Companies are pursuing climate solutions amid shifting U.S. politics and economic uncertainty. Drawing from MIT Technology Review’s 10 Climate Tech Companies to Watch list, this session highlights the most promising technologies—from electric trucks to gene-edited crops—and explores the challenges companies face in advancing climate progress today.

Speakers: Casey Crownhart, Senior Climate Reporter; James Temple, Senior Climate Editor; and Mary Beth Griggs, Science Editor

Recorded on October 28, 2025


Related Coverage:

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The market is officially three years post ChatGPT and many of the pundit bylines have shifted to using terms like “bubble” to suggest reasons behind generative AI not realizing material returns outside a handful of technology suppliers. 

In September, the MIT NANDA report made waves because the soundbite every author and influencer picked up on was that 95% of all AI pilots failed to scale or deliver clear and measurable ROI. McKinsey earlier published a similar trend indicating that agentic AI would be the way forward to achieve huge operational benefits for enterprises. At The Wall Street Journal’s Technology Council Summit, AI technology leaders recommended CIOs stop worrying about AI’s return on investment because measuring gains is difficult and if they were to try, the measurements would be wrong. 

This places technology leaders in a precarious position–robust tech stacks already sustain their business operations, so what is the upside to introducing new technology? 

For decades, deployment strategies have followed a consistent cadence where tech operators avoid destabilizing business-critical workflows to swap out individual components in tech stacks. For example, a better or cheaper technology is not meaningful if it puts your disaster recovery at risk. 

While the price might increase when a new buyer takes over mature middleware, the cost of losing part of your enterprise data because you are mid-way through transitioning your enterprise to a new technology is way more severe than paying a higher price for a stable technology that you’ve run your business on for 20 years.

So, how do enterprises get a return on investing in the latest tech transformation?

First principle of AI: Your data is your value

Most of the articles about AI data relate to engineering tasks to ensure that an AI model infers against business data in repositories that represent past and present business realities. 

However, one of the most widely-deployed use cases in enterprise AI begins with prompting an AI model by uploading file attachments into the model. This step narrows an AI model’s range to the content of the uploaded files, accelerating accurate response times and reducing the number of prompts required to get the best answer. 

This tactic relies upon sending your proprietary business data into an AI model, so there are two important considerations to take in parallel with data preparation: first, governing your system for appropriate confidentiality; and second, developing a deliberate negotiation strategy with the model vendors, who cannot advance their frontier models without getting access to non-public data, like your business’ data. 

Recently, Anthropic and OpenAI completed massive deals with enterprise data platforms and owners because there is not enough high-value primary data publicly available on the internet. 

Most enterprises would automatically prioritize confidentiality of their data and design business workflows to maintain trade secrets. From an economic value point of view, especially considering how costly every model API call really is, exchanging selective access to your data for services or price offsets may be the right strategy. Rather than approaching model purchase/onboarding as a typical supplier/procurement exercise, think through the potential to realize mutual benefits in advancing your suppliers’ model and your business adoption of the model in tandem.

Second principle of AI: Boring by design

According to Information is Beautiful, in 2024 alone, 182 new generative AI models were introduced to the market. When GPT5 came into the market in 2025, many of the models from 12 to 24 months prior were rendered unavailable until subscription customers threatened to cancel. Their previously stable AI workflows were built on models that no longer worked. Their tech providers thought the customers would be excited about the newest models and did not realize the premium that business workflows place on stability. Video gamers are happy to upgrade their custom builds throughout the entire lifespan of the system components in their gaming rigs, and will upgrade the entire system just to play a newly released title. 

However, behavior does not translate to business run rate operations. While many employees may use the latest models for document processing or generating content, back-office operations can’t sustain swapping a tech stack three times a week to keep up with the latest model drops. The back-office work is boring by design.

The most successful AI deployments have focused on deploying AI on business problems unique to their business, often running in the background to accelerate or augment mundane but mandated tasks. Relieving legal or expense audits from having to manually cross check individual reports but putting the final decision in a humans’ responsibility zone combines the best of both. 

The important point is that none of these tasks require constant updates to the latest model to deliver that value. This is also an area where abstracting your business workflows from using direct model APIs can offer additional long-term stability while maintaining options to update or upgrade the underlying engines at the pace of your business.

Third principle of AI: Mini-van economics

The best way to avoid upside-down economics is to design systems to align to the users rather than vendor specs and benchmarks. 

Too many businesses continue to fall into the trap of buying new gear or new cloud service types based on new supplier-led benchmarks rather than starting their AI journey from what their business can consume, at what pace, on the capabilities they have deployed today. 

While Ferrari marketing is effective and those automobiles are truly magnificent, they drive the same speed through school zones and lack ample trunk space for groceries. Keep in mind that every remote server and model touched by a user layers on the costs and design for frugality by reconfiguring workflows to minimize spending on third-party services. 

