Welcoming a buffet: Biting off more than we can chew?

Demand side: cheaper utility is displacing frontier models and default workflows, for three reasons.

- We must still understand and control our own processes. For high-risk AI, the EU AI Act requires that the people overseeing a system can understand its capacities and limitations, monitor its operation, and override or reverse its output.
Source: EU AI Act, Article 14 (European Commission)
https://ai-act-service-desk.ec.europa.eu/en/ai-act/article-14

- Validating AI output is the bottleneck. In a randomized controlled trial, experienced developers using early-2025 AI tools took 19% longer to finish tasks than without them. Reviewing and correcting AI suggestions that were close but not quite right outweighed the gains.
Source: METR, July 2025
https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/

- Full automation is being pulled back. Gartner predicts over 40% of agentic AI projects will be canceled by the end of 2027, because of escalating costs, unclear business value or inadequate risk controls.
Source: Gartner press release, June 2025
https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027

Supply side: comparable capability is arriving in cheaper, smaller formats.

- Near-frontier performance now costs very little. GPT-3.5-level inference fell from $20 to $0.07 per million tokens between November 2022 and October 2024. The performance gap between top closed and open-weight models shrank from 8.0% to 1.7%.
Source: Stanford HAI, 2025 AI Index Report
https://hai.stanford.edu/ai-index/2025-ai-index-report

- Small, task-specific models are replacing general LLMs for business work. Gartner predicts that by 2027 their usage will be at least three times that of general-purpose LLMs, because general models lose accuracy on domain-specific tasks while smaller models are faster and use less compute.
Source: Gartner press release, April 2025
https://www.gartner.com/en/newsroom/press-releases/2025-04-09-gartner-predicts-by-2027-organizations-will-use-small-task-specific-ai-models-three-times-more-than-general-purpose-large-language-models

- Scaling is yielding less. Ilya Sutskever describes 2020-2025 as the "age of scaling" and argues that because pre-training data is finite, more resources no longer guarantee qualitative leaps.
Source: Dwarkesh Podcast interview, November 2025
https://www.dwarkesh.com/p/ilya-sutskever-2

- The payoff lies in downstream integration, not bigger models. MIT NANDA found 95% of organizations saw no measurable return from generative AI. It attributed the gap mainly to brittle workflows and poor fit with daily operations, not model quality.
Source: Fortune coverage of the MIT NANDA report, August 2025
https://fortune.com/2025/08/18/mit-report-95-percent-generative-ai-pilots-at-companies-failing-cfo

Conclusion: Frontier scaling faces diminishing returns, while the value shifts to optimizing and integrating current models: cheaper, smaller, and under human oversight.

COMPUTE BUYERS (most to least exposed)

CRWV - CoreWeave
Segment: GPU cloud
Exposure: Highest
Main risk: Customer concentration, heavy leverage
Offset: None; pure AI capacity

ORCL - Oracle
Segment: Cloud / AI infrastructure
Exposure: Very high
Main risk: Debt-funded capex, OpenAI contract dependence
Offset: Database and software business

META - Meta
Segment: AI capex (internal use)
Exposure: Moderate
Main risk: Write-downs if AI payoff disappoints
Offset: Ad business cash flow

MSFT - Microsoft
Segment: Hyperscaler
Exposure: Moderate
Main risk: Azure AI growth tied to OpenAI
Offset: Diversified software, strong cash flow

GOOGL - Alphabet
Segment: Hyperscaler
Exposure: Lower
Main risk: Capex scale
Offset: Own TPUs and models, flexible capacity

AMZN - Amazon
Segment: Hyperscaler
Exposure: Lower
Main risk: Capex scale
Offset: Broad AWS base, own Trainium chips


COMPUTE SUPPLIERS (most to least exposed)

SMCI - Super Micro
Segment: AI servers
Exposure: Highest
Main risk: Thin margins, tracks accelerator shipments
Offset: None significant

NVDA - Nvidia
Segment: Training GPUs
Exposure: Highest
Main risk: Volume and margin compression
Offset: CUDA ecosystem, inference share

000660 - SK Hynix (Korea)
Segment: HBM memory
Exposure: High
Main risk: HBM demand tied to high-end accelerators
Offset: Conventional memory

MU - Micron
Segment: HBM memory
Exposure: High
Main risk: HBM exposure, memory cyclicality
Offset: Conventional memory

VRT - Vertiv
Segment: Data center power / cooling
Exposure: High
Main risk: Build-out slowdown
Offset: Long project backlogs delay impact

AMD - AMD
Segment: AI GPUs / CPUs
Exposure: High
Main risk: AI share-gain story stalls
Offset: CPUs, gaming, inference chips

MRVL - Marvell
Segment: Custom ASICs / optics
Exposure: Moderate-high
Main risk: Data-center concentration
Offset: Custom inference chips

