A popular topic of conversation of late has been the existence of an artificial intelligence (AI) bubble and the likelihood that it will burst with great detriment to the IT industry as a whole. Yet, and perhaps surprisingly, the impact of a bursting bubble on digital twins might not be as problematic as one might think.
Ready adoption and fast diffusion of AI might warrant the tremendous investment flows of past years and could create revenue and profit streams quickly. We might as well be standing on the precipice of a bubble popping that will lead to sweeping valuation corrections. But digital twins stand to benefit from advancing AI either way. That said, the timeline of AI-enabled applications of digital twins might move, however.
Since the start of 2023, AI-related company valuations have ballooned. OpenAI is often attributed with starting the AI frenzy when releasing ChatGPT at the end of 2022. The company was valued at $29bn in 2023 and reached $500bn in October 2025, with observers wondering if the company can pull off a $1tn initial public offering soon.
AI chip leader Nvidia’s stock, meanwhile, multiplied by 13 between the beginning of 2023 and the end of October 2025, making it the first $5tn company ever. Even companies that are related but not at the centre of AI developments have increased substantially in value, with the stock price of Microsoft and Alphabet more than doubling and tripling, respectively, during that time period.
AI encompasses many different types of technologies and has many use cases, so it should be seen as an enabling technology rather than a sole application or a market per se. AI will play a major role across most application areas, but to varying degrees. Much like the way the internet shaped past decades – and will continue to shape coming decades – AI will transform industries for good in the long term. Potential potholes on the path that create setbacks are only par for the course.
Looking back to gaze ahead
It is worthwhile recalling the dot com era from the end of the last century to judge AI’s current hype. The Nasdaq Composite index – a stock index that skews toward information-technology companies – peaked at more than 5,100 points in March 2000 and then rapidly declined to a final low of barely above 1,100 points in October 2002. It took more than 12 years to move beyond 5,000 points again.
Likely, a crash is in the making. Similarly to 2000, a bursting bubble does not mean AI will go away, as internet-enabled companies and business models did not vanish. On the contrary, AI will flourish as the internet did. In fact, many infrastructure elements, such as datacentres, will become affordable for general use after lofty valuations come down.
During the late 1990s, the construction of fibre communication networks was perceived as a promising business opportunity. The business never became as profitable as expected, but the initial excitement created an infrastructure of dark fibre – unused but readily available communication lines – that supports today’s business models as a commodity that can be readily leveraged.
AI as an enabling technology will boost capabilities and accelerate the use of advanced digital twins. In particular, digital twins that have to work with difficult-to-capture data and not-completely-understood real-world dynamics will benefit tremendously. Digital twins of machinery can rely on a solid understanding of physics and measurable data that sensors can capture cost-effectively.
Factory environments have many known equipment dynamics and interactions – even workers’ likely movement patterns can be plugged into simulations. But urban digital twins attempt to capture the dynamics and behaviours of relevant elements across entire cities. They are not only subject to less understood dynamics, but also phenomena that are difficult – often impossible – to measure. AI can make available data usable and create additional data of unmeasurable phenomena.
AI in digital twins also allows the use of scenarios to better prepare for sudden events that can affect the entire system in unexpected ways. For example, city managers might use it to develop strategies for unusual weather events, pandemic-like occurrences, or localised industrial accidents with ripple effects across the urban landscape.
Digital twins and AI to plan for tomorrow’s cities
Digital twins of urban environments are difficult to design, implement and maintain, but the potential commercial and societal impact such digital twins can have promises to be substantial. Because of the number of parameters, intersecting dynamics and range of conceivable scenarios, the benefits AI can provide in understanding urban environments are considerable. AI and digital twins reinforce each other.
Digital twins of urban environments are difficult to design, implement and maintain, but the potential commercial and societal impact such digital twins can have promises to be substantial
AI can speed up the building of digital twins by supporting code development for virtual environments. Such applications accelerate overall design development and allow design details to be embedded more easily. For clients and users, AI reduces costs, enables faster implementation of digital twins, and allows for quick and inexpensive changes and alterations as requirements change or new needs arise. In addition, AI can improve the interface experience between virtual environments, as well as simulations of operations and users.
Ari Lightman, a professor at Carnegie Mellon University, explains: “Generative AI would be used to look at the entire simulation and turn it into a summary for humans. It could tell me things I might be missing and summarise things in a way I can understand.”
AI doesn’t only benefit digital twins, but digital twins also support AI’s capabilities. Scott Likens, emerging technology leader at PwC, points out: “We’re using digital twins to generate information for large language models…. We see an opportunity to have the digital twins generate the missing pieces of data we need, and it’s more in line with the environment because it’s based on actual data.”
Nvidia serves the market of smart cities as city planners and managers “are turning to digital twins and AI agents for urban planning scenario analysis and data-driven operational decisions”. The company is providing a range of solutions to enable users to create photo-realistic, simulation-ready digital twins of urban environments to optimise city operations.
A partnership of Japanese companies is developing the digital entertainment city Namba in Osaka, Japan. The aim is to “create the world’s first smart city that integrates artificial intelligence, extended reality and decentralised physical infrastructure networks [a blockchain-based approach to manage decentralised networks] on a city-wide scale”. The group intends to offer services beyond entertainment and tourism. Namba is a neighbourhood within Osaka, thereby limiting the claim to city-wide application of the concept.
