AI, Infrastructure, and the Next Investment Cycle
Summary
The AI investment cycle is enormous, but the panel argues that earnings—not multiple expansion—are driving the market. Since the referenced presentation came out almost four years ago, the market has risen 90%, or 17% annualized; stocks recently gained roughly 20% as multiples fell roughly 20%; and the S&P 500 trades below 20 times earnings while growing about 15%. “We’re definitely in a hot period,” but this looks unlike 2000 or 2021, when valuation and growth detached.
Hyperscaler spending is becoming an industrial cycle whose scale keeps outrunning forecasts. Alphabet, Amazon, Meta, Microsoft, and Oracle are investing about $780 billion in 2026, up from $416 billion in 2025, with expectations of more than $1 trillion annually from 2027; combined cloud backlog at Microsoft, Google, and Amazon is about $1.7 trillion. Compute demand is a “model buster”: some inputs are unavailable until 2028, yet available GPUs still command attractive prices and returns.
AI’s buildout is merging with a much broader “new age of atoms.” Global infrastructure needs are estimated at $90 trillion through 2040, spanning chips, power, cooling, factories, water, roads, transit, and skilled labor. The counterintuitive grid argument is that shared investment can benefit households: one cited study found residential electricity rates fell 40 basis points for every 10% increase in data-center capacity.
Enterprise AI has broad experimentation but remarkably little institutionalized adoption. Some 69% of S&P 500 companies report live deployments and 30% report quantifiable impact, yet only 2% track an AI metric over time. That gap—“moving from these individual deployments to really deeply influential recurring workflows”—is the application-layer opportunity, because models know much about the world but little about a specific company.
Power users and measurable business outcomes show how deep adoption might eventually run. The top 1% of AI spenders spend roughly eight times the top-10% level; inside AI-native portfolio companies, leading users spend $7,500-$9,000 monthly versus $200-$400 for median users. Chime reports more than 10% annual reductions in cost to serve for four years, Shopify reports that the share of merchants reaching five orders within 15 days grew 8% after Sidekick launched, and ServiceNow has surpassed $1 billion of AI ACV.
Falling inference costs are making multi-step agents economically viable and potentially more profitable for application companies. Agent-token usage on OpenRouter has grown 14-fold, Hebia’s financial-chat workloads became 10 times cheaper, Databricks’ router solved more problems at 35% lower cost than the strongest standalone model, and EliseAI’s fine-tuned smaller model cost 60% less. The investable unit is increasingly “the cost of getting the customer’s job done,” not allegiance to one model.
Value is bifurcating across software, consumer discovery, and private markets rather than disappearing uniformly. Only 30% of public software companies grow at least 20%, while fewer than five exceed 30%; cybersecurity, observability, and vertical AI are holding up better than horizontal applications. Meanwhile, six private champions total roughly $2.4 trillion in valuation, AI-related companies represent 86% of U.S. venture activity, and agents may relocate advertising and marketplace profits even if lower friction expands total consumption.
Deep dive
1. Technology has become the economy’s dominant investment cycle
The panel’s opening frame is “tech is the everything cycle”: high-tech equipment, software, and R&D now represent roughly 55% of U.S. capital spending, technology is nearly 40% of U.S. stock-market value, and eight of the world’s ten most valuable companies are American technology businesses.
George estimates that model companies have collectively raised more than $350 billion, while the AI buildout has surpassed historical railroad investment as a share of GDP. The group thinks today’s cumulative figures could look “20x higher” five to ten years from now as accessible software creation multiplies demand for compute, chips, power, and construction.
The panel treats this as the next chapter of “software is eating the world.” Previous software waves empowered a limited population of builders; AI lets far more people create automations, including long-running processes that continue working overnight.
The panel also tracks OpenAI and Anthropic’s combined annualized revenue, which has risen with unusual speed and surpassed estimates of net-new revenue added by the best software companies.
2. Strong earnings distinguish the rally from a valuation bubble
The panel raises the unavoidable question—“That’s all great, but is it a bubble?”—then points to the divergence between prices and valuations: stocks are up about 20% while trading multiples are down about 20%, meaning earnings rather than multiple expansion account for the appreciation.
The comparison with the dot-com boom is categorical: today’s largest companies are high-quality businesses trading at nothing like the roughly 100-times-P/E valuations once attached to major internet stocks. Some highly cyclical memory companies now trade at only six or seven times forward earnings.
The skeptical case remains worth preserving. Since the referenced presentation came out almost four years ago, a 90% market gain and 17% annualized return naturally invite mean-reversion concerns; the panel’s rebuttal is that a sub-20-times market growing earnings around 15% is fundamentally different from 2000 or 2021.
3. Compute demand keeps breaking every capex forecast
The five largest hyperscalers’ projected capex rises from $416 billion in 2025 to about $780 billion in 2026, with expectations exceeding $1 trillion annually from 2027. Forecasts repeatedly assumed spending would flatten, but each supposed ceiling became near-term reality as installed capacity was absorbed.
The mechanism has changed from occasional prompts to parallel, long-running agents that write code, search, use tools, and retry tasks autonomously. Because AI sits atop internet, cloud, and mobile distribution, new capacity can immediately serve billions of potential users; the panel calls the resulting demand a “model buster.”
The panel’s sharpest anecdote is that massive compute commitments criticized as reckless a year earlier came to look prescient—yet a provider still had to pause new subscriptions on its Pro plans. Across the supply chain, some materials and products cannot be secured until 2028.
Microsoft, Google, and Amazon together have about $1.7 trillion of cloud backlog. Free cash flow is temporarily depressed by construction and chip purchases, but forecasts show recovery beginning in 2028; longer-than-expected GPU and TPU lives underpin the “J-curve” between upfront expenditure and later cash generation.
