2025-11-27 02:41:21
On Sunday, 30th November, ChatGPT turns three. Wild three years.
It’s triggered a dramatic race in research. Before ChatGPT launched, the big US labs released a major model every six months. After ChatGPT, releases picked up to one every two weeks.
It’s created a cascade of spending commitments. Big tech firms have increased their capex from about $120-150 billion per year before ChatGPT, to closer to $400 billion this year. We reckon that in 2025, about $220-230 billion of that is incremental investment to meet AI demand. The bankers are struggling to make sense of it all.
As ChatGPT approaches its third birthday, I want to summarise my current beliefs about the deployment of the technology and the wider environment. I first shared these with members on the EV member Slack.
I will go into more detail in a four-part series over the following days, to cover:
Part 1: The Firm
Enterprise adoption – hard but accelerating
The revenue rocket
Part 2: Physical limitations
Energy constraints limiting scaling
The inference-training trade-off
The long life of the GPU
The existential battle for compute
Part 3: The economic engine
The capital market struggles
The productivity countdown
Part 4: The macro view
Sovereign AI fragments stack
Utility-trust gap dangerously widens
Today’s first part will focus on the firms, looking at how adoption and revenue are materializing. The next three parts will analyze the physical build-out of compute, the ecosystem’s new economics and the wider macro-political system.
Today’s post is open to all – so share widely. Parts 2-4 of the series will be open to paying members of Exponential View.
Here are the 10 things I think about AI right now.
There is a clear disconnect between the accelerating spend on AI infrastructure and the relatively few enterprises reporting transformative results.
By historical standards, the impact is arriving faster than in previous technology waves, like cloud computing, SaaS or electricity. Close to 90% of surveyed organizations now say they use AI in at least one business function.
But organizational integration is hard because it requires more than just API access. AI is a general-purpose technology which ultimately transforms every knowledge-intensive activity, but only after companies pair the technology with the institutional rewiring that’s needed to metabolise them. This requires significant organizational change, process re‑engineering and data governance.
McKinsey shared that some 20% of organizations already report a tangible impact on value creation from genAI. Those companies have done the hard yards fixing processes, tightening data, building skills and should find it easier to scale next year. One such company is BNY Mellon. The bank’s current efficiency gains follow a multi-year restructuring around a “platforms operating model”. Before a single model could be deployed at scale, they had to create an “AI Hub” to standardize data access and digitize core custody workflows. The ROI appeared only after this architectural heavy-lifting was completed. The bank now operates over 100 “digital employees” and has 117 AI solutions in production. They’ve cut unit costs per custody trade by about 5% and per net asset value by 15%. The next 1,000 “digital employees” should be less of a headache.
The best example, though, is JP Morgan, whose boss Jamie Dimon said: “We have shown that for $2 billion of expense, we have about $2 billion of benefit.” This is exactly what we would expect from a productivity J‑curve. With any general‑purpose technology, a small set of early adopters captures gains first, while everyone else is reorienting their processes around the technology. Electricity and information technology followed that pattern; AI is no exception. The difference now is the speed at which the leading edge is moving.
I don’t think this will be a multi‑decadal affair for AI. The rate of successful implementation is higher, and organizations are moving up the learning curve. As we go from “hard” to “less hard” over the next 12-18 months, we should expect an inflection point where value creation rapidly broadens. The plus is that the technology itself will only get better.
Crucially, companies are already spending as if that future value is real. A 2025 survey by Deloitte shows that 85% of organizations increased their AI investment in the past 12 months, and 91% plan to increase it again in the coming year.
One complicating factor in assessing the impact of genAI on firms is the humble mobile phone. Even if their bosses are slow to implement new workflows, employees have already turned to AI – often informally, on personal devices and outside official workflows – which introduces a latent layer of traction inside organisations. This is a confounding factor, and it’s not clear whether this speeds up or slows down enterprise adoption.
