$75.2bn
NVIDIA data-center revenue in Q1 FY2027, up 92% year over year.
Cash has already reached the bottom of the stack.
Saturday Seminar - June 2026 - working professionals
The Economics
of AI
How value is created, who captures it, and why depreciation suddenly matters
Ashish Kulkarni
an economic lens, an accounting lens, and a clearer way to read the news
Press P to switch into slide-show mode. Press P again, or Esc, to return to the single scrolling version. Press C to hide the two optional depth slides.
The puzzle
Four entries in the AI ledger all point in different directions.
The technology is getting cheaper to use. The infrastructure is getting more expensive to build. Suppliers are already earning. The final buyer return is still being proven.
Supplier ledger
Power ledger
415 → 945 TWh
IEA base-case global data-centre electricity use, 2024 to 2030.
Software is turning back into physical infrastructure.
Cost curve
600x
Estimated decline in LLM token prices across 2020-2026.
Cheaper intelligence may create more usage, not a smaller bill.
Return hurdle
$1tn+
Illustrative annual monetizable AI revenue needed if capex reaches the multi-trillion-dollar range.
This is an estimate, not a settled fact.
Sources: NVIDIA Q1 FY2027 results; IEA, Energy and AI; Du, token-price study; GeometricInvestor, The AI Capex Ledger.
How we'll do this
Two lenses, one story.
Lens one - economics
How value is created
Trace the value chain from chips and data to models, apps, firms, workers, and consumers. This lens asks: what gets cheaper, what stays scarce, and who benefits?
Lens two - accounting
Whether value is captured
Trace the required returns across the stack: suppliers sell chips and power gear to cloud operators; cloud operators sell compute to labs and app builders; apps sell AI capability to firms and consumers. Each layer has to earn enough for the layer below it to stay funded.
The thesis is deliberately modest: AI can create enormous social value while still disappointing some of the investors who financed the infrastructure. That is not a contradiction. It is the normal tension between value created and value captured.
I
Lens one - economics
How value is created
Start with the simple value chain. AI is not magic floating in the cloud; it is an industry with upstream inputs, midstream producers, and downstream users.
Upstream
What does it cost to make AI?
Chips, data, training, inference, power, cooling, and talent.
Midstream
Why do so few firms sit at the frontier?
Fixed costs, scale economies, model know-how, distribution, and open-weight pressure.
Downstream
Who benefits from using it?
Consumers, firms, workers, capital owners, and states - rarely in equal measure.
Upstream - build vs run
The central tension: expensive to build, cheapening fast to use.
The economics of AI begins with a split: frontier training looks like a giant fixed cost; inference looks like a digital service whose unit price keeps falling.
Training - build the factory
Inference - run the factory
Model training costs have risen about 2.4x/year since 2016 in one cost-model estimate.
Token prices have fallen roughly 600x across 2020-2026 in one market-pricing study.
If the trend continues, the largest training runs cost $1bn+ by 2027.
Lower unit prices invite more tasks, longer contexts, and agent loops.
This is why both claims can be true: AI can become cheaper per unit and more expensive in total. That is the economic logic behind the capex boom.
Sources: Cottier et al., The rising costs of training frontier AI models; Du, Tiered Super-Moore's Law; IEA, data-centre demand.
Stylized cost curves
The picture is deliberately stylized: one curve is the cost of building frontier capability; the other is the price of running useful intelligence.
Upstream - scarcity
Three inputs make the "cloud" feel very physical.
Compute
$60.4bn
NVIDIA data-center compute revenue in Q1 FY2027 under its prior reporting framework.
Electricity
15%/yr
IEA base-case growth rate for global data-centre electricity demand from 2024 to 2030.
Web data rights
45%
Share of the C4 web-training corpus now restricted by website Terms of Service in one 2024 audit.
The common thread is not that AI will "run out" of everything. It is that each scarce input changes bargaining power. Chips, power, and high-quality permissioned data are all places where control can turn into surplus capture.
Sources: NVIDIA Q1 FY2027 results; IEA, Energy and AI; Longpre et al., Consent in Crisis; Zhang et al., Regurgitative Training.
Midstream - market structure
Why the market wants to concentrate - and why open weights keep pushing back.
The concentration force
- High fixed costs favor firms that can spread them across huge user bases.
- Distribution matters: cloud, search, office software, devices, and developer tools are demand channels.
- Know-how compounds because model training is partly engineering craft.
The price-ceiling force
- Open-weight models make "good enough" intelligence cheaper to adopt.
- They weaken pure API resale businesses.
- They shift value toward distribution, workflow, data, support, and trust.
So the midstream story is not simply "oligopoly forever." It is a tug of war: scale pulls toward concentration; open weights and falling inference prices pull toward diffusion.
