Why AI’s Next Leap Forward Is ‘Long Thinking’

Summary

WSJ Tech News Briefing – Episode Summary: "Why AI’s Next Leap Forward Is ‘Long Thinking’"
Release Date: December 13, 2024
Host: Bel Lin | The Wall Street Journal

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1. Introduction

In this episode of the WSJ Tech News Briefing, host Bel Lin delves into two major topics shaping the tech landscape: the evolving race in the autonomous vehicle industry, particularly focusing on Waymo and its competitor Cruise, and the burgeoning field of artificial intelligence (AI) with an emphasis on its "long thinking" capabilities. The episode provides insightful analysis, expert opinions, and future projections that are essential for understanding current and upcoming technological advancements.

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2. Waymo’s Ascendancy in the Autonomous Vehicle Market

a. Cruise's Shutdown and Its Impact on Waymo

The episode opens with significant news in the autonomous vehicle sector: General Motors (GM) has decided to shut down its robo-taxi program, Cruise, citing intense competition, escalating costs, and the challenges of scaling the business. This development marks a pivotal moment for Waymo, Alphabet's driverless car subsidiary, positioning it to further dominate the robo-taxi industry.

Notable Quote:
"This is a really big opportunity for Waymo and it widens their lead in the robo taxi industry even more than they already had been."
— Danny Lewis, Reporter [01:59]

Danny Lewis explains that Cruise had been a formidable competitor until an incident in October 2023, where a Cruise-operated driverless car struck a pedestrian in San Francisco, leading to severe regulatory setbacks. Despite efforts to revive their services, GM found the endeavor financially unsustainable and opted to discontinue Cruise's operations.

b. Waymo’s Expansion Plans and Future Prospects

With Cruise exiting the arena, Waymo is poised to capitalize on the reduced competition. The company is not resting on its laurels; instead, it is aggressively expanding its services to new metropolitan areas known for their tech-savvy populations. Cities like Austin, Texas; Atlanta, Georgia; and Miami, Florida are on Waymo's expansion roadmap for 2026, leveraging partnerships such as the one with Uber to integrate robo-taxi services into popular ride-hailing platforms.

Notable Quote:
"Their partnership with Uber where they're going to be launching Waymo services on Uber's app..."
— Danny Lewis [03:27]

This strategic expansion aims to replicate Waymo's success in San Francisco, targeting regions with high concentrations of tech enthusiasts who are more likely to adopt and support autonomous transportation solutions.

Industry Insight:
Shweta Kajuriev from Wolf Research comments on Waymo's unparalleled position, stating, "There really isn't any other competitor in autonomous. What else are consumers today going to be dusting? There isn't anything else. And so that also allows Waymo the first mover advantage." [04:26]

c. Challenges Ahead for Waymo

Despite the promising outlook, Waymo faces several hurdles in its journey. The financial viability of operating driverless cars remains a concern, with higher operational costs compared to traditional ride-hailing services. These costs stem from advanced technology, sensors, and maintenance required for autonomous vehicles. Additionally, safety remains a paramount issue, as maintaining trust and avoiding accidents is crucial for widespread adoption.

Notable Quote:
"Waymo and a lot of these companies say that it's cheaper to operate because you're not paying a driver. But driverless cars have a lot of extra costs associated with them as well."
— Danny Lewis [04:46]

Analysts highlight that while Waymo enjoys a first-mover advantage, sustaining profitability and ensuring safety standards are critical for its long-term success.

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3. The Next Frontier in AI: Long Thinking

Transitioning from autonomous vehicles, the episode shifts focus to artificial intelligence, specifically the concept of "long thinking." This advancement represents a significant leap in AI's problem-solving abilities, moving beyond rapid, surface-level responses to more deliberate and complex reasoning.

a. Understanding Long Thinking in AI

Bel Lin introduces the topic by comparing AI's current capabilities to human-like thinking systems. Stephen Rosenbush, WSJ’s Enterprise Technology Bureau Chief and columnist, elaborates on the distinction between "System One" and "System Two" thinking.