On balance, I’d expect this to be the case. In diffusion models inspired by Everett Rogers and popularised by Geoffrey Moore, analysts often treat roughly 15-20% adoption as the point at which a technology begins to cross from early adopters into the early majority1. Once a technology reaches that threshold, adoption typically accelerates as the mainstream follows. We could reasonably expect this share to rise towards 50% over the coming years.
However, 2026 will be a critical check-in. If the industry is still relying on the case studies of JP Morgan, ServiceNow and BNY Mellon rather than a slew of positive productivity celebrations from large American companies, diffusion is taking longer than expected. AI would be well off the pace.
We estimate that the generative AI sector experienced roughly 230% annual revenue growth in 2025, reaching around $60 billion2.
That puts this wave on par with commercialization of cloud, which took only two years to reach $60 billion in revenue3. The PC took nine years; the internet 13 years4.
More strikingly, the growth rate is not yet slowing. In our estimates, the last quarter’s annualized revenue growth was about 214%, close to the overall rate for the year. The sources are familiar – cloud, enterprise/API usage and consumer apps – but the fastest‑growing segment by far is API, which we expect to have grown nearly 300% in 2025 (compared to ~140% for apps and ~50% for cloud). Coding tools are already a $3 billion business against $157 billion in developer salaries, a massive efficiency gap. Cursor reportedly hit $1 billion ARR by late 2025, the fastest SaaS scale-up ever, while GitHub Copilot generates hundreds of millions in recurring revenue (see my conversation with GitHub CEO Thomas Dohmke). These tools are converting labor costs into high-margin software revenue as they evolve from autocomplete to autonomous agents. The current market size is just the beginning.
Consumer revenues, meanwhile, are expanding as the user base compounds. Monthly active users of frontier chatbots are driving a classic “ARPU ratchet”: modest price increases, higher attach rates for add-ons, and a growing share of users paying for premium tiers. There are structural reasons to expect this to continue, even before AI feels ubiquitous inside firms.
First, the base of adoption is widening. If 2026 brings a wave of verified productivity wins, this trajectory will steepen. More firms should enjoy meaningful results and the surveys should show unambiguously that 25-30% of firms that started pilots are scaling them. As the remaining majority shift from pilots to production, they will push a far greater workload onto a small number of model providers. Revenue can rise even while most firms are still doing the unglamorous integration work; pilots “chew” tokens, but scaling up chews more.
Second, the workloads themselves are getting heavier. A basic chatbot turn might involve a few hundred tokens, but agentic workflows that plan, load tools and spawn sub‑agents can consume tens of thousands. To the user it still feels like “one question,” but under the surface, the token bill – and therefore the revenues – is often 10-40x higher.
Of course, this growth in usage will see token bills rise. And companies may increasingly use model-routers to flip workloads to cheaper models (or cheaper hosts) to manage their bills.
But ultimately, what matters here is the amount consumers and firms are willing to spend on genAI products.
Tomorrow, we’ll turn from the demand side to the supply side – and confront the physical constraints that are shaping the industry’s trajectory. Parts 2-4 are available exclusively to paying members of Exponential View.
I tend to view 6% as a cross point.
This is our tightest, conservative model which only looks at deduplicated spend or uplift from genAI services. If a company offers a bundled product with a genAI element, we try to isolate that element. We exclude revenue from services firms.
2025-adjusted.
Assuming “commercial launch” of the internet (for ads) is 1994.
2025-11-24 20:08:34
Hi all,
Here’s your Monday round-up of data driving conversations this week in less than 250 words.
Let’s go!
Diplomacy is linear; technology is exponential. Despite the gridlock at COP30, clean-tech adoption is shattering historical forecasts – rendering the fossil fuel era economically obsolete faster than regulators can phase it out.1
Intelligence deflation ↓ The cost-per-task for frontier AI models has dropped 300-fold year-on-year.2
Author anxiety ↑ Over half of fiction authors agree that AI is likely to replace them. 3
Parental FOMO ↑ Parents are 60% more willing to pay for AI education tools if they are told their peers’ children are using them. Social pressure is a stronger driver than utility.