4. The AI boom is becoming a physical-infrastructure boom
Every hyperscaler capital outlay becomes “someone else’s order books”: chips, power, cooling, construction, financing, materials, and skilled labor. The operating challenge differs from software’s build-once, sell-infinitely model because teams must manage vendors, capacity, financing, and uncertain demand simultaneously.
The broader frame is a “new age of atoms,” with global infrastructure needs estimated at $90 trillion through 2040. Examples include an Anderoll manufacturing facility spanning 87 football fields, Whimo depot expansion, and SpaceX’s cited $100 billion Louisiana investment—evidence that betting on technology increasingly means building factories and physical systems. As David frames it, “the factory is the product,” and factory execution can become a competitive advantage.
One counterintuitive claim is that data centers need not raise household power bills. Because poles, substations, and wires are shared fixed costs, a large stable customer can spread them across more electricity; a recent U.S. study found that each 10% increase in data-center capacity was associated with a 40-basis-point decline in residential rates.
5. Enterprise deployment is widespread, but real diffusion has barely started
The adoption funnel falls rapidly from 69% of S&P 500 companies reporting live deployments, to 30% reporting quantifiable impact, to just 2% tracking an AI metric over time. Most enterprises the panel encounters remain centered on Microsoft Copilot, leaving substantial room for recurring, workflow-level adoption.
The application layer bridges a contextual gap: models may know the world while knowing little about a specific company. Revolute, despite its sophisticated engineering organization, still partnered with ElevenLabs to connect voice capabilities securely to accounts and banking workflows—the work required to turn a model into a reliable service.
Usage is already radically uneven. The top 1% of AI spenders spend roughly eight times the top-decile level and almost as much as the 2%-10% group combined; within AI-native companies, monthly spend can range from $200-$400 for median users to $7,500-$9,000 for leaders.
Public-company evidence shows both margin and revenue effects. Chime’s cost to serve has fallen more than 10% annually for four years, Shopify reports that the share of merchants reaching five orders within 15 days grew 8% after Sidekick launched, and ServiceNow reports more than $1 billion of AI ACV alongside a ninefold increase in agentic deployments.
6. Cheaper agents turn reliability into an economic design problem
Agents require many model calls because tasks involve multiple steps, checks, and retries; the panel cites 14-fold growth in agent-token usage on OpenRouter. Caching helped Hebia make its financial-chat workload 10 times cheaper, allowing the same budget to support substantially more work.
Lower costs let an agent “try, check its work, try again,” especially where reliability previously made deployment uneconomic. The panel argues that another one or two orders of magnitude of cost reduction could unlock use cases that are currently difficult even to imagine, particularly when paired with lower latency.
Databricks’ smart router performed better than the strongest individual model while solving more problems at 35% lower cost. Fine-tuning produced similar gains for EliseAI: a smaller model became 60% cheaper and sufficiently faster for live audio.
The application-company insight is to optimize for completed work rather than defaulting to the most expensive model at every step. Routing and fine-tuning can decouple customer value from inference cost, creating a plausible path to better margins even as usage intensifies.
7. Consumer agents may expand commerce while redistributing its profits
Only slightly more than 2% of U.S. households pay for AI subscriptions, tiny beside Amazon Prime’s more than 200 million households or Netflix’s roughly 70 million. Yet paid AI products show unusually strong “smiling” retention curves as improving products bring users back more frequently.
Josh Elman’s counterpoint is that consumer AI is what people use in daily life, not necessarily what they personally expense; low household payment may therefore understate adoption. Traditional screen-time measures also miss agents doing deep research or completing tasks in the background.
Agentic discovery creates a strategic split between Amazon and Instacart. Amazon has less incremental demand to gain and more customer-relationship economics to defend, including an advertising business above $70 billion; grocery’s lower online penetration gives Instacart more potential orders to capture from reduced friction.
The disagreement stays unresolved. Agents might erode marketplace advertising and move those dollars elsewhere, but they might also create trips, dinners, and purchases that consumers previously abandoned. As the panel concludes, “both could be true”: individual profit pools can shrink while economy-wide consumption and productivity rise.
8. Software and private markets are separating winners from passengers
Roughly 75% of the public software sample is profitable, but only 30% grows at least 20%; fewer than five companies exceed 30% growth. AI budgets can displace incremental SaaS projects, so incumbents must pair durable retention—around 98% gross dollar retention—with new AI products that reaccelerate revenue.
The proposed offensive target is 10% or more of revenue-growth acceleration over 12-18 months. Cybersecurity and observability benefit as agents access more systems and create monitoring needs, while vertical companies such as Harvey, Abridge, and EliseAI are growing faster than historical precedents in their industries.
Stripe’s private-company data nevertheless suggests a SaaS “renaissance,” with growth accelerating into 2026 across young and mature businesses. Six private companies—Anthropic, OpenAI, Data Brick, Stripe, Wayland, and Revolute—carry about $2.4 trillion of combined valuation, versus $1.7 trillion for a decade of IPOs excluding SpaceX.
Staying private can let founder-led companies take longer-duration bets; the IPO is framed as another financing event. Employee tenders provide liquidity, but Carta data shows only 58% participation, while current secondary transactions are occurring at roughly the last-round price rather than the meaningful discounts seen in prior years.
AI-related companies now represent 86% of U.S. venture deal activity, up from 65% in 2025, but the label spans applications, chips, power, services, defense, and infrastructure. The panel’s next-cycle bets include robotics potentially exceeding LLMs, autonomous networks expanding miles traveled by at least an order of magnitude, AI-enabled biology and personal health, deeper enterprise diffusion beyond coding, and American Dynamism, where newer vendors still account for less than 5% of military spending.