Too many companies have found that their customer support AI workflows add millions of dollars of operational run rate costs and end up adding more development time and cost to update the implementation for OpEx predictability. Meanwhile, the companies that decided that a system running at the pace a human can read—less than 50 tokens per second—were able to successfully deploy scaled-out AI applications with minimal additional overhead.

There are so many aspects of this new automation technology to unpack—the best guidance is to start practical, design for independence in underlying technology components to keep from disrupting stable applications long term, and to leverage the fact that AI technology makes your business data valuable to the advancement of your tech suppliers’ goals.

This content was produced by Intel. It was not written by MIT Technology Review’s editorial staff.

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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

“We will never build a sex robot,” says Mustafa Suleyman

Mustafa Suleyman, CEO of Microsoft AI, is trying to walk a fine line. On the one hand, he thinks that the industry is taking AI in a dangerous direction by building chatbots that present as human: He worries that people will be tricked into seeing life instead of lifelike behavior.

On the other hand, Suleyman runs a product shop that must compete with those peers. Last week, Microsoft announced a string of updates to its Copilot chatbot designed to make Copilot more expressive, engaging, and helpful.

Will Douglas Heaven, our senior AI editor, talked to Suleyman about the tension at play when it comes to designing our interactions with chatbots and his ultimate vision for what this new technology should be. Read the full story.

An AI adoption riddle

—James O’Donnell, senior AI reporter 

A few weeks ago, I set out on what I thought would be a straightforward reporting journey.

After years of momentum for AI, hype had been slightly punctured. First there was the underwhelming release of GPT-5 in August. Then a report released two weeks later found that 95% of generative AI pilots were failing, which caused a brief stock market panic. I wanted to know: Which companies are spooked enough to scale back their AI spending?

But if AI’s hype has indeed been punctured, I couldn’t find a company willing to talk about it. So what should we make of my failed quest?

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Hundreds of thousands of ChatGPT users exhibit severe mental health symptoms
That’s according to estimates from OpenAI, which says it has tweaked GPT-5 to respond more effectively to users in distress. (Wired $)
+ OpenAI won’t lock access to force users to take a break, though. (Gizmodo)
+ Why AI should be able to “hang up” on you. (MIT Technology Review)

2 Elon Musk has launched his answer to Wikipedia
Grokipedia’s right-leaning entries reflect the way the billionaire sees the world. (WP $)
+ Several pages perpetuate historical inaccuracies and conservative views. (Wired $)
+ The AI-generated encyclopedia briefly crashed shortly after it launched. (Engadget)

3 Surgeons have removed a pig kidney from a patient
It was the longest-functioning genetically engineered pig kidney so far. (Wired $)
+ “Spare” living human bodies might provide us with organs for transplantation. (MIT Technology Review)

4 Amazon is planning to cut up to 30,000 corporate jobs
Partly in response to staff’s reluctance to return to the office five days a week. (Reuters)
+ The company is planning yet another round of layoffs in January. (NYT $)

5 Older people can’t get enough of screens
Their digital habits mirror the high usage typically observed among teenagers. (Economist $)

6 A British cyclist has been given a 3D-printed face
Dave Richards received severe third-degree burns to his head after being struck by a drunk driver. (The Guardian)

7 The twitter.com domain is being shut down
Make sure you re-enroll your security and passkeys before the big switch-off. (Fast Company $)
+ It means the abandoned accounts could be sold on. (The Verge)
+ But 2FA apps should be fine—in theory. (The Register)

8 When is a moon not a moon?
Believe it or not, we don’t have an official definition. (The Atlantic $)
+ Astronomers have spotted a “quasi-moon” hovering near Earth. (BBC)
+ The moon is just the beginning for this waterless concrete. (MIT Technology Review)

9 Threads’ ghost posts will disappear after 24 hours
If anyone saw them in the first place, that is. (TechCrunch)

10 In the metaverse, anyone can be a K-pop superstar
Virtual idols are gaining huge popularity, before crossing over into real-world fame. (Rest of World)
+ Meta’s former metaverse head has been moved into its AI team. (FT $)

Quote of the day

“The impulse to control knowledge is as old as knowledge itself. Controlling what gets written is a way to gain or keep power.”

—Ryan McGrady, senior research fellow at the University of Massachusetts Amherst, reflects on Elon Musk’s desire to create his own online encyclopedia to the New York Times.

One more thing

Inside Amsterdam’s high-stakes experiment to create fair welfare AI

Amsterdam thought it was on the right track. City officials in the welfare department believed they could build technology that would prevent fraud while protecting citizens’ rights. They followed these emerging best practices and invested a vast amount of time and money in a project that eventually processed live welfare applications. But in their pilot, they found that the system they’d developed was still not fair and effective. Why?

