Photo:Audun Wickstrand Iversen ©DNB Asset ManagementAutonomous driving is steadily emerging as one of the most promising applications of artificial intelligence. The story is no longer just about electric vehicles, but about the convergence of software, data, sensors and robotics. It is precisely at the intersection of these technologies that the next major growth platforms — and new opportunities for investors — could emerge.
Tesla is a particularly clear example of this technological convergence. Many investors still view the group primarily as an electric vehicle manufacturer. In the longer term, we see Tesla rather as a robotics company. Electrification alone would not, in our view, constitute a sufficient investment case. What matters is the combination of electrification, software, data, autonomous driving and, ultimately, humanoid robotics.
Just as the smartphone fused mobile telephony and the internet from 2007 onwards, opening the door to new platforms, the combination of electric propulsion and autonomous software could profoundly reshape transportation. The car then becomes a robot on four wheels.
The societal argument is powerful. In the United States, around 40,000 people die on the roads every year. Tesla and Waymo estimate that a large-scale rollout of autonomous technologies could reduce that figure significantly, to around 4,000 deaths annually. The reason is well known: between 80% and 90% of road accidents are linked to human error — speeding, alcohol, inattention or smartphone-related distraction.
Of course, autonomous technologies will not eliminate accidents altogether. But the question may gradually change in nature. Today, we ask whether self-driving cars should be permitted on the road. In ten years, we may be asking whether human drivers should still be allowed to drive everywhere.
For now, however, we remain cautious on Tesla. We would like greater visibility on the deployment of robotaxis and on the regulatory approvals tied to the Full Self-Driving system. Several decisions are expected in Europe, notably in Brussels, even if some progress has recently been made in the Netherlands. In addition, the postponement to August of the Optimus humanoid robot unveiling was a short-term disappointment. Tangible progress on robotaxis, Full Self-Driving approvals or Optimus would, in our view, be positive signals.
The traditional automotive industry is a telling example in this respect. Legacy manufacturers have considerable expertise in mechanics, engines, industrial production and supply-chain management. Those capabilities remain valuable, but they may no longer be sufficient in the next phase of the sector’s transformation. The car is becoming a software-defined product.
The problem is not purely financial, but also technological and cultural. We refer here to “technological debt” — the organisational and cultural inertia of groups whose structures, decision-making processes and ways of thinking remain deeply rooted in past technologies. This is particularly visible today among certain German carmakers.
To close the gap in autonomous driving, these players must first design vehicles equipped with the right sensors, cameras and computing capabilities — a process that takes several years. Then comes data collection. Tesla and Alphabet already hold large volumes of real-world data generated by the daily use of their systems. For traditional players, that gap remains difficult to bridge.
In time, some manufacturers may therefore be compelled to license technologies developed by Tesla, Alphabet or other specialists. For investors, the question is no longer simply who sells the most cars today, but who owns the data, software and platform capable of scaling autonomous mobility.
In equity markets, artificial intelligence initially emerged as a highly concentrated theme, dominated by a handful of major US technology stocks. The market is now broadening. Europe has at times made up part of its ground, while Japan and South Korea have also posted strong performances. The traditional winners in AI are no longer carrying markets on their own — a development we regard as healthy.
Artificial intelligence is gradually extending into other sectors, such as autonomous trucks. Reducing AI to mega-cap technology stocks alone would therefore be a mistake. The next phase may benefit more from specialised players able to solve specific technological bottlenecks or infrastructure challenges.
Aurora Innovation is a good illustration. The company, which specialises in autonomous trucks, is beginning to accelerate its development in the United States, notably in Texas. It is able to retrofit existing heavy trucks with its technologies and is gradually securing larger contracts. In time, Aurora could compete with Tesla and other autonomous platforms in a particularly strategic segment: long-haul logistics, which faces labour shortages and operational efficiency and safety challenges.
In the short term, markets are likely to remain volatile. Geopolitical risks, conflicts, energy prices and US politics can all trigger sharp corrections. Historically, however, markets have tended to absorb such shocks relatively quickly, with sometimes very strong recoveries following the initial stress phases.
We therefore expect further swings this year. As the US elections approach, markets typically remain sensitive to political uncertainty. Once that hurdle is passed, attention often shifts back to economic fundamentals.
Yet many companies positioned along the AI value chain continue to post robust demand and strong earnings momentum. S&P 500 companies that have already reported results are currently delivering year-on-year earnings growth of between 15% and 20% — a historically elevated level.
Our investment horizon, however, extends well beyond the coming months. We are thinking in terms of 2030. By then, several breakthrough technologies should have advanced considerably in maturity and adoption: autonomous vehicles, drones, robotics, AI-related infrastructure, satellite communications and industrial automation.
Find all our Growth articles
