The Great Tech Hype Bubble: Popping the Myths of AI and Robotics
In a world where technological advancements are often hyped to the extreme, it's time to burst some bubbles and bring some much-needed realism. Enter Rodney Brooks, an Australian-born technologist with a mission: to deflate the exaggerated promises and predictions surrounding cutting-edge technologies.
Brooks, a co-founder of iRobot (yes, the Roomba vacuum cleaner folks) and a leading figure in robotics and AI, is not your typical Luddite. In fact, he's been at the forefront of these industries, yet he's not afraid to call out the hype.
The Reality Check: Turning Ideas into Scalable Solutions
Having ideas is one thing, but turning them into reality is a whole different ball game. And when it comes to deploying these technologies at scale, well, that's an even tougher nut to crack. Brooks puts it best: "Having ideas is easy. Turning them into reality is hard. Scaling them up? That's the real challenge."
Brooks' Predictions: A 32-Year Journey
In 2018, Brooks embarked on a bold experiment. He made predictions about the future of major technologies like self-driving cars, space travel, AI bots, and humanoid robots, promising to revisit these predictions annually for the next 32 years. A bold move, indeed.
His predictions were categorized into three: "Not In My Lifetime" (NIML), "No Earlier Than" (NET) a specified date, and "By" a certain date. So, how did he fare?
The Scorecard: A Reality Check
As of January 1st, 2026, Brooks published his eighth annual predictions scorecard. And while he admits to being "a little too optimistic" overall, his predictions have largely held up. For instance, he predicted a robot to assist the elderly with multiple tasks wouldn't appear before 2028, and as of now, there's still no general-purpose solution in sight.
Similarly, his predictions about human colonies on Mars and humanoid robots with dog-like intelligence have proven to be more challenging than initially thought. Brooks highlights the subtle redefinition tactics used by high-tech promoters to finesse their promises, especially with "self-driving cars."
The Waymo Paradox: Fully Autonomous, Really?
Waymo, the largest player in self-driven transport, claims its robotaxis are "fully autonomous" and "always in control." But Brooks challenges this claim. He points out that during a power blackout in San Francisco, Waymo's robotaxi fleet was stranded, unable to navigate without human intervention.
Waymo later acknowledged that its vehicles sometimes require human confirmation checks when faced with blacked-out traffic signals or confusing situations. This reveals a crucial gap in the fabric of full autonomy.
The Human Factor: Gig Workers to the Rescue
It's not just about remote control centers; Waymo also relies on gig workers from Honk, a third-party app, to physically deal with immobilized vehicles. These humans are summoned to handle situations like a passenger failing to close a car door properly.
"Current-generation Waymos need a lot of human help," Brooks observes. "From remote operations centers to Honk gig workers, humans are an integral part of the system."
The Fascination with Humanoid Robots: A Chimera?
As a pioneering robot designer, Brooks is skeptical of the tech industry's obsession with humanoid robots. He argues that general-purpose robots that look and act like humans are not just challenging but often dangerous. The unsolved problem? Creating a robot with human-like dexterity.
Two-legged robots tend to fall over and require human intervention to get back up. They're heavy and unstable, making them unsafe for humans to be close to when walking.
The Jetsons' Legacy: A Forgotten Lesson
Even the creators of "The Jetsons" understood that domestic robots wouldn't rely on legs. Their robot maid, Rosie, moved around on wheels, a perception that seems to have been forgotten by today's engineers.
The Limits of Large Language Models: Confabulations and Guardrails
Brooks' experience with AI gives him unique insights into the shortcomings of large language models (LLMs), the technology behind contemporary chatbots. He explains that LLMs don't answer questions directly but rather give something that sounds like an answer. They've learned a probability distribution of what word is most likely to come next, not facts about the world.
The solution, according to Brooks, is not to "train" LLMs with more data but to purpose-build them for specific needs in specific fields. He believes in adding guardrails to make LLMs useful, a concept that will likely see a lot of action in the next decade.
The Scaling Challenge: Overestimating and Underestimating
Brooks' overarching theme is that we tend to overestimate what new technologies can do and underestimate the time it takes to scale them up. The hardest problems are often the last to be solved, and people tend to expect new technologies to develop at the same rapid pace as their early stages.
This is why the march to fully self-driving cars has stalled. It's one thing to have lane-change warnings or cruise control; achieving Level 5 autonomy, as defined by the Society of Automotive Engineers, is a whole different ball game. No Level 5 vehicles are in general use today.
The Bottom Line: Don't Believe the Hype
Believing technology promoters' claims that nirvana is just around the corner is a fool's errand. As Brooks puts it, "It always takes longer than you think."
So, the next time you hear grand promises about the latest tech, remember Brooks' words: "Ideas are easy; reality is hard. Scaling up? That's the real challenge." And this is the part most people miss: it's a marathon, not a sprint.