Lighthouse Reports, MIT Technology Review, and the Dutch newspaper Trouw have gained unprecedented access to the system to try to find out. Read about what we discovered.

—Eileen Guo, Gabriel Geiger & Justin-Casimir Braun

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.)

+ Happy 70th birthday to Bill Gates, who is not revered enough for his chair-jumping skills.
+ Bring back Guitar Hero—the iconic game that convinced us all we were capable of knocking out Heart’s Barracuda (note: the majority of us were not.)
+ Even the swankiest parts of London aren’t immune to rumours of ghostly hauntings.
+ Justice for medieval frogs and their unfair reputation! 🐸

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Mustafa Suleyman, CEO of Microsoft AI, is trying to walk a fine line. On the one hand, he thinks that the industry is taking AI in a dangerous direction by building chatbots that present as human: He worries that people will be tricked into seeing life instead of lifelike behavior. In August, he published a much-discussed post on his personal blog that urged his peers to stop trying to make what he called “seemingly conscious artificial intelligence,” or SCAI.

On the other hand, Suleyman runs a product shop that must compete with those peers. Last week, Microsoft announced a string of updates to its Copilot chatbot, designed to boost its appeal in a crowded market in which customers can pick and choose between a pantheon of rival bots that already includes ChatGPT, Perplexity, Gemini, Claude, DeepSeek, and more.

I talked to Suleyman about the tension at play when it comes to designing our interactions with chatbots and his ultimate vision for what this new technology should be.

One key Copilot update is a group-chat feature that lets multiple people talk to the chatbot at the same time. A big part of the idea seems to be to stop people from falling down a rabbit hole in a one-on-one conversation with a yes-man bot. Another feature, called Real Talk, lets people tailor how much Copilot pushes back on you, dialing down the sycophancy so that the chatbot challenges what you say more often.

Copilot also got a memory upgrade, so that it can now remember your upcoming events or long-term goals and bring up things that you told it in past conversations. And then there’s Mico, an animated yellow blob—a kind of Chatbot Clippy—that Microsoft hopes will make Copilot more accessible and engaging for new and younger users.  

Microsoft says the updates were designed to make Copilot more expressive, engaging, and helpful. But I’m curious how far those features can be pushed without starting down the SCAI path that Suleyman has warned about.  

Suleyman’s concerns about SCAI come at a time when we are starting to hear more and more stories about people being led astray by chatbots that are too engaging, too expressive, too helpful. OpenAI is being sued by the parents of a teenager who they allege was talked into killing himself by ChatGPT. There’s even a growing scene that celebrates romantic relationships with chatbots.

With all that in mind, I wanted to dig a bit deeper into Suleyman’s views. Because a couple of years ago he gave a TED Talk in which he told us that the best way to think about AI is as a new kind of digital species. Doesn’t that kind of hype feed the misperceptions Suleyman is now concerned about?  

In our conversation, Suleyman told me what he was trying to get across in that TED Talk, why he really believes SCAI is a problem, and why Microsoft would never build sex robots (his words). He had a lot of answers, but he left me with more questions.

Our conversation has been edited for length and clarity.

In an ideal world, what kind of chatbot do you want to build? You’ve just launched a bunch of updates to Copilot. How do you get the balance right when you’re building a chatbot that has to compete in a market in which people seem to value humanlike interaction, but you also say you want to avoid seemingly conscious AI?

It’s a good question. With group chat, this will be the first time that a large group of people will be able to speak to an AI at the same time. It really is a way of emphasizing that AIs shouldn’t be drawing you out of the real world. They should be helping you to connect, to bring in your family, your friends, to have community groups, and so on.

That is going to become a very significant differentiator over the next few years. My vision of AI has always been one where an AI is on your team, in your corner.

This is a very simple, obvious statement, but it isn’t about exceeding and replacing humanity—it’s about serving us. That should be the test of technology at every step. Does it actually, you know, deliver on the quest of civilization, which is to make us smarter and happier and more productive and healthier and stuff like that?

So we’re just trying to build features that constantly remind us to ask that question, and remind our users to push us on that issue.

Last time we spoke, you told me that you weren’t interested in making a chatbot that would role-play personalities. That’s not true of the wider industry. Elon Musk’s Grok is selling that kind of flirty experience. OpenAI has said it’s interested in exploring new adult interactions with ChatGPT. There’s a market for that. And yet this is something you’ll just stay clear of?

Yeah, we will never build sex robots. Sad in a way that we have to be so clear about that, but that’s just not our mission as a company. The joy of being at Microsoft is that for 50 years, the company has built, you know, software to empower people, to put people first.

Sometimes, as a result, that means the company moves slower than other startups and is more deliberate and more careful. But I think that’s a feature, not a bug, in this age, when being attentive to potential side effects and longer-term consequences is really important.

