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Is AI 1996 or 1999? The June FOMC and What Comes Next

Wed, 17 Jun 2026 18:00:00 +0900

Context
This note follows our work on AI productivity evidence, the mid-cycle AI supercycle, and the June macro event cluster. The question here is narrower: does AI look more like a 1996-style productivity disinflation story, or a 1999-style expectations and CapEx cycle?

TL;DR

AI today looks less like a clean 1996 productivity-disinflation regime and more like a 1999-style expectations and CapEx cycle with a 1996 productivity option embedded inside it.
Workplace productivity evidence is real. Adoption is rising. But broad macro disinflation from AI has not yet been proven.
AI data centers, chips, power, cooling, land, labor, and credit demand are already visible. These are current macro inputs for the Fed.
The base case for the June FOMC is a hold, but not a dovish hold. It is more likely to be a hawkish hold or hawkish neutral outcome.
The statement matters, but the key signals are the SEP dots, the longer-run neutral-rate dot, Chair Warsh’s comments on AI and r-star, and the July 8 minutes.

Core View

The Fed is unlikely to pre-emptively treat AI as a rate-cutting disinflation force. Productivity is an option. CapEx, power demand, semiconductors, and financial conditions are current facts.

1996 vs 1999

The 1996 version of the story is positive supply. Productivity rises, unit labor costs slow, inflation pressure eases, and the Fed can be patient even with strong growth.

The 1999 version is different. The productivity story may be real, but asset prices, corporate investment, and capital-market expectations run ahead of realized diffusion. The Fed then has to watch demand, credit, valuation, and financial conditions, not only future productivity.

AI has both elements. The 1996 option is real. Brynjolfsson, Li, and Raymond’s Generative AI at Work finds a roughly 15% productivity gain for customer-support agents. Fed FEDS Notes shows AI adoption has moved beyond experimentation, and the San Francisco Fed argues policymakers need micro and sector evidence before the macro aggregates fully show the transformation.

But the immediate macro shock looks more like 1999. AI infrastructure spending is pulling on chips, power, data centers, cooling, land, skilled labor, and financing. The April FOMC minutes already treated AI as two-sided: possible productivity disinflation over time, but also AI-related investment that could raise input costs now.

Why This Matters for the June FOMC

The June FOMC is scheduled for June 16-17, with the decision at 2:00 p.m. ET and press conference at 2:30 p.m. ET on June 17. The minutes are scheduled for July 8. The current target range is 3.50-3.75%.

Recent data do not give the Fed an easy easing case:

Indicator	Latest reading	Policy read
May CPI	+0.5% MoM, +4.2% YoY	Headline inflation uncomfortable
May core CPI	+0.2% MoM, +2.9% YoY	Better monthly core, still above target YoY
April PCE	+3.8% YoY	Fed’s preferred inflation gauge still high
April core PCE	+3.3% YoY	Core still too high
May payrolls	+172k	Labor market not weak
May unemployment	4.3%	Stable

The market already expects a hold. The real issue is what kind of hold.

Base Case

My base case is a hawkish hold.

Item	Base case
Policy rate	Hold at 3.50-3.75%
Statement	Data dependent, inflation still elevated
Easing bias	Weakened or removed
2026 dot	Moves toward no cuts in 2026
Longer-run dot	Holds at 3.1% or edges higher
Press conference	AI productivity is possible, but not a current policy basis
Minutes	More hawkish than the statement

The March SEP had a 2026 federal funds median of 3.4%, roughly implying one cut from the current midpoint. If the median shifts toward 3.625%, markets should read it as “no 2026 cuts.”