2025-11-23 11:40:29
Free for everyone:
Open models, closed minds: Open systems now match closed performance at a fraction of the cost. Why enterprises still pay up, and what OLMo 3 changes.
Watch: Exponential View on the split reality of AI adoption.
⚡️ For paying members:
Gemini 3: Azeem’s take on what Google’s latest model means for the industry.
Post‑human discovery: How autonomous research agents can cut through barriers that have held back disruptive breakthroughs.
AI Boom/Bubble Watch: This week’s key dislocations and movers.
Elsewhere: new chip cooling technology, biological resilience in space, AI and water use, the return of the Triple Revolution++
Plus: Access our paying members’ Slack (annual plans).
Despite open models now achieving performance parity with closed models at ~6x lower cost, closed models still command 80% of the market. Enterprises are overpaying by billions for the perceived safety and ease of closed ecosystems. If this friction were removed, the market shift to open models would unlock an estimated $24.8 billion in additional consumer savings in 2025 alone.
Enterprises pay a premium to closed providers for “batteries-included” reliability. The alternative, the “open” ecosystem, remains fragmented and legally opaque. Leading challengers like Meta’s Llama or Alibaba’s Qwen often market themselves as ‘open,’ but many releases are open-weight under restrictive licences or only partially open-source. This creates a compliance risk as companies cannot fully audit these models for copyright violations or bias. For regulated industries, such as banking or healthcare, an unauditable model in sensitive applications is a non-starter.
The Allen Institute for AI released OLMo 3 this week, a frontier-scale US model that approaches the performance of leading systems like Llama 3.1 while remaining fully auditable. By open‑sourcing the pipeline, OLMo 3 lowers the barrier for Western firms and researchers to build and to reduce the dependence on closed or foreign models.
We are moving beyond AI as a productivity tool to a fundamental transformation in the architecture of discovery. This shift, marked by the arrival of autonomous agents like Locus, Kosmos, and AlphaResearch, could dismantle the sociological constraints of human science and completely change what we choose to explore.
Intology’s Locus runs a 64-hour continuous inference loop, effectively “thinking” for three days straight without losing the thread and outperforming humans on AI R&D tasks benchmarks. Kosmos’ run-time of 12 hours of agent compute can traverse a search space that would take a human PhD candidate six months.
The primary constraint on progress is sociological, not biological. The incentive architecture of modern science has stifled it. A landmark 2023 analysis in Nature of 45 million papers and 3.9 million patents found a marked, universal decline in disruptive breakthroughs across all fields over the past six decades.
2025-11-22 20:39:16
Some two and a half years ago, Google faced a GPT tidal wave. Sundar Pichai may (or may not) have declared a “code red.” This week, words gave way to a physics lesson in vertical integration.
When massive objects warp spacetime, smaller things will either get captured by the gravity well or achieve escape velocity. Google is adding enormous mass to the AI field: $93 billion in capex, custom silicon, vertical integration top to bottom, coupled with the breakthroughs from the research team at Google DeepMind.
Does that mean game over for competitors? Well only if intelligence is a monolithic mass, one dimension where the biggest model always wins. But that “God Model” fallacy, the notion that intelligence is monolithic, doesn’t sit with my intuitions, as I have written previously. Recent research from concurs, suggesting intelligence is more multifaceted. If that’s true, Google’s gravity well has a floor but not a ceiling. It’s tough to escape by matching their mass. You escape by achieving different densities in other dimensions.
Keep that frame in mind as we look at what Google just shipped this week.
This week Google released a new flagship LLM, Gemini 3, which includes an advanced thinking capability called DeepThink. Accompanying the launch was a remarkable new image generator called Nano Banana 3 and some developer tools.
