to an annual cap, split between workers and employers, with additional revenue from income taxes on benefits and interest on trust fund reserves.

Since costs now exceed income, the retirement trust fund is being drawn down and is projected to be depleted in late 2032, according to the 2026 Trustees Report, see chart below.

Once reserves run out, the program can pay only what it takes in, about 78% of scheduled benefits, a cut of roughly 22% unless Congress acts. Combining the retirement and disability funds, which would require legislation, would delay depletion to 2034, with about 83% of benefits payable.

Closing the gap would require raising revenue (a higher payroll tax rate or taxable wage cap), reducing benefits (a later retirement age or slower cost-of-living adjustments) or a combination of both.

The bottom line is that a possible 22% benefit cut makes it more important for households to save more and build a stream of income in retirement.

Data from the San Francisco Fed shows that core PCE inflation at 3% is split evenly between supply and demand, see chart below.

In 2022, supply was the largest driver.

The rise in the demand contribution since 2024 suggests inflation is no longer just a supply story. That matters for the Fed, because interest rates can cool demand-driven inflation but do little about supply shocks.

Every housing market has a long-term average for how much new rental housing gets built each year, measured as a percentage of its existing stock. For example, a metro might typically add about 2% to its apartment inventory annually. During the construction boom, many markets ran well above their own average, creating the “supply wave” that held down rents.

The equilibrium date is the point where a market’s new supply rate falls back to its own long-term average. Before that date, supply is above normal and can hold down rents. After it, supply is at or below normal, so that puts upward pressure on rents.

The chart below shows that only Tampa, Philadelphia and Austin are still forecasted to have above-average rental supply. With new buildings taking 12-18 months to lease up, the fading supply wave is set to put upward pressure on rents and, by extension, on shelter inflation into 2027. This complicates the Fed’s task, with inflation already running above its 2% target.

For more discussion, see our Daily Spark on the higher rates, higher rent doom loop.

Japan Weeks (Oct. 26-30), now in its fourth year, brings global investors and asset managers together to explore the opportunities in Japan’s financial and capital markets. One of the most important themes behind that opportunity is shareholder activism, which is reshaping corporate Japan. Shareholder proposals have more than tripled since 2018, reflecting a stronger focus on governance and shareholder value, see first chart below. This is starting to translate into stronger corporate profits, with profits at Japan’s medium-sized businesses (those with capital of JPY 100 million to 1 billion) reaching historically high levels, a sign that improving profitability extends well beyond the country’s largest companies, see second and third charts.

Despite massive investments and widespread hype around artificial intelligence, the technology has delivered zero financial payoff to corporate America outside of tech. Revenue per worker is rising for the Mag 7 but is flat for the S&P 493, see the first chart below. Profit margins are rising for the Mag 7 but are flat for the S&P 493, see the second chart below. And there are still no signs of AI boosting productivity growth, see the third chart below. With no impact on the bottom line, mainstream corporate America is pulling back and starting to view token spending not as a source of growth but as a cost center that weighs on margins.

VC-backed companies are spending more and more on tokens, but mainstream corporate America does not need the latest model and is unwilling to pay for it. As long as agentic AI and token spending are confined to a small part of the economy, the macroeconomic effects of AI will be very limited, see the fourth and fifth charts below.

The question looking ahead is whether, even as models become commoditized, mainstream corporate America can turn increasingly cheap intelligence into higher revenue growth and higher profit margins. That may be harder than it sounds. AI may be a great calculator, but in business, intelligence is mostly judgment. Judgment depends on context, and the answer varies with which human or which model you ask. Perhaps most importantly, AI does not understand accountability, trust, culture, unwritten rules, ethical trade-offs or fairness, all of which shape reputational risk and ultimately drive revenue growth and profit margins.

The bottom line is that the AI story will be decided in the S&P 493, not the Mag 7, and so far, there is no evidence that revenue growth or margins are improving outside the tech sector.

The Fed survey below for the third quarter of 2026 shows that only 19% of firms report that financing costs or access to credit are constraining their investment and spending plans, while 81% report no such constraints. The bottom line is that corporate America is saying that monetary policy is not restrictive.

Ramp data show that 80% of the top 1% of AI spenders are VC-backed, compared with only 13% of the median spender, suggesting that the circularity seen among AI hyperscalers and model providers also extends to token spending, as the startups buying the most compute are paying for it with venture capital rather than organic earnings, see chart below.

If the AI payoff is slow to materialize or never arrives, VC returns would come under pressure, since the capital funding today’s token spending would have little to show for it, and a pullback in venture funding could in turn weaken demand for compute.

Compute demand is significantly greater than supply, so the speed of the data center buildout matters. Who are the winners and losers if the buildout takes longer?

Winners

• Owners of operating data centers. Capacity that is already online can raise rents and fill every megawatt, since new competitors cannot arrive quickly.
• Hyperscalers with capacity already secured. Those that locked in power, land and chips early can keep serving customers while rivals wait.
• Holders of powered land and grid access. Sites with secured power, interconnection and permits are the scarcest input and hardest to replicate, so their value rises.
• Suppliers of long-lead equipment. Makers of transformers, switchgear, generators and cooling gear keep long backlogs and pricing power, with lead times of up to 26 months for transformers.
• Power producers and on-site generation. Where the grid is the bottleneck, gas turbines, fuel cells and batteries become more valuable.

Losers

• Hyperscalers’ growth and earnings. Revenue is capped by supply rather than demand, and committed capex keeps accruing while capacity sits idle. Pricing power on existing capacity only partly offsets this, so earnings growth slows versus plan.
• AI labs, startups and enterprise buyers. They face rationing, higher prices and slower model training and product launches, with smaller players crowded out first.
• Developers and construction-stage investors. Cash flows start later while interest and carrying costs continue, lowering returns. Leveraged or pre-lease-dependent developers face covenant and refinancing stress.
• Equity investors in the AI trade. Valuations that assume on-time delivery are exposed to earnings revisions and multiple compression.
• The broader economy. Near-term GDP growth, which leans on AI investment, is lower, and the productivity gains from AI arrive later. Households and businesses on the same grid may also see higher electricity costs.

For chipmakers, the outcome of a delay is more mixed. Chip demand stays strong, but if sites cannot energize fast enough, chip deliveries could outrun installation and delay revenue.

The key distinction for investors is delay versus cancellation. A delay hurts returns by pushing cash flows out, but the asset remains and may become more valuable. The real risk is that scarcity unwinds, for example, if supply catches up in a rush or demand softens, which would erode the premium on existing capacity.

The bottom line for investors is that a delay would favor assets tied to scarce physical capacity, such as operating data centers, power producers and long-lead equipment makers, while assets priced on on-time delivery would underperform, notably AI-linked equities with high multiples and the debt of leveraged developers. Chipmakers face mainly timing risk, and hyperscaler bonds face mostly spread risk rather than default risk. The broader market would be exposed through the weight of AI names in major indices and through slower growth.