Scenario Map

Scenario	Probability	What happens	Market read
Base: hawkish hold	65%	Hold, weaker easing bias, higher 2026 dots	Short rates supported, dollar firm, equity multiples capped
Hawkish surprise	25%	Some hike dots, longer-run dot up, r-star language	Front-end yields rise, risk assets volatile
Dovish surprise	10%	One-cut median stays, energy shock treated as temporary	Yields fall, dollar eases, risk relief

What to Watch

The key post-FOMC checklist:

Signal	Dovish	Hawkish
2026 median dot	Stays near 3.4%	Moves toward 3.625%
Longer-run dot	Stays 3.1%	Moves above 3.1%
AI language	Productivity and disinflation	CapEx, input costs, r-star
Warsh tone	Patience toward headline inflation	Credibility and elevated inflation
July 8 minutes	Productivity gains emphasized	Input costs, asset valuations, policy firming

Market Implication

This is not a stock-picking note. At the macro level, the message is:

AI can raise long-run earnings potential
but if it also raises r-star and real rates
the same future earnings deserve a lower multiple

That is the tension. AI can be good for growth and still bad for rate-cut hopes.

Final View

AI may eventually become a 1996-style productivity disinflation force. But for this FOMC, the observable facts are closer to 1999: CapEx, power demand, input costs, financial conditions, and expectations are moving before economy-wide productivity disinflation is proven.

AI being good is not the same thing as rates going down. Until productivity shows up in broad unit labor costs and core services inflation, the Fed is more likely to treat AI as a neutral-rate and investment-demand issue than as a reason to ease.

Sources: Fed June calendar, Fed July calendar, April FOMC minutes, March SEP, Governor Cook on AI, BLS CPI, BLS jobs, BEA PCE, QJE Generative AI at Work, Fed FEDS Notes, Kansas City Fed, San Francisco Fed.

Is AI Productivity Real? From 15% Workplace Gains to Macro Diffusion

Wed, 17 Jun 2026 10:30:00 +0900

Context
This note follows our prior work on the AI supercycle, AI data-center capex, memory demand, and rate risk. The question here is more basic: is AI actually raising productivity, and is that effect already visible at the macro level?

TL;DR

The right answer is micro yes, macro not yet fully confirmed.
Brynjolfsson, Li, and Raymond’s Generative AI at Work finds that AI assistance raised customer-support agents’ productivity by about 15% on average, with the largest benefits for less experienced and lower-skilled workers.
The Fed’s FEDS Notes shows that AI adoption is no longer trivial: about 18% of firms had adopted AI by year-end 2025, work-related GenAI usage among individuals was around 41%, and employment-weighted survey estimates suggest that 78% of workers were at AI-adopting firms and 54% at firms using LLMs.
The Kansas City Fed finds that U.S. labor productivity has moved above its pre-pandemic trend since late 2022, but the pickup is not broad-based. AI adoption aligns with faster productivity growth across industries, but explains little of the aggregate shift so far.
The San Francisco Fed’s policy lesson is that aggregate productivity, labor, and inflation data are not enough. Policymakers and investors need disaggregated evidence and business-level signals before the macro data fully reveal the transformation.

Core Takeaway

AI productivity is not a yes-or-no debate. It is a diffusion timeline. The workplace evidence is real, adoption is rising quickly, but economy-wide productivity gains are still narrow and uneven.

The Four Sources Fit Together

Source	Layer	Main question	Core answer
Generative AI at Work	Workplace task	Does AI raise productivity on the job?	Yes, in specific workflows
Fed FEDS Notes	Adoption	How widely is AI used?	Meaningfully, but unevenly
Kansas City Fed	Industry productivity	Is the U.S. productivity pickup AI-driven?	Related, but not fully explained by AI
San Francisco Fed	Policy	What should policymakers watch?	Micro and sector data, not only aggregates

The combined message is straightforward:

AI raises productivity in real workplace settings
→ adoption is spreading
→ macro productivity gains are still narrow
→ policy and investment should track diffusion quality

Workplace Evidence: What AI Actually Changed

The QJE version of Generative AI at Work studies 5,172 customer-support agents at a Fortune 500 firm using a GPT-3-based conversational assistant. The tool offered real-time response suggestions that agents could accept, edit, or ignore.

The headline result is a roughly 15% increase in issues resolved per hour. But the deeper result is distributional. Lower-skilled and newer workers benefited most, while experienced high performers saw smaller speed gains and some quality trade-offs.