ANET - Arista
Segment: AI networking
Exposure: Moderate
Main risk: Slower AI cluster builds
Offset: General cloud networking

AVGO - Broadcom
Segment: Custom ASICs / networking
Exposure: Moderate
Main risk: Hyperscaler capex slowdown
Offset: Custom inference chips, VMware software

005930 - Samsung (Korea)
Segment: Memory / foundry
Exposure: Moderate
Main risk: Trails in HBM
Offset: Phones, displays, commodity memory

TSM - TSMC
Segment: Foundry
Exposure: Moderate
Main risk: Advanced packaging overcapacity
Offset: Makes chips for nearly everyone

LRCX - Lam Research
Segment: Equipment (memory-weighted)
Exposure: Lower, lagged
Main risk: HBM capacity expansion slows
Offset: Broad memory and logic demand

ASML - ASML
Segment: Lithography equipment
Exposure: Lower, lagged
Main risk: Leading-edge expansion pauses
Offset: EUV monopoly pricing

KLAC - KLA
Segment: Process control equipment
Exposure: Lower
Main risk: Fab capex slowdown
Offset: Needed at every node

AMAT - Applied Materials
Segment: Equipment (broad)
Exposure: Lower
Main risk: Fab capex slowdown
Offset: Mature nodes; China the bigger driver

SNPS / CDNS - Synopsys / Cadence
Segment: Chip design software
Exposure: Low
Main risk: Fewer frontier designs
Offset: More custom and edge chip designs

ARM - Arm
Segment: CPU architecture / royalties
Exposure: Low / possible beneficiary
Main risk: Data-center CPU slowdown
Offset: Edge and on-device inference growth

QCOM - Qualcomm
Segment: Mobile and PC chips
Exposure: Low / possible beneficiary
Main risk: Minimal
Offset: Leading on-device AI chips

INTC - Intel
Segment: CPUs / foundry
Exposure: Least
Main risk: Own execution, not this thesis
Offset: Possible edge upside


Based on business mix and contracts as of mid-2026, not current valuations. Not financial advice.

Some residual momentum at work providing some positioning

Pre or aftershock? Earnings / Seasonality / Mid-terms

Jaded, forward looking, fomo induced possibly

bear steepener with rising 10s bad for long-duration / hyperscaler / private credit refinancing

juggling event risk, disruptive tech, inflation is a lot for policy to manage

awaiting incoming data points (policy, macro, earnings, rates, events)

setup could be optimistic checking what is currently provided but there are a few major items that can swing things

that trips the dominos toward other possibilities: stressors that cause junk to invade higher quality

//

as they say until then just higher for longer - pretty sure with everything on agentic autopilot things should be a lot easier (now that LLMs have time series support)

bit of turbulence in the making

not a drastic shift yet pending events

somewhat bear steepening and uncertain

//

is true productivity and energy seeding prices

or is this the rampant spend following through

Quiet Fragility Under the AI Funding Boom

Rush to fund AI is met with open arms. AI-linked bond issuance already blown past last year's, roughly $218B in investment-grade and $31.9B in high-yield deals by July, and hyperscalers taking estimated 15–20% of US IG supply. Much of the money flows through SPVs, private credit and securitized structures whose risks are hard to see, let alone size.

May be fine while conditions stay friendly. If inflation sticks, an energy squeeze or a risk event breaks, effects may not contain. Shifts in 2/5-year could tighten AI funds into growth, and knock-on effects across rates, credit and policy could get complicated.

Early hints worth noting. HY spreads broke out of recent range for Q3, Sep weakest month for credit, though selective rather than systemic. Nothing or first sign of a thinner cushion.

Links
1. Morningstar, AI bond issuance tops $250B: https://www.morningstar.com/bonds/bond-issuance-backing-ai-investment-tops-250b-testing-limits-voracious-investor-demand
2. Trustnet, Do bond markets have an AI spending problem?: https://www.trustnet.com/news/13478617/fund/sectors
3. Quinn Emanuel, Litigation risks in financing AI data centers: https://www.quinnemanuel.com/the-firm/publications/client-alert-emerging-litigation-risks-in-financing-ai-data-centers-boom
4. Guggenheim, Q4 2026 High Yield sector view: https://portal.guggenheiminvestments.com/GuggenheimInvestments/media/PDF/FISV-4Q26-High-Yield.pdf
5. OCBC, Monthly Credit View (October 2026): https://www.ocbc.com/kkeSO/iwov-resources/sg/ocbc/gbc/pdf/Credit%20Research/Monthly%20Reports/2026/OCBC%20Asia%20Credit%20Monthly%20Credit%20View%20%20%20Public%20.pdf

suspended disbelief is the approach, yet nothing is really broken

the anxious sentiment is abound and there are growing pains

global events, credit risk, an environment testing fitness perhaps

//

data and learning able to monitor and adjust this time around

is this enough to evade an overzealous scenario?