The silver lining of AI over-investment
The existence of an AI investment bubble is increasingly perceived as a foregone conclusion. AI companies and technology suppliers are now even investing in each other’s operations, adding to lofty valuations. There are obvious indications of a bubble, but positive effects can emerge from the current investment excitement. Whatever the outcome, applications for digital twins will see their timeline solidify as the immediate future of AI plays out.
Over-investment in fibre during the dot com years ended up creating dark fibre – overbuilt fibre cables for data transmission. This infrastructure has served as a ready and inexpensive resource ever since. For AI, investment in datacentres is comparable to the fibre investment from 30 years ago
If use of AI applications proves to be an all-encompassing and rapidly growing market opportunity, the immense investment of the past couple of years will be retroactively viewed as forward-looking wisdom that locked in favourable competitive positions and profits for years to come. More likely, though, investors have outrun their headlights, and expectations of adoption and diffusion of AI applications over the next few years are overrated.
If so, there will be a shock to the system, like the burst of the dot com bubble at the beginning of the century when the Nasdaq Composite Index dropped by almost 80% within 30 months. Initial warnings existed – the former chairman of the Federal Reserve used the phrase “irrational exuberance” when discussing the development of the stock market in December 1996. Warnings of an exuberant AI bubble are common today.
Bursting investment bubbles hurt investors and bring down many companies. Indeed, 25 years ago, a slew of dot com companies vanished. But related over-investment in infrastructure can make assets suddenly affordable, opening up new opportunities. Such affordability changes cost structures that enable business models that could not have become successful at previous valuations. Infrastructure overhang – infrastructure built for rapid growth that does not materialise in the short run – leads to commodification of infrastructure elements, which can democratise a technology for incumbents and startups alike.
The over-investment in fibre during the dot com years ended up creating dark fibre – overbuilt fibre cables for data transmission. This infrastructure has served as a ready and inexpensive resource ever since. For AI, investment in datacentres is comparable to the fibre investment from 30 years ago. Morgan Stanley analysts forecast datacentre spending globally of up to almost $3tn between now and 2028. The amount is staggering, and it is difficult to imagine use cases and adoption rates that will provide the required return on investment for any business model. But as initial investors see their investments decrease or vanish, new players can snap up or use related infrastructures at bargain prices.
Alkesh Shah, a tech analyst at the Bank of America, explains the underlying reason for such recurring dynamics: “You always over-estimate how fast the change will happen. And you underestimate the magnitude of the change.”
The impact digital twins will have on the marketplace will follow a similar dichotomy between today’s expectations of rate of change and tomorrow’s impact of such change. Digital twins require many technological bits and pieces to come together. And AI will play an important role in digital twins, if not tomorrow, then the day after tomorrow.
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An unknown technical problem caused a number of robotaxis owned by the Chinese tech giant Baidu to freeze on Tuesday in the middle of traffic, trapping some passengers in the vehicles for more than an hour.
In Wuhan, a city in central China where Baidu has deployed hundreds of its Apollo Go self-driving taxis, people on Chinese social media reported witnessing the cars suddenly malfunction and stop operating. Photos and videos shared online show the Baidu cars halted on busy highways, often in the fast lane.
A college student in Wuhan tells WIRED that she was stuck in a Baidu robotaxi with two friends for about 90 minutes on Tuesday. (She asked to be only identified with her last name, He, to protect her privacy.) The student says the car malfunctioned and stopped four or five times during the trip before it eventually parked in front of an intersection in eastern Wuhan. Luckily, it was not a busy road, and the group was not in immediate danger. The screen display in the car asked the passengers to remain in the car with seatbelt on and wait for a company representative to come “in five minutes,” according to a photo He shared with WIRED.
He says it took about 30 minutes to reach a Baidu customer representative on the phone. “They kept saying it would be reported to their superior. But they didn’t explain what caused [the outage] or let us know how long we needed to wait for the staff to come,” He says. But no one ever came, and after another hour of waiting, the three passengers decided to just get out and go home by themselves (the doors weren’t locked).
On Chinese social media, other passengers also complained about being unable to reach Baidu’s customer support. “I tried every way I could think of to call for help using the options the app showed, but the phone line wouldn’t go through, and when I pressed the SOS button it told me it was unavailable. So then what exactly is the SOS for?” wrote one person in a post on RedNote alongside a video showing the button not working. She said she had to force the door to open and get out of the car as traffic halted to a complete stop behind her robotaxi. “Apollo Go, you really owe me an apology,” she wrote.
Baidu didn’t immediately respond to a request for comment. Local police in Wuhan issued a statement around midnight in China that said the situation was “likely caused by a system malfunction,” but the incident is still under investigation. No one was injured and all passengers have exited the vehicles, the police added. It’s unclear how many of Baidu’s robotaxis may have been impacted.
One dash cam recording posted to RedNote shows a car passing 16 Apollo Go vehicles parked on the road in the span of 90 minutes. On several occasions, the video shows the driver narrowly avoiding hitting the robotaxis by braking or changing lanes at the last minute.
Others were apparently not as fortunate. In another RedNote post, a man claimed he crashed into one of the malfunctioning Baidu vehicles. The man wrote in the caption that he was driving over 40 mph on a highway when the car in front of him suddenly changed lanes to avoid the stopped robotaxi. He couldn’t react fast enough and ended up running into the self-driving car. Photos of the man’s orange SUV being towed away show that the car’s front-right fender was completely torn off, and other parts appeared to have sustained major damage.
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