Sources: Meta, Llama 3.1 release; Du, token-price competition; Coles et al., Apertus engineering journey; Vake et al., open-source AI; SemiAnalysis, AI Value Capture.
Downstream - surplus
The buyer may capture value that the lab cannot bill.
If a professional pays a flat monthly subscription and saves hours of work, much of the value never appears as the model lab's revenue. Economists call that consumer surplus.
Value created
Value captured
Time saved, better drafts, faster code, cheaper analysis, more learning.
Subscription revenue, API fees, enterprise contracts, cloud margins.
Can be enormous and widely distributed.
Must be visible on a profit-and-loss statement.
This distinction is the spine of the talk: good technology and good investment are related, but they are not the same question.
Sources: OpenAI ChatGPT pricing; The Verge on Claude Max pricing; GeometricInvestor, value-created/value-captured frame.
Downstream - subscription wedge
$200 can buy more AI work than $200 of revenue suggests.
Public coverage of SemiAnalysis testing reports that maxed-out flat-rate plans can deliver thousands of dollars of API-priced usage, especially on long coding and agent tasks.
ChatGPT Pro
$14k
Reported API-priced monthly usage from a fully used $200 plan.
Claude Max
$8k
Reported API-priced monthly usage from Anthropic's comparable top tier.
Margin cliff
5.7%
Reported OpenAI top-tier utilization point where gross margin reaches zero; Anthropic's comparable figure is roughly 10%.
Value created
Value captured
Repo sweeps, debugging loops, research, implementation passes, and long agent runs.
A flat subscription, plus routing limits, usage caps, and pricing experiments.
User surplus can be enormous when the task is valuable.
The provider still has to turn that usage into sustainable margins.
Read the numbers carefully: they are API-equivalent retail values, not the lab's literal cost. But the economics lesson is exactly the one we need - a flat subscription can make value visible to the user and only weakly visible to the seller.
Sources: SemiAnalysis Tokenomics Model; Tom's Guide on SemiAnalysis subscription testing; Let's Data Science summary; SemiAnalysis, AI Dark Output.
Downstream - labour and productivity
AI changes tasks first. Jobs and productivity statistics move later.
The task lens
A job is a bundle of tasks. AI automates some, complements others, and creates new ones. The practical question is not "Will my job vanish?" It is "Which tasks move, and where does my comparative advantage remain?"
The macro uncertainty
Goldman Sachs models large upside; Acemoglu's task-based model is much more cautious. The disagreement is not about whether AI helps somewhere; it is about whether enough firms reorganize fast enough to move aggregate productivity.
Question for the room
If AI is better at many tasks, what should humans become relatively best at?
Sources: Acemoglu, The Simple Macroeconomics of AI; Goldman Sachs Research, GDP/productivity upside; Handa et al., Anthropic Economic Index task data.
Downstream - organization
Messrs Coase and Cheung: productivity appears when contracts change.
Coase asked why firms exist if markets can contract for everything. Cheung pushes the question down one level: which contract is cheapest when output, effort, quality, and risk are hard to measure?
Coase
Costly markets
Search, bargain, monitor, and enforce. Firms economize when internal coordination is cheaper than contracting task by task.
Cheung
Contract spectrum
Wages, piece rates, subcontracting, platforms, and franchises are alternative ways to price what can be measured.
AI implication
Redesign lag
AI makes tasks cheap; firms still need new rules for verification, responsibility, handoffs, and surplus sharing.
Before AI
After AI
Work was routed by job title, team, and software seat.
Work can be routed by task, model, reviewer, and liability.
The double thank-you was mostly human-to-human.
The contract has to keep both thank-yous alive when an agent does part of the work.
That is why productivity statistics move late. The tool arrives first; the organization has to discover the contract that makes the new division of labor legible.
Sources: Coase, The Nature of the Firm; Foss on Cheung's contractual view of the firm; Ashish Kulkarni, Contracts and the Double Thank You note, 2026.
II
Lens two - accounting
Whether value is captured
The accounting lens asks a colder question: after capex, depreciation, power, financing, and model costs, who earns a return?
The stack as a ledger
Each layer's revenue is the next layer's cost.
I
Infrastructure suppliers
Chips, HBM, networking, power gear, cooling.
already earning
II
Hyperscalers and neoclouds
Convert capex into rentable compute.
must cover depreciation
III
Token buyers
Firms and consumers buying AI capability.
must get ROI
IV
The macro economy
Productivity, prices, jobs, rates, tax.
must show diffusion
The proof lives at the top of the stack. The supplier can be paid in cash today while the buyer's productivity gain is still only a hope.
The missing accounting slide
Depreciation is why the useful life of a GPU matters.