I’ve been running Gemini 3 through my usual battery of tests in the run-up to this week’s launch. It’s a noticeable upgrade on Gemini 2.5 – sharper reasoning and a real jump in dealing with complex, theoretical questions. It’s also a concise communicator.
Compared to GPT-5, differences showed up quickly. GPT-5 tends to pile on layers of complexity; Gemini 3 gets to the point. That clarity compounds over hundreds of queries. I now default about a third of my prompts to Gemini 3. I’ve also moved a high‑stakes multi‑agent “council of elders” workflow, where several different prompts challenge and critique analyses, from GPT‑5 to Gemini 3. In that workflow GPT-5 worked noticeably better than Claude 4.5; Gemini 3 Pro is the best of the lot.
Model choice isn’t about novelty-chasing. You’re recalibrating the tone of a colleague you consult dozens of times each day. And for those who need numbers, Gemini 3 Pro tops Anthropic and OpenAI across a wide range of benchmarks.
If we just focused on Alphabet’s technical milestones, we’d miss half the picture. The unsung hero here is the firm’s deep infrastructure.
Markets are jittery about AI spend and the bubble chatter. It’s the tension of the “exponential gap”, linear financing pulled by exponential tech. Even Sundar Pichai has flagged elements of “irrationality.”
Alphabet raised its capex guidance three times this year. It now expects $91-93 billion of capital expenditure in 2025, up from about $52.5 billion in 2024, and has already signalled a “significant increase” again in 2026.
So far, that splurge has not forced a retreat from the financial cosseting that Alphabet offers its investors. The firm authorised a $70 billion buyback in 2025, spent roughly $62 billion on repurchases and $7 billion on dividends in 2024, and is still retiring around $10-12 billion of stock a quarter while paying a $0.21 quarterly dividend.
The vast bulk of the capex is going into technical infrastructure, servers, custom tensor processing units and the data centres and networks that house them, with recent cycles funding the shift to the new Ironwood TPUs and the AI Hypercomputer fabric that ties them together. Nvidia will still get a slice. Google Cloud continues to roll out high‑end Hopper‑ and Blackwell‑class GPU instances, and the Gemini and Gemma families are being optimised to run on Nvidia hardware for customers who want them.
But the core Gemini stack – training and Google’s own first‑party serving – now runs almost entirely on its in‑house TPUs. And those Ironwoods are impressive. They are comparable to Nvidia’s Blackwell B200 chips, delivering similar amounts of raw processing (42 exaFLOPs FP8 for Ironwood) and the same 192GB of HBM3e memory. But Google’s chips only need to be good at one thing: “Google-style” house models, massive LLMs with Mixture-of-Experts inference, for high-throughput serving. And so Ironwood promises lower cost per token and latency for Google services.
Gemini 3 is the dividend of that spending.
Crucially, the new model also addressed a criticism thrown at this sector. For the past few years, foundation model labs have bet on scaling laws, which state that spending more on data and compute reliably produces performance improvements. Many outside commentators claimed that scaling itself had failed. My view, last year, was that while other innovations would be welcome, scaling still had some years to go.
In the words of Google researcher, Oriol Vinyals, on the subject of pre-training scaling:
Contra the popular belief that scaling is over—the team delivered a drastic jump. The delta between [Gemini] 2.5 and 3.0 is as big as we’ve ever seen. No [scaling] walls in sight!
And as for post-training, the step where a trained model gets further refined, Vinyal’s is even more explicit:
Still a total greenfield. There’s lots of room for algorithmic progress and improvement, and 3.0 hasn’t been an exception.
What Google has shown is that scaling still works if you have the vertical stack to sustain the burn: infrastructure, data, researchers. To what extent can Anthropic or OpenAI follow that strategy?
To understand whether Google’s gravity well is escapable, we need to look at what kind of mass creates AI gravity in the first place. has argued that intelligence isn’t monolithic; it’s a composite of general reasoning plus distinct, hard‑earned capabilities shaped by targeted data and training. In practice, that broad reasoning acts like Google’s diffuse gravitational mass, bending the competitive field1.
