As with any investment, your capital is at risk. Past performance is not a guide to future returns.
Agentic Dawn
At the start of 2025, the artificial intelligence company Anthropic had an annualised revenue run rate of $1bn. 17 months later, it has now surpassed $47bn. No company in recorded history has grown organic revenue at this scale and pace.
Anthropic, is one of Scottish Mortgage’s private holdings, and it has helped usher in the third era of generative artificial intelligence.
The first was the conversational era, which began with the launch of OpenAI’s ChatGPT in late 2022, when models became reliably capable of following natural-language instructions and holding back-and-forth exchanges with their users.
The second was the reasoning era, which began with OpenAI's o1 in September 2024, when models learned to pause, think through problems step by step and produce considered, rather than instinctive, answers. This made them better at solving complex problems, particularly in mathematics, science and coding.
By late November 2025, with the release of Anthropic’s Claude Opus 4.5, the agentic era had become unmistakable. Models could now be given a goal and work towards it over many steps: planning, using tools, checking their work and producing useful outputs without constant human prompting. Each era has built on the last rather than replaced it. Today's agents are reasoning models that have learned to act, just as reasoning models were conversational models that had learned to think.
The implications of this reach right across our portfolio from the software industry, to the value of consumer businesses, to the rise of a parallel Chinese AI ecosystem and of course to the physical supply chain that must be built to meet the near-insatiable demand for compute.
Software
The impact of agents is being felt first in software.
Adoption has been rapid. Google's chief executive has said that 75 per cent of new code is now written by AI. The founder of one of our portfolio companies recently told us that it is spending more on AI coding tools for its engineers than it is paying them in compensation. Moreover, he claimed it gets a better return on those AI tools than on the engineers themselves.
So we are heading towards a future in which software is increasingly built, operated and used by agents.
The market has reacted quickly, with the global software sector losing roughly $2tn in market capitalisation over the past 12 months. Some repricing is justified: starting valuations left little room for the questions that are now being asked about pricing power, competition and value capture. But it has also been indiscriminate, and not all software is equal. There is a difference between software that is primarily a product for humans to use, and software that provides the infrastructure on which other digital activity depends. The former may be more exposed if agents change how people interact with applications. The latter however may benefit as agents generate more demand for the rails beneath them: more data queries, more payments, more checks that the agent is who it claims to be.
Our own software holdings are skewed towards this infrastructure layer. Databricks and Snowflake organise the governed company data that agents need if they are to be useful inside enterprises. Cloudflare provides the network and security layer on which agent applications can run, which is helping websites identify, control and charge AI agents for access. Adyen and Stripe provide the payment and trust infrastructure that allows agents to transact safely on behalf of customers and merchants.
Far beyond software development
The impact of agents will not remain confined to software development alone. Strip most knowledge work down to its parts, and it is some combination of reading, writing, reasoning and using software to get things done. These are exactly the things agents are learning to do. So agents are not a tool for one industry, but for many.
Agents will become our personal shoppers, financial assistants and everyday assistants. This could cause some disruption if AI assistants sit between customers and platforms. But the strongest platforms control assets an agent needs: customer history, payments, credit, logistics and merchant networks. Amazon, MercadoLibre and Sea Limited are thus looking to build their own agents to serve their platforms and enable shopping beyond them as well.
Nubank, in Latin America, offers a similar possibility in finance. Long before agents were in vogue, its founder, David Vélez, told us that Nubank’s ambition was to give every customer a private banker in their pocket. Agents could make that ambition more practical: helping users manage bills, understand spending and find the cheapest rates on the market for them, improving price transparency and reducing the friction to taking action.
Few companies will be left untouched by these developments. For some companies, agents will create new demand; for others, they will threaten existing profit pools; for many, they will do both at once. This will be a key challenge of growth investing in the years ahead. Scottish Mortgage is we believe well placed to meet this challenge because we invest across both public and private markets. Many of the companies shaping the AI frontier remain private, and our access to them gives us a broader view of how quickly the technology is improving, how it is being adopted, and where value may ultimately accrue.
Beyond the Valley
Meeting that challenge also requires geographic perspective. It is tempting to read the AI story as a Silicon Valley one. On questions of frontier model capability, that reading is broadly right. But it is incomplete. Because China is not merely a follower in artificial intelligence. It is developing different strengths under different conditions.