This matters because AI did not merely make the best workers even better. It compressed skill gaps.

Finding	Economic meaning
Larger gains for less experienced workers	Faster onboarding and lower training cost
Text style shifted toward high-skill agents	Tacit knowledge became more transferable
Customer sentiment improved	Quality and work experience also changed
Stronger effects for moderately rare issues	AI helps when the firm has patterns but humans lack repeated exposure

This is not full automation. It is workflow augmentation. The first-order effect is not “remove the human”; it is “raise the average worker’s output and quality.”

Adoption: AI Is Already Inside the Workplace

The Fed’s FEDS Notes reconciles three different survey lenses. The numbers vary because they use different units, but together they show that AI has moved beyond experimentation.

Estimate	Unit	Interpretation
18%	Firm count	Share of firms reporting AI adoption at year-end 2025
41%	Individuals	Workers reporting work-related GenAI usage
78%	Employment-weighted firms	Workers employed at AI-adopting firms
54%	Employment-weighted LLM firms	Workers employed at firms using LLMs

The high employment-weighted numbers matter. Large firms employ many workers, so AI adoption by large organizations can reach a large share of the workforce even if the firm-count adoption rate looks modest.

The industry pattern also matters. AI adoption is strongest in finance and professional services, the high-value cognitive work sectors where research, analysis, compliance, coding, legal drafting, accounting, and client communication are core workflows.

Macro Productivity: Promising but Not Broad-Based Yet

The Kansas City Fed provides the necessary caution. U.S. labor productivity has strengthened since late 2022 and moved above its pre-pandemic trend. That period overlaps with the commercial emergence of widely used generative AI tools.

But the pickup is not broad-based. A relatively small set of industries accounts for much of the improvement. Higher AI adoption is associated with faster productivity growth across industries, but it explains little of the aggregate productivity shift so far.

That is not a bearish conclusion. It is an early-diffusion conclusion.

Task-level gains
→ firm workflow redesign
→ industry-level adoption
→ macro productivity data

The economy is still moving through the middle of that chain.

Policy Lesson: Do Not Wait for Aggregates

The San Francisco Fed frames the AI moment through the historical lens of the 1990s productivity acceleration. The lesson is not that AI will repeat the 1990s exactly. The lesson is that aggregate data often lag transformative business change.

For monetary policy, AI is complicated because it can create short-term demand pressure and long-term supply benefits at the same time.

Channel	Short-term effect	Long-term possibility
Data-center capex	Raises demand for power, equipment, land, financing	Builds productive capacity
AI tools	Implementation costs and disruption	Lower unit labor cost
Labor market	Task displacement and job redesign	Higher output per worker
Inflation	Infrastructure bottlenecks	Disinflation from productivity

This is why the Fed cannot simply wait for aggregate productivity to settle the debate. It needs firm-level and industry-level signals.

Investment Read-Through

For investors, the key implication is that AI capex can still be justified, but not by model benchmarks alone. It needs workflow productivity.

Positive evidence:

real task-level productivity gains
rising work-related adoption
early penetration into high-value cognitive sectors
signs of tacit knowledge transfer and faster onboarding

Remaining risks:

usage does not equal value creation
workflow redesign takes time
aggregate productivity gains remain narrow
capex may outrun proven ROI
quality, compliance, and liability issues can slow adoption

For Korea, this supports the long-term logic behind AI infrastructure: HBM, server DRAM, eSSD, networking, power equipment, data-center construction, AI cloud, and enterprise workflow software. But it also raises the bar. The market should reward companies that turn AI into repeatable productivity workflows, not companies that merely attach AI labels to old businesses.

Final View

AI productivity is real at the task level. Adoption is high enough to matter. But the macro productivity revolution is not yet fully proven.

That is exactly why the next phase of the AI cycle is about diffusion, not demo performance. The question is no longer whether AI can answer questions. The question is whether it changes workflows deeply enough to show up in margins, output per worker, and eventually the national productivity data.