But there’s also a second category of contingent capabilities, spiky domain-specific skills like high-level coding, legal discovery, or proteomic analysis that don’t emerge by default. These require deliberate, expensive investment in specific data and training. Think of these as concentrated density in particular capability dimensions.
This changes everything about escape dynamics. In classical gravity, you can’t escape a larger mass, full stop. But in a composite system, you can achieve localized density that exceeds the dominant player’s density in specific dimensions, even while having far less total mass.
The customer choosing between them isn’t asking “who has more total mass?” They’re asking, “Who has the highest density in the dimension I care about?”
This is the key to the next five years. If capabilities are heterogeneous and orthogonal, you can’t build one “God Model” that maximizes every dimension simultaneously – even Google faces resource constraints.
Scale creates mass; specialization creates density. Pick one capability, invest hard, and win on price‑performance in that lane. That’s how you slip Google’s gravity – there will be no head‑to‑head fight.
Google commands three fronts simultaneously: (1) research labs rivalling OpenAI’s talent density, (2) infrastructure investment approaching $93 billion annually, and – most crucially – (3) distribution channels no startup can replicate.
The last of these, distribution, is becoming decisive. AI Overviews surface before two billion searchers each month, putting Gemini in front of more users in a day than most competitors reach in a year. Its assistant already claims 650 million monthly actives, second only to ChatGPT. Gemini doesn’t need to win every task; it just needs to meet the threshold of good enough inside products people already live in.
2025-11-21 23:10:39
Listen on Apple Podcasts
The AI industry is sending mixed signals – markets are turning red while teams report real productivity gains. Today, I examine the latest research to understand this split reality.
(02:53) Unpacking three years of AI productivity data
(09:54) The surprising group benefitting from AI
(14:33) Anthropic’s alarming discovery
(17:29) The counterintuitive truth about AI productivity
There is a lot of opinion in this space and not every hot take is built on reliable data. I stick to carefully executed research. To help you ground your stance, we’ve curated the studies behind this analysis and included the full talk transcript, available to Exponential View members.
Sarkar, S. K. (2025). AI Agents, Productivity, and Higher-Order Thinking: Early Evidence From Software Development. → Senior developers gain the most because they know how to direct and evaluate AI, which drives the biggest productivity jumps.
2025-11-20 00:48:17
Hi, it’s Azeem, here with a special guest essay.
Europe once stood alongside the United States as a central force shaping global technology and industry. Its relative decline in the digital era is often pinned on regulation and bureaucracy.
But our guest, Brian Williamson – Partner at Communications Chambers and a long-time observer of the intersection of technology, economics and policy – argues the deeper issue is a precautionary reflex that treats inaction as the safest choice, even as the costs of standing still rise sharply.
Over to Brian.
If you’re an EV member, jump into the comments and share your perspective.
“Progress, as was realized early on, inevitably entails risks and costs. But the alternative, then as now, is always worse.” — Joel Mokyr in Progress Isn’t Natural
Europe’s defining instinct today is precaution. On AI, climate, and biotech, the prevailing stance is ‘better safe than sorry’ – enshrined in EU law as the precautionary principle. In a century of rapid technological change, excess precaution can cause more harm than it prevents.
The 2025 Nobel laureates in Economic Sciences, Joel Mokyr, Philippe Aghion, and Peter Howitt, showed that sustained growth depends on societies that welcome technological change and bind science to production; Europe’s precautionary reflex pulls us the other way.
In today’s essay, I’ll trace the principle’s origins, its rise into EU law, the costs of its asymmetric application across energy and innovation, and the case for changing course.
The precautionary principle originated in Germany’s 1970s environmental movement as Vorsorgeprinzip (literally, ‘foresight principle’). It reflected the belief that society should act to prevent environmental harm before scientific certainty existed. Errors are to be avoided altogether.