The first is physical AI. Because Simulation will be vital, but embodied intelligence improves fastest when virtual training is connected to real-world deployment. China’s manufacturing base matters because it provides the world’s largest deployment surface. This is reinforced by having the largest installed stock of industrial robots, dense local supply chains and supportive government policy. Horizon Robotics, one of our holdings, sits directly in this intersection between AI and the physical world, enabling autonomous cars with the ambition to extend this into broader robotics.
The second is cost-performance. Restricted access to the most advanced chips has pushed Chinese model companies to do more with less. This matters because the agentic era will be far more compute-intensive than the conversational era, and if agents are to be widely adopted, the cost of useful intelligence must fall dramatically. MiniMax, one of our holdings, develops open-source models that approach frontier capability at a fraction of the cost. Such models do not need to win every benchmark; they will win by making intelligence cheap enough to embed into software agents, consumer apps, enterprise workflows, robots and cars. The next phase of AI therefore, will not be shaped only by those with the largest models. It will also be shaped by those that can make intelligence cheap, useful and applicable to the physical world.
Physical supply chain
These changes all come with immediate implications for the physical supply chain. Each successive era of generative artificial intelligence has added a new layer of compute demand without removing the last.
The agentic era is the third layer of this demand for compute, and it’s the fastest-growing. Anthropic's own data shows what this means for compute. A single agent, their data shows, does around four times the work of a normal chat interaction. A team of agents working together does around 15 times. But the bigger change is this: until now, demand for AI was capped by human attention. A person can only ask so many questions in a day, and each answer waits for the next prompt. Agents remove that cap entirely. Given a goal, an agent can loop through its reasoning dozens of times, work through the night while we sleep, and increasingly work alongside other agents on the same task.
So, the three eras present compounding S-curves of compute demand, with none yet plateauing and each steeper than the last. The consequence is a sharp and continuing rise in demand for chips. It is this logic that underpins our holdings across the supply chain in TSMC, Nvidia, ASML and SK Hynix.
Investing in the supply chain is, in effect, a bet on the growth of AI itself, rather than a bet on which company will capture it. Whichever applications succeed and whichever frontier models prevail, the underlying compute demand runs through the same handful of supply chain companies. This is what makes investing in the supply chain such an unusually attractive way to own the growth of AI.
The pattern of revolutions
We are well aware that the history of revolutionary technology is also a history of market overshoot. Human and market psychology have a reliable capacity to misprice the path of even the most transformational innovation. The railway companies of the nineteenth century reshaped the modern economy. At their peak, they comprised roughly 60 per cent of the entire US stock market, before a series of busts wiped out a great deal of capital. The canal buildout of the late eighteenth century and the fibre-optic buildout of the late 1990s followed a similar pattern: real technological progress, real economic impact, and real financial excess. We should expect the AI buildout to echo that history.
Yet the rational response is not to stand aside from a technological revolution. That is not the safe position it may appear to be. If AI disrupts most industries, then avoiding it doesn’t remove risk, it merely shifts it. You might still own businesses exposed to disruption, and not own the businesses in line for generational upside.
The harder task then, is to remain invested without becoming indiscriminate: to distinguish between durable value and temporary exuberance, between enabling infrastructure and fragile applications, and between companies that merely invoke AI and those that can turn it into enduring competitive advantage.
The emergence of capable agents has made us more convinced that AI demand can keep expanding. But history argues for humility. There will be waste, disappointment and overbuilding along the way. Our job is not to believe every claim made for AI, but to own the exceptional companies that can benefit as intelligence becomes cheaper, more capable and more widely deployed. That is what we believe Scottish Mortgage offers shareholders: a stake in the AI revolution.
Scottish Mortgage
Annual past performance to 31 March each year (%)
| |
2022 |
2023 |
2024 |
2025 |
2026 |
| Share Price |
-9.5 |
-33.5 |
32.5 |
6.0 |
26.8 |
| NAV* |
-13.1 |
-17.8 |
11.5 |
11.2 |
27.4 |
| Benchmark** |
12.8 |
-0.9 |
21.0 |
5.5 |
18.0 |
Performance figures appear in GBP, total return. NAV is calculated with borrowings deducted at fair value. *NAV = Net Asset Value. **FTSE All World Index (GBP) TR. Performance source: Morningstar and FTSE.
Past performance is not a guide to future returns.
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