Notable Quote:
"System one is quick, instantaneous, doesn't have to work very hard. It's almost instinctive. And that's sort of where most generative AI is located right now."
— Steven Rosenbush [07:40]

System Two Thinking:
System Two involves more deliberate and effortful mental activities, akin to solving complex problems and making reasoned decisions. AI models incorporating long thinking can engage in deeper analysis, potentially reducing errors like hallucinations and improving performance in areas requiring intricate computations.

b. Benefits of Long Thinking AI Models

Long thinking AI models promise significant enhancements in various fields. By allocating more time and computational resources, these models can tackle intricate scientific, mathematical, and coding challenges more effectively. This capability leads to more accurate and reliable outcomes, fostering advancements in areas such as personalized medicine and global weather prediction.

Notable Quote:
"The O1 models have the capacity to step back and say, maybe this isn't the best approach. Let me try another approach to solve the problem."
— Steven Rosenbush [10:36]

This self-critical element of long thinking AI allows models to iterate on solutions, thereby minimizing mistakes and improving overall accuracy.

c. The Path Toward Artificial General Intelligence (AGI)

The conversation also touches upon the broader implications of long thinking AI in the pursuit of Artificial General Intelligence (AGI). While AGI remains a theoretical construct, advancements in long thinking represent incremental steps towards more generalized and human-like reasoning abilities in machines.

Notable Quote:
"The agents are in turn a step along the path toward some sort of AGI or think human, like broad based reasoning."
— Steven Rosenbush [09:00]

d. Potential Concerns and Ethical Considerations

Despite the promising developments, long thinking AI introduces new challenges. Ethical considerations regarding the deployment and oversight of such powerful technologies are paramount. Ensuring that AI is used responsibly to solve societal problems without creating new ones requires robust governance and clear guidelines.

Notable Quote:
"There’s a need to think about how the technology is put to use, what guardrails are in place."
— Steven Rosenbush [12:08]

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4. Conclusion

The WSJ Tech News Briefing episode "Why AI’s Next Leap Forward Is ‘Long Thinking’" offers a comprehensive exploration of two transformative areas in technology today. Waymo's strengthened position in the autonomous vehicle market underscores the dynamic and competitive nature of tech industries, while advancements in AI's long thinking capabilities herald a new era of intelligent problem-solving. As these technologies evolve, their implications for society, ethics, and future innovations will continue to shape the technological landscape.

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Produced by: Julie Chang
Additional Support: Pierre, Biana May, Danny Lewis
Theme Music: Jessica Fenton and Michael Lavalle
Supervising Producer: Catherine Millsop
Development Producer: Aisha El Muslim
Deputy Editors: Scott Salloway, Chris Sinsley
Head of News Audio: Philana Patterson

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For those interested in a deeper dive into Waymo's strategies and the autonomous vehicle race, don't miss part two of Danny Lewis's special series, "Driverless Waymo and the Robo Taxi Race," available in the Tech News Briefing feed.

Transcript

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[Bel Lin]

Welcome to Tech News briefing. It's Friday, December 13th. I'm Bel Lin for the Wall Street Journal. Google's 15 year old bet that cars of the future will drive themselves might finally be paying off. We'll find out why. Waymo, the driverless car company owned by tech giant Alphabet, has even more at stake as one of its biggest competitors shuts down. And then it's time to get ready for AI. AI that can think for a long time. AI models that are designed to take more time to think over. The results they generate for us are coming. Our Enterprise Technology Bureau chief and columnist Stephen Rosenbush tells us what that means for the chatbots we talk to but first, General Motors this week announced that it is shutting down Cruise, its robo taxi program, citing competition, time and costs needed to scale the business. But that's good news for Waymo, the driverless car unit under Google parent company Alphabet. Our own Danny Lewis has been reporting on this as part of a special WSJ podcast series called Driverless Waymo and the Robo Taxi Race. You can listen to part one right here in the Tech News Briefing feed. Danny, General Motors announced that it is slamming the brakes on its driverless car service. Cruise what does GM's move mean for Waymo?