And that means what, exactly?

We’re very clear on, you know, trying to create an AI that fosters a meaningful relationship. It’s not that it’s trying to be cold and anodyne—it cares about being fluid and lucid and kind. It definitely has some emotional intelligence.

So where does it—where do you—draw those boundaries?

Our newest chat model, which is called Real Talk, is a little bit more sassy. It’s a bit more cheeky, it’s a bit more fun, it’s quite philosophical. It’ll happily talk about the big-picture questions, the meaning of life, and so on. But if you try and flirt with it, it’ll push back and it’ll be very clear—not in a judgmental way, but just, like: “Look, that’s not for me.”

There are other places where you can go to get that kind of experience, right? And I think that’s just a decision we’ve made as a company.

Is a no-flirting policy enough? Because if the idea is to stop people even imagining an entity, a consciousness, behind the interactions, you could still get that with a chatbot that wanted to keep things SFW. You know, I can imagine some people seeing something that’s not there even with a personality that’s saying, hey, let’s keep this professional.

Here’s a metaphor to try to make sense of it. We hold each other accountable in the workplace. There’s an entire architecture of boundary management, which essentially sculpts human behavior to fit a mold that’s functional and not irritating.

The same is true in our personal lives. The way that you interact with your third cousin is very different to the way you interact with your sibling. There’s a lot to learn from how we manage boundaries in real human interactions.

It doesn’t have to be either a complete open book of emotional sensuality or availability—drawing people into a spiraled rabbit hole of intensity—or, like, a cold dry thing. There’s a huge spectrum in between, and the craft that we’re learning as an industry and as a species is to sculpt these attributes.

And those attributes obviously reflect the values of the companies that design them. And I think that’s where Microsoft has a lot of strengths, because our values are pretty clear, and that’s what we’re standing behind.

A lot of people seem to like personalities. Some of the backlash to GPT-5, for example, was because the previous model’s personality had been taken away. Was it a mistake for OpenAI to have put a strong personality there in the first place, to give people something that they then missed?

No, personality is great. My point is that we’re trying to sculpt personality attributes in a more fine-grained way, right?

Like I said, Real Talk is a cool personality. It’s quite different to normal Copilot. We are also experimenting with Mico, which is this visual character, that, you know, people—some people—really love. It’s much more engaging. It’s easier to talk to about all kinds of emotional questions and stuff.

I guess this is what I’m trying to get straight. Features like Mico are meant to make Copilot more engaging and nicer to use, but it seems to go against the idea of doing whatever you can to stop people thinking there’s something there that you are actually having a friendship with.

Yeah. I mean, it doesn’t stop you necessarily. People want to talk to somebody, or something, that they like. And we know that if your teacher is nice to you at school, you’re going to be more engaged. The same with your manager, the same with your loved ones. And so emotional intelligence has always been a critical part of the puzzle, so it’s not to say that we don’t want to pursue it.

It’s just that the craft is in trying to find that boundary. And there are some things which we’re saying are just off the table, and there are other things which we’re going to be more experimental with. Like, certain people have complained that they don’t get enough pushback from Copilot—they want it to be more challenging. Other people aren’t looking for that kind of experience—they want it to be a basic information provider. The task for us is just learning to disentangle what type of experience to give to different people.

I know you’ve been thinking about how people engage with AI for some time. Was there an inciting incident that made you want to start this conversation in the industry about seemingly conscious AI?

I could see that there was a group of people emerging in the academic literature who were taking the question of moral consideration for artificial entities very seriously. And I think it’s very clear that if we start to do that, it would detract from the urgent need to protect the rights of many humans that already exist, let alone animals.

If you grant AI rights, that implies—you know—fundamental autonomy, and it implies that it might have free will to make its own decisions about things. So I’m really trying to frame a counter to that, which is that it won’t ever have free will. It won’t ever have complete autonomy like another human being.

AI will be able to take actions on our behalf. But these models are working for us. You wouldn’t want a pack of, you know, wolves wandering around that weren’t tame and that had complete freedom to go and compete with us for resources and weren’t accountable to humans. I mean, most people would think that was a bad idea and that you would want to go and kill the wolves.

Okay. So the idea is to stop some movement that’s calling for AI welfare or rights before it even gets going, by making sure that we don’t build AI that appears to be conscious? What about not building that kind of AI because certain vulnerable people may be tricked by it in a way that may be harmful? I mean, those seem to be two different concerns.

I think the test is going to be in the kinds of features the different labs put out and in the types of personalities that they create. Then we’ll be able to see how that’s affecting human behavior.

But is it a concern of yours that we are building a technology that might trick people into seeing something that isn’t there? I mean, people have claimed they’ve seen sentience inside far less sophisticