US perspectives: things are getting weird, quickly

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As with any investment, your capital is at risk.

The image jars in the best way: an oar in one hand, a global studio in the other. It’s the feeling of the frontier suddenly moving. Imagine where this level of pervasive connectivity, combined with generative AI and autonomous robots, could lead?

Harnessing massive structural shifts is part of our investment edge: our 5-10-year time horizon and our experience of investing through multiple system changes in society, from the beginning of the internet through the mobile and cloud eras, are key.

We know that we can’t say where a technology may take us exactly, so you have to think probabilistically, and some patterns and laws help give us certainty. We know that it pays to back tech-savvy leaders and founders driving disruption, and we seek a value proposition to the customer that will drive significant change. Using these methods, we can predict which companies might form the foundational layers for the future.

For example, in 2013, when we were first analysing Tesla, there were some predictions we could make with a high degree of confidence about the energy density of batteries, the cost of onboard computing and the power of software. Wright's law in manufacturing dictated that as Tesla made more vehicles, it would be able to do so increasingly efficiently. Jevon’s paradox dictates that those costs decline, and increased energy efficiency would disproportionately impact demand. In 2023, the Tesla Model Y became the best-selling passenger vehicle in the world. While he is controversial, having mercurial tech-savvy Elon Musk as CEO was also key to Tesla’s success over that decade.

We could tell similar stories about our long-term investments in Amazon, Netflix and NVIDIA, among many others.

Now we are in a new technological paradigm. Things are getting weird, quickly.

Streets that drove the same way for a century have cars with nobody in the front seat. Hiring pipelines built on “learn on the job” are wobbling, while AI copilots are becoming standard for employees. None of this is linear. That’s the point: the weirdness is the signal.

Street-level weird: robotaxis jump from novelty to infrastructure

If you visit San Francisco today, there’s a decent chance you’ll ride with Waymo, the autonomous taxi company. Paid trips have surged from experimental levels to hundreds of thousands per week across its markets, with California service areas widening and total ridership crossing eight figures. Waymo is now ahead of Lyft in terms of the number of rides, despite currently taking longer and costing more. This is an early sign of product–market fit before cost curves fall and networks compound. People really like travelling without a driver. This could be a ‘normalisation moment’: fewer headline-grabbing demos, more everyday transport.

The point: once a technology is “good enough somewhere,” capital, talent and regulators reorient around the new baseline, and adjacent uses (delivery, logistics and beyond) become more plausible – like a YouTube studio in the middle of the Pacific Ocean.

Labour-market weird: juniors feel it first

AI will bring about the most significant change to the global labour market in human history, so says the CEO of leading AI company Anthropic, Dario Amodei. Recent evidence suggests this may have already started: since late 2022, employment for 22–25-year-olds in the most AI-exposed occupations (such as software development or customer support) declined by about 6 per cent, even as older workers in the same roles grew by roughly 6–9 per cent. In AI-exposed jobs, entry-level hiring is approximately 13 per cent lower than expected versus comparable roles, even after controlling for company-wide trends.

Of course, reality is nuanced: within a single firm, AI can reduce headcount in Department A while raising it in Department B so that macro aggregates can mask sharp local effects. And yes, it’s observational, not causal. But it is a signal that something weird is going on, at the very least. Just look at Microsoft – AI coding copilots and research assistants are now table stakes for many developers and analysts – up to 30 per cent of Microsoft’s code is now written by AI.

So what? Why this matters for our portfolio work

The Macleans’ crossing is a visceral metaphor. New capabilities create disruption and innovation. When the middle of the Pacific becomes a broadcast studio, everything else, such as how we travel, how our businesses operate and how we work, trade and socialise with each other, could all be upended in hard-to-predict ways.

We aim to create portfolios that are on the right side of this disruption. As such, we seek to invest in enablers and embedded platforms tied to falling cost curves, enhanced workflows, distribution, autonomy and developer ecosystems – the infrastructure that is hard to dislodge, and where we see real traction and signal in company operations and fundamentals.

For example, one of our exposures to ‘street-level weird’ is self-driving vehicle company Aurora Innovation, which has driverless trucks delivering goods along highways in Texas.

From the commercial launch of its autonomous trucks at the end of April 2025 to the end of June, Aurora has already logged more than 50,000 driverless miles in daytime with clear weather conditions. In July, the company completed validation and began driverless operations at night, and it now has three driverless trucks on its Dallas to Houston route. Aurora has maintained close to 100 per cent on-time performance, with a perfect safety record. By the end of this year, it aims to open two more routes from Fort Worth to El Paso and Phoenix, while also working to validate driverless operations in more challenging weather, which is also expected by the end of 2025. A small but impressive start, with steady progress to address a huge opportunity: the trucking market in the US alone was $875 bn in 2021, a third of which was driver wages. We wouldn’t be surprised if the majority of truck miles driven were autonomous in ten years. Will human drivers even be seen as weird in 2035?

Elsewhere, relating to weirdness in the labour market and the enterprise, our investment in cloud-based data storage and analysis company Snowflake is an interesting example. Like Aurora, Baillie Gifford has invested in Snowflake since it was a private company. Our original thesis at IPO in 2020 was that it would grow rapidly because it simplifies messy, siloed data for its customers with a fast, easy, pay-as-you-go Data Cloud. The powerful tailwind of the shift from on-premises to cloud computing, the ever-increasing data growth, and the desire to gain insights from it were hugely appealing.

However, growth slowed in the post-pandemic period due to its customers optimising their cloud spend to control costs in a higher interest rate environment. As generative AI began to gain traction, the CEO at the time, Frank Slootman, announced his retirement in early 2024 to give a “hard-driving technologist” the helm, Sridhar Ramaswamy, Snowflake’s senior vice president (SVP) of AI, and ex-head of Google Ads. This was the tech-savvy leader Snowflake needed for its next phase of growth centred on AI.

Since then, growth has reaccelerated, with AI now powering 25 per cent of all customer projects. Its most recent results suggest AI is automating and accelerating cloud migrations through tools like SnowConvert AI. Ramaswamy says, “Today, AI is a core reason why customers are choosing Snowflake, influencing nearly 50 per cent of new logos won” in the last quarter. Fundamental acceleration like this at Snowflake’s scale is impressive.

Weird today, norm tomorrow

In conclusion, ‘weird’ is what system change feels like up close: robotaxis normalising in one city while juniors are squeezed even as senior productivity climbs and cloud migration accelerates, self-driving trucks on the highway day and night, and a live ocean podcast from the literal middle of nowhere. For long-term investors, the task isn’t to pick a single story. It’s to see the big picture, look for signals and patterns, and then back the teams and exceptional growth companies most likely to convert today’s strangeness into tomorrow’s operating leverage and returns.

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