[Danny Lewis]

This is a really big opportunity for Waymo and it widens their lead in the robo taxi industry even more than they already had been, which was also in a lot of ways thanks to Cruise. Cruise was one of Waymo's biggest competitors for a very long time, but Cruise ran into some pretty serious trouble last year. In October 2023, there was an incident where a person who had been crossing the street in San Francisco was hit by a car being driven by a human. But she ended up in the path of one of Cruise's driverless cars. It struck her and she ended up being stuck underneath the vehicle and Cruise ended up having their permits to run a robo taxi business in California. Polled They've been working really hard over the last year or so to try and restart paid robo taxi services in San Francisco and a few other cities around the country, but it seems like it just got too expensive for General Motors, and they've decided to pull the plug.

[Bel Lin]

So Waymo kind of already had a pretty immense lead, as you said, but now with crews dropping out and their other competitors operating in other parts of the space, the road is clear for Waymo. It's just accelerator straight ahead. Let's back up a little bit, though. Waymo had been seeing success in its ridership increasing in San Francisco. Do you think it can repeat its success elsewhere in the country?

[Danny Lewis]

That's Waymo's big question right now. They've expanded their operations to paid rides through all of Los Angeles. They've announced a partnership with Uber where they're going to be launching Waymo services on Uber's app in Austin, Texas, and Atlanta, Georgia. And recently, Waymo announced that it's going to be bringing its Waymo One robo taxi service to Miami, Florida as well in 2026.

[Bel Lin]

And it seems like one thing that's notable is that all those metropolitan areas also have large groups of techies. Right. Just like San Francisco, the home of Silicon Valley in the Bay Area. So we'll see if more techies and other places want to ride in robo taxis. Right.

[Danny Lewis]

They definitely seem to be hoping that going to places where maybe there are more people who are curious about riding in a driverless car and may be inclined to take one. I spoke to an industry analyst. Her name is Shweta Kajuriev. She's from Wolf Research. And she told me that there's basically no one else who can do this kind of service right now.

[Merrill Compliance Officer]

There really isn't any other competitor in autonomous. What else are consumers today going to be dusting? There isn't anything else. And so that also allows Waymo the first mover advantage.

[Bel Lin]

Yeah, but surely they have some roadblocks on this path to crazy success.

[Danny Lewis]

One of the big ones is can they make money? Waymo and a lot of these companies say that it's cheaper to operate because you're not paying a driver. But driverless cars have a lot of extra costs associated with them as well. There's the cost of the technology. There's the cost of the sensors that they need to see the world around them. There's the cost of the computers that they need to run the programs that drive the cars, you know, and that's in addition to all the normal things of maintaining the cars, making sure they are cleaning, making sure they're in good condition at the moment, Analysts have said that most Waymo rides cost a little bit more than a regular cab or ride hail might cost a person. There's also questions about safety, whether they can maintain their pace of expansion and avoid bad accidents down the line.

[Bel Lin]

That was WSJ's Danny Lewis. And you can check out part two of Danny's special series, driverless Waymo and the Robo Taxi Race this Sunday, right here in the Tech News Briefing feed. Coming up, it turns out that AI that can literally think for a long time might produce better answers than AI, which doesn't what the ability for long thinking actually means and how it could affect our use of AI. After the break.

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[Bel Lin]

Artificial intelligence is just like us, sort of Scientists and researchers are working on developing AI's long thinking capabilities, quite literally the ability for the technology to think for a longer period of time. That may just be AI's next leap forward as AI companies and researchers race to build up more computing firepower and improve the intelligence of their AI models. While this advancement is still in its early stages, some experts say it's on track to improve significantly very soon. For more on why we need AI to think for longer and why that's probably a good thing, we're joined by WSJ Enterprise Tech bureau chief and columnist Steven Rosenbush. Steven our listeners might be familiar with the idea that AIs can reason, but what's the idea behind long thinking?