The German Greens later elevated Vorsorgeprinzip into a political creed, portraying nuclear energy as an intolerable, irreversible risk.
The principle did not remain confined to Germany. It was incorporated at the EU level through the environmental chapter of the 1992 Maastricht Treaty, albeit as a non‑binding provision. By 2000, the European Commission had issued its Communication on the Precautionary Principle, formalizing it as a general doctrine that guides EU risk regulation across environmental, food and health policy.
Caution may be justified when uncertainty is coupled with the risk of irreversible harm. But harm doesn’t only come from what’s new and uncertain; the status quo can be dangerous too.
In the late 1950s, thalidomide was marketed as a harmless sedative, widely prescribed to pregnant women for nausea and sleep. Early warnings from a few clinicians were dismissed, and the drug’s rapid adoption outpaced proper scrutiny. As a result of thalidomide use, thousands of babies were born with limb malformations and other severe defects across Europe, Canada, Australia, New Zealand and parts of Asia. This forced a reckoning with lax standards and fragmented oversight.
In the US, a single FDA reviewer’s insistence on more data kept the drug off the market – an act of caution that became a model for evidence‑led regulation. In this instance, demanding better evidence was justified.
Irreversible harm can also arise where innovations that have the potential to reduce risk are delayed or prohibited. Germany’s nuclear shutdown is the clearest example. Following the Chernobyl and Fukushima accidents — each involving different reactor designs and, in the latter case, a tsunami — an evidence‑based reassessment of risk would have been reasonable. Instead, these events were used to advance a political drive for nuclear phase‑out which was undertaken without a rigorous evaluation of trade‑offs.
Germany’s zero‑emission share of electricity generation was about 61% in 2024; one industry analysis found that, had nuclear remained, it could have approached 94%. The missing third was largely replaced by coal and gas, which raises CO₂ emissions and has been linked to higher air‑pollution mortality (about 17 life‑years lost per 100,000 people).
In Japan, all nuclear plants were initially shut after Fukushima. They overhauled the regulation and restarted permits on a case-by-case basis, under new, stringent safety standards. They never codified a legalistic ‘precautionary principle’ and have been better able to adapt. Europe often seeks to eliminate uncertainty; Japan manages it.
A deeper problem emerges when caution is applied in a way that systematically favours the status quo, even when doing so delays innovations that could prevent harm.
A Swedish company, I‑Tech AB, developed a marine paint that prevents barnacle formation, which could improve ships’ fuel efficiency and cut emissions. Sixteen years after its initial application for approval, the paint has not been cleared for use in the EU, though it is widely used elsewhere. The EU’s biocides approval timelines are among the longest globally. Evaluations are carried out in isolation rather than comparatively, so new substances are not judged against the risks of existing alternatives. Inaction is rewarded over improvement.
This attitude of precaution has contributed to Europe’s economic lag. Tight ex‑ante rules, low risk tolerance and burdensome approvals are ill‑suited to an economy that must rapidly expand clean energy infrastructure and invest in frontier technologies where China and the United States are racing ahead. The 2024 Draghi Report on European competitiveness recognized that the EU’s regulatory culture is designed for “stability” rather than transformation:
[W]e claim to favour innovation, but we continue to add regulatory burdens onto European companies, which are especially costly for SMEs and self-defeating for those in the digital sectors.
Yet nothing about Europe’s present circumstances is stable. Energy systems are being remade, supply chains redrawn and the technological frontier is advancing at a pace unseen since the Industrial Revolution.
Like nuclear energy, AI may carry risks, but also holds the potential to dramatically reduce others - and the greater harm may lie in not deploying AI applications rapidly and widely.
This summer, 38 million Indian farmers received AI‑powered rainfall forecasts predicting the onset of the monsoon up to 30 days in advance. For the first time, forecasts were tailored to local conditions and crop plans, helping farmers decide what, when, and how much to plant – and avoid damage and loss.