Depreciation is the accounting rule that spreads the cost of a long-lived asset over the years it is expected to be useful. It is not just bookkeeping: it changes reported profit.
Illustrative cost
$1bn
spent on AI servers today
If useful life = 5 years
$200m/yr
depreciation expense
If useful life = 2 years
$500m/yr
depreciation expense
Same server, different assumed life
Shorter useful life means the same cash outlay hits profit faster. The machine is identical; the accounting assumption changes the yearly expense.
The cash went out on day one either way. The accounting question is how quickly that cash cost hits profit. If AI hardware becomes obsolete faster than the books assume, reported margins can look healthier than the economics really are.
Sources: WSJ, Big Tech Accounting Creates a Blind Spot in the AI Boom; GeometricInvestor on depreciation risk. Arithmetic is illustrative.
Accounting lensDemand lens
The Azeem report says AI is above water, not ashore.
The useful update is that revenue is real and growing fast. The unresolved question is whether it keeps outrunning depreciation, power, and efficiency assumptions.
What improves the ledger
What still has to hold
$110bn trailing revenue and a $175bn run rate; AI infrastructure revenue now clears quarterly depreciation with 19% hyperscaler/neocloud headroom.
That headroom assumes 6-year IT life and 14-year building life. Shorter chip lives can erase it quickly.
A 1GW fleet model gives 75-85q tokens/year and about $0.10 provider cost per million tokens before margin.
The denominator depends on GB200 NVL72 systems, FP4 inference, 65% utilization, workload mix, and speculative decoding.
Demand appears elastic: a 10% price cut is associated with 12-18% more tokens, so spend can still rise.
OpEx remains physical: the 1GW model carries $900m/yr OpEx, including $594m for energy.
Verdict: promising, but not yet a blank-cheque bull case. The bull case needs revenue growth, utilization, tokens-per-watt, and useful lives to improve together.
Sources: Exponential View, State of the AI Economy 2026; Azeem Azhar, accompanying essay.
The size of the bet
The capex cycle needs revenue, not just enthusiasm.
Buildout
$3tn
One reported estimate for big-tech property-and-equipment spending over the next four years.
Revenue hurdle
$1-2tn/yr
Range of estimates for annual AI revenue needed to validate the buildout by decade-end.
Disclosure gap
unknown
The decisive metric is not token volume; it is gross profit after depreciation, power, and financing.
This is the accounting lens in one sentence: usage is necessary, but not sufficient. A trillion tokens are good news only if they become durable gross profit and durable buyer ROI.
Sources: WSJ on $3tn capex estimate; WSJ on Bain's $2tn revenue estimate; Sequoia, AI's $600B Question; GeometricInvestor hurdle framework.
depth - optional
How to audit the bet
The useful scoreboard is boring, which is why it matters.
For compute owners
- Utilization-adjusted compute margin.
- Gross profit per watt.
- Depreciation-adjusted return on deployed compute.
- Customer concentration and contract durability.
For buyers
- Revenue per employee rising.
- SG&A intensity falling.
- Support, coding, analysis, and operations budgets moving from pilots to operating spend.
- Productivity data eventually confirming the firm-level stories.
Sources: GeometricInvestor, token and buyer ledgers; BLS Productivity and Costs, Q1 2026 revised.
III
The present tense
How to read today's AI headlines
Every headline belongs to one or more ledgers. Ask: does it prove value creation, value capture, or just another financing round?
Economic lensAccounting lens
Cheaper tokens do not automatically mean a smaller AI bill.
A falling unit price can trigger more usage, longer contexts, and agentic loops that spend far more tokens per task.
Good news
Accounting trap
Blackwell-era systems may produce far more tokens per watt and at lower cost per million tokens.
Agentic coding tasks can consume vastly more tokens than ordinary chat or code reasoning.
Capability becomes cheaper and more widely available.
Total spend can still rise if use expands faster than unit cost falls.
Sources: Business Insider reporting on SemiAnalysis Blackwell token economics; Bai et al., agentic coding token consumption; Du, token-price evolution.
Economic lens
Sovereign AI is an insurance policy against dependency.
The reported Anthropic export-control episode made the dependency problem vivid: a strategically important model can become unavailable for reasons outside the user's control.
What the headline means
The issue is not just model quality. It is continuity of access. If critical workflows depend on a foreign-hosted frontier model, policy risk becomes business risk.
Why India cares
India's AI strategy is trying to widen access to compute and build domestic capacity. The hard question is whether buying infrastructure creates sovereignty if the chips, models, and clouds remain externally controlled.
Question for the room
If a hospital, bank, or startup can be unplugged from a frontier model overnight, what is that model worth on its balance sheet?
Sources: The Verge on Anthropic/export controls; IndiaAI Mission; Economic Times on AI Impact Summit commitments.
Economic lensAccounting lens
India's AI infrastructure moment is also a return-on-capital question.
The AI Impact Summit reportedly secured more than $250bn of infrastructure commitments. That is an opportunity, but it is not automatically sovereignty or profit.
The economic case
The accounting question
Cheaper local compute can widen access for startups, universities, firms, and public services.
Domestic deployment can create large user surplus even without a frontier lab.
Returns may leak abroad if the scarce inputs and distribution are controlled elsewhere.
Sources: Economic Times, $250bn infrastructure commitments; IndiaAI Mission; Takshashila, GPUs and India.
depth - optional
Accounting lens
The financial plumbing is getting strange because compute is the scarce asset.
Reported deals
- SpaceX agreed to acquire Cursor for $60bn after a prior option-style arrangement.
- MarketWatch reported Anthropic was paying SpaceX about $1.25bn/month under a compute lease, later clarified as short-term and cancellable.
Ledger reading
- The scarce thing is not just the model; it is energized compute at scale.
- Rivals may rent to rivals when the capital cost and utilization risk are large enough.
This is not a moral claim about any one company. It is a structural clue: in a capital-intensive stack, financing arrangements and customer contracts become part of the product.
Sources: The Verge on SpaceX/Cursor; MarketWatch on SpaceX/Anthropic compute lease.
Accounting lensEconomic lens
So is anyone actually making money?
The honest answer is ledger by ledger, not yes or no.
Suppliers
Yes
NVIDIA's data-center results show supplier revenue and margins today.
Compute operators
Maybe
Hyperscalers and neoclouds must prove utilization and margins after depreciation, power, and financing.
Buyers and economy
Not yet clear
Firm-level stories are promising; aggregate productivity evidence remains mixed.
The most sensible verdict is not "bubble" or "revolution." It is: the bottom ledger has cleared; the upper ledgers are still on trial.
Sources: NVIDIA Q1 FY2027 results; BLS Productivity and Costs, Q1 2026 revised; GeometricInvestor ledger framework.
Firm-level evidence
AI-native firms are what productivity looks like before it becomes macro data.
Kim and Koning study Y Combinator W20-F24 startups and U.S. venture-backed startups first financed from 2020 to 2024. The visible pattern: smaller firms, denser technical talent, flatter hierarchy, comparable valuations.
Headcount
25%
AI-native firms are smaller than non-AI startups in the same industry-cohort.
Talent mix
+13%
Engineer share is higher; entry-level and manager shares are each roughly 15% lower.
Hierarchy
0.5
Seniority level flatter, with valuations comparable to non-AI peers.
Process channel
Product channel
AI changes how employees code, sell, serve, analyze, and coordinate inside the firm.
AI is embedded into what the firm sells, so some knowledge work moves into the product.
Tools can make a worker more productive.
Products can let a small team serve demand that once required a larger organization.
This is not yet aggregate proof. It is a clue about mechanism: AI value may first appear as more output per employee in new firms designed around the technology from day one.
Sources: Kim and Koning, AI-Native Firms; Marginal Revolution, AI-Native Firms; Rem Koning thread.
Where the two lenses meet
AI can enrich the world and disappoint its financiers.
That is the key idea. The economic lens can see a real general-purpose technology. The accounting lens can still ask whether the capital cycle earns its keep.
The economic lens sees...
The accounting lens sees...
Cheaper intelligence, wider access, consumer surplus, workflow redesign.
Capex, depreciation, power, financing, utilization, and required returns.
Value created.
Value captured.
The economy can get the railroads while the railway shareholders get the lesson. That historical analogy is imperfect, but the accounting warning is exactly right.
The question to leave with
When you hear an AI headline, which ledger changed - and what evidence would prove it?
Sources: GeometricInvestor, chain of returns; Sequoia, AI's $600B Question.
Further reading
A reading list from the clipping trail.
Use these to go deeper. I have grouped them by the job they do in the argument: capex ledgers, cost curves, productivity, labour, India, and the practical future of agents.
Value capture
AI Value Capture - The Shift to Model LabsSemiAnalysis on where the profit pool may migrate.
Training
The Rising Costs of Training Frontier AI ModelsWhy frontier training remains capital intensive.
India
Should the US Sell Advanced GPUs to China? An Indian PerspectiveA strategic view of chips, controls, and India's position.
Source note: This slide draws on the Obsidian clippings folder, especially "The AI Capex Ledger," "AI Value Capture," "The Labor Share Fell," "1,302 real-world gen AI use cases," and "Should the US Sell Advanced GPUs to China? An Indian Perspective."