[Steven Rosenbush]

Well, we've all been blown away over the last few years by the incredible speed with which AI tackles all sorts of of problems. But the next stage of AI may be even more powerful and as it's allowed to take even more time to solve more complex problems. And it draws on this system of thought, or inspired by this system of thought that psychologists refer to as System two. System one is quick, instantaneous, doesn't have to work very hard. It's almost instinctive. And that's sort of where most generative AI is located right now. But there's this shift underway at OpenAI and underway at Nvidia and other tech companies to embrace this sort of System two model of thinking. And we've seen this with what's known as the O1 series of models at OpenAI. But Nvidia's advancing this research, Salesforce is advancing this research, so it's beginning to enter the market in force.

[Bel Lin]

Okay, Stephen, so it sounds like System two thinking allocates attention to these effortful mental activities like complex computations. But what about AGI, the idea of artificial general intelligence? Does that move us into System 3 thinking?

[Steven Rosenbush]

I don't know that we have a System 3 yet. But when I spoke to Srinivas Narayanan, the engineer at OpenAI about the O1 series models, he did say that the O1 series models are going to lead to the Release of what OpenAI refers to as AI agents. The agents are in turn a step along the path toward some sort of AGI or think human, like broad based reasoning.

[Bel Lin]

Let's talk about the benefits of long thinking. What does that really mean for these AI models? Our chatgpt like chatbots, for them to be able to think in the long term or long think.

[Steven Rosenbush]

You may have noticed that the AI that we now have and presumably use will make mistakes. It will hallucinate. And also, if you're really, really maxing out AI models today, you do hit a wall. Like, there are really complex problems in science, math that AI that we're working with right now isn't so great at. And the O1 models developed by OpenAI can take more time to solve more complex problems in areas such as math, coding and science that OpenAI says its earlier models weren't as adept at.

[Bel Lin]

So in those areas, we could see a noticeable improvement in the output from these models.

[Steven Rosenbush]

That is the idea. The AI that we're working with right now tends to double down on a strategy and produce an answer, because that's what it must do. And that's one reason why you end up hallucinations. The O1 models have the capacity to step back and say, maybe this isn't the best approach. Let me try another approach to solve the problem. And that's why it's able to take more time and it's able to reason a little bit more. There's a certain self critical element that's been introduced.

[Bel Lin]

What happens when these AI models think for a long time? How much longer do we have to wait for a response?

[Steven Rosenbush]

That's an interesting problem. As Nvidia CEO Jensen Huang said earlier this year, people in General are applying AI to problems that can take 100 days to address or solve. And you think about what certain models can do in the blink of an eye. Think about all of that computing power. Trained on a problem for 100 days, you're able to attack much more complicated problems, such as predicting the weather everywhere in the world, all the time, all at once, within a square kilometer. You're able to attack problems in genetics. As Katherine Brownstein, a researcher at Harvard Medical School, told me, you're able to maybe make more advances in personalized medicine.

[Bel Lin]

Are there any concerns around long thinking for AI models?

[Steven Rosenbush]

As in every discussion of AI, there's a need to think about how the technology is put to use, what guardrails are in place. Yes, you can apply this technology to solve all sorts of important societal problems, but you could also, in theory, use it to create all sorts of societal problems. And we need to think about how we cope with that. And at what level Is this something that we really want to continue to happen at the company level or the developer level? Do we want greater public oversight? All those questions need to be thought through, and we should probably be decisive, intentional about it.

[Bel Lin]

That was our Enterprise Tech bureau chief and columnist Steven Rosenbusch. And that's it for Tech News Briefing. Today's show was produced by Julie Chang. Logging off for the weekend, I'm your host, Belle Lynn. Additional support this week from Pierre, Biana May and Danny Lewis. Jessica Fenton and Michael Lavalle wrote our theme music. Our supervising producer is Catherine Millsop. Our development producer is Aisha El Muslim. Scott Salloway and Chris Sinsley are the deputy editors and Philana Patterson is the Wall Street Journal's head of news audio. We'll sign back in this afternoon with TNB Tech Minute. Thanks for listening.

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