Radical Reads

Forecasting the near-term future presents its own set of challenges, but extending our foresight to a five-year horizon amplifies these difficulties. This week, we feature excerpts from Radical Partner Rob Toews’ latest article in Forbes as he explores the complexities of making predictions in such a rapidly evolving domain. The purpose of presenting these forward-looking perspectives is not only to predict the future, but to initiate dialogue and encourage a collective imagining of the unfolding landscape of artificial intelligence. All views expressed are those of Rob personally.

Nvidia’s position as the single dominant provider of chips for AI cannot and will not last.

What Nvidia has built is difficult, but not impossible, to replicate. A resurgent AMD is emerging as a credible alternative provider of advanced GPUs, with its cutting-edge new MI300 chip about to become widely available. The big tech companies—Amazon, Microsoft, Alphabet, Meta—are all investing heavily to develop their own AI chips in order to lessen their dependence on Nvidia.

Meanwhile: what is the one thing that sets Intel apart from virtually every other chip company in the world? It manufactures its own chips. Intel alone owns and operates its own chip fabrication facilities.

Put simply, America needs Intel. And that bodes well for Intel’s commercial prospects. Nvidia’s market cap today is $2.2 trillion. Intel’s, at $186 billion, is more than an order of magnitude smaller. We predict that this gap will have shrunk significantly by 2030.

The number of touchpoints that the average person actually has with cutting-edge AI systems today is limited: the occasional query to ChatGPT or Google Bard/Gemini, perhaps. By the year 2030, this will have changed in dramatic fashion. We will use AIs as our personal assistants, our tutors, our career counselors, our therapists, our accountants, our lawyers. They will be ubiquitous in our work lives: conducting analyses, writing code, building products, selling products, supporting customers, coordinating across teams and organizations, making strategic decisions.

As with any new technology, there will be an adoption curve. Some portions of the population will more readily adjust to interacting with their new AI peers; others will resist for longer. But make no mistake: this transition will be inevitable.

Today’s AI boom has unfolded almost entirely in the digital realm. There is a whole other domain that is waiting to be transformed by today’s cutting-edge AI: the physical world, the world of atoms.

Today’s robots have narrowly defined capabilities and limited intelligence. They are typically purpose-built for a particular task—say, moving boxes around a warehouse, or completing a specific step in a manufacturing process, or vacuuming a floor. This is going to change in the years ahead. Generative AI is going to conquer the world of atoms—and it will make everything that has happened to date in AI seem modest by comparison.

Dating back to the dawn of digital computing, a recurring theme in technology has been to make hardware platforms as general as possible and to preserve as much flexibility as possible for the software layer.

We will see this same shift play out in robotics over the coming years: away from specialized machines with narrowly defined use cases and toward a more general-purpose, flexible, adaptable, universal hardware platform.

Two of the hottest topics in AI today are agents and artificial general intelligence (AGI). Agents are AI systems that can complete loosely defined tasks: say, planning and booking your upcoming trip. AGI refers to an artificial intelligence system that meets or exceeds human capabilities on every dimension.

When people envision the state of AI in 2030, agents and/or AGI are often front and center. Yet we predict that these two terms won’t even be widely used by 2030. Why? Because they will cease to be relevant as independent concepts.

Let’s start with “agents”. By 2030, agentic behavior will have become a fundamental, essential element of any advanced AI system.

What about the term “AGI”? Artificial intelligence is fundamentally unlike human intelligence, a basic truth that people often fail to grasp.

Using human intelligence as the ultimate anchor and yardstick for the development of artificial intelligence fails to recognize the full range of powerful, profound, unexpected, societally beneficial, utterly non-human abilities that machine intelligence might be capable of.

By 2030, AI will be unfathomably more powerful than humans in ways that will transform our world. It will also continue to lag human capabilities in other ways. If an artificial intelligence can, say, understand and explain every detail of human biology down to the atomic level, who cares if it is “general” in the sense of matching human capabilities across the board?

The concept of artificial general intelligence is not particularly coherent. As AI races forward in the years ahead, the term will become increasingly unhelpful and irrelevant.

Concerns about technology-driven job loss are a familiar theme in modern society, dating back to the Industrial Revolution and the Luddites. The AI era is no exception.

Creative policy proposals like universal basic income will go from fringe theories to adopted legislation. There will be no easy solutions or clear-cut ethical choices. Political affiliations and social identities will increasingly be determined by one’s opinions on how society should navigate the spread of AI throughout the economy.

If you think the political moment in 2024 is tumultuous: buckle up.