The AI sector has now officially moved beyond its initial hype phase. Sophisticated investors have seen a once-in-a-generation structural shift where revenues have demonstrably reached the foundational LLM layer in clear, quantifiable terms. OpenAI attained approximately $25 billion in annualised revenue by early 2026. Anthropic accelerated even further, reaching a $30 billion annualised run rate (ARR) by April 2026. Together, the two leaders now command combined annualised revenues exceeding $55 billion.

These figures derive from real enterprise and developer spending on API access, subscriptions, and custom models, rather than internal credits or projections. NVIDIA's own leadership in terms of chip stocks and data centres, and the hyperscalers' AI cloud businesses reinforce the picture that the foundational layer has delivered measurable ROI.

The central question has also become more nuanced. With infrastructure bets validating at the model layer, where will the next wave of returns accrue? Will a select group of application-layer companies capture the majority? Might enterprises realise sustained margin expansion through productivity gains and new revenue streams? Or could value emerge through some other channel?

This panoply of queries represents something of a reset, akin to the cloud transition of the 2010s, but are now even more compressed in time and elevated in scale. Capital that entered the infrastructure must now migrate towards a display of durable, compounding opportunities.

By mid-2025 OpenAI had already exceeded its earlier $12.7 billion full-year projection. Since then, execution has continued to outpace forecasts. Enterprise adoption now drives the majority of spend, with Anthropic securing a rising share of corporate LLM budgets.

This revenue reflects genuine customer commitments that have advanced well beyond pilot stages, and the economics begin to justify the preceding years of substantial capital expenditure.

Identifying the subtle signals, however, remain essential to the sector. OpenAI continues to forecast notable losses extending into the later part of the decade and hyperscalers anticipate annual AI infrastructure outlays measured in hundreds of billions. American VC Bill Gurley has cautioned against bubble dynamics if capex-to-sales ratios exceed those of the dot-com era without parallel revenue acceleration. Brad Gerstner, the founder and CEO of Altimeter Capital, is more constructive on the long-term super cycle, and emphasises the necessity for unit economics to align and for those applications downstream to fuel the subsequent phase of value creation. This draws parallels with the cloud era in which infrastructure leaders ultimately shared gains with those of application builders.

The South African-American, entrepreneur-investor (and former COO of PayPal) David Sacks, has recently underscored a related dynamic specific to Anthropic. The company has sustained hyper-growth at a scale and velocity rarely seen even among the largest technology firms, conferring a valuation premium. Sacks has outlined a credible path for Anthropic toward a trillion-dollar valuation, driven by the rarity of such sustained momentum and the potential for duopoly-like positioning in frontier AI.

The history of cloud application provides some precedent, with early infrastructure investment establishing the foundational pipes. The greatest compounding returns ultimately accrued to applications that leveraged them at scale and to the enterprises that reconfigured their operations around them.

AI is progressing along a similar parallel but with an accelerated trajectory. McKinsey's global survey indicates that while experimentation remains widespread, only a minority of companies yet report measurable enterprise-level, earnings before interest and taxes (EBIT) impact. Those that integrate AI as a core transformation driver are distinguishing themselves through superior revenue growth, innovation rates, and cost discipline.

Still, the data remains uneven. Many organisations observe productivity improvements yet encounter difficulty linking them directly to profit-and-loss statements. Leading adopters, meanwhile, are pulling ahead. The next wave of returns is likely to divide between two primary sources.

First, vertical and horizontal application-layer companies that embed AI so deeply into workflows that they become indispensable. These are AI-native platforms that did not exist prior to 2023. Second, enterprise customers that translate AI capabilities into tangible margin expansion through reduced time on routine tasks, elevated output, and novel revenue from AI-enhanced products and services.

Still, the data remains uneven. Many organisations observe productivity improvements yet encounter difficulty linking them directly to profit-and-loss statements. Leading adopters, meanwhile, are pulling ahead. The next wave of returns is likely to divide between two primary sources.

First, vertical and horizontal application-layer companies that embed AI so deeply into workflows that they become indispensable. These are AI-native platforms that did not exist prior to 2023. Second, enterprise customers that translate AI capabilities into tangible margin expansion through reduced time on routine tasks, elevated output, and novel revenue from AI-enhanced products and services.

Proof has advanced beyond anecdote, especially in those industries and areas where AI has become integrated into their whole ecosystems.

US healthcare tech company Abridge deployed products and services that illustrated the potential with particular clarity. At Corewell Health Beaumont Troy Hospital in Michigan, US, clinicians experienced a 61% reduction in cognitive load and 48% less time on after-hours documentation in pilot phases. Systems in San Diego’s Sharp HealthCare have implemented similar platforms, enabling clinicians to prioritise patient care above administrative burdens. These changes translate directly into higher patient throughput, lower burnout-related turnover costs, and enhanced revenue-cycle performance.

Cursor, the AI-powered coding assistant, achieved $1 billion ARR by late 2025 and doubled to $2 billion by February 2026. This constitutes pure application-layer value capture. Developers are not merely accelerating; they are redesigning workflows around autonomous agents capable of debugging, log analysis, and rapid code deployment.

Organisations with mature, AI-enabled supply chains report measurable profitability improvements through predictive maintenance, demand forecasting, and agentic optimisation. These tools have transitioned from experimental pilots to production environments, yielding concrete cost savings and service-level enhancements that support revenue growth. The winning organisations are not retrofitting AI onto legacy processes, but redesigning workflows around its capabilities.

For emerging ventures, the threshold has risen decisively. Conventional SaaS models reliant on static workflows confront AI-native competitors that achieve scale at materially faster rates. Non-AI-native concepts now carry heightened commoditisation risk, with engineering teams operating with 10- to 100-fold productivity gains, and even non-technical founders can prototype functional applications with AI assistance. The implication for founders and investors is clear: any startup that is not overwhelmingly AI-first across product development, go-to-market, operations, or manufacturing faces material obsolescence risk. The divergence between adopters and laggards continues to dilate.

Much of the capital deployed today continues to pursue infrastructure narratives or the most recent foundation-model entrant. Yet, discerning family offices and institutional investors have begun posing more demanding questions. They’re looking for answers about which application layer companies will secure ownership of critical workflows? The enterprises that will demonstrate persistent margin expansion? And in which verticals (healthcare, finance, manufacturing) will AI compound into enduring competitive advantages? Capital inflows into AI now respond to a concrete signal that the foundational layer functions.

The ensuing revaluation will favour those positioned to capture downstream value, whether through specialised applications, agentic systems, or enterprises that have genuinely re-engineered core operations.

Today, the relevant inquiry for investors with extended time horizons is whether portfolios are structured around an AI environment where ROI accrues to the subsequent layer, and whether capital is being deployed with sufficient speed to secure it. At Arbra, we work with investors everyday to illustrate how this reset has commenced, and tailor the strategies necessary to calibrate and construct portfolios to overcome the prior assumptions that no longer align with emerging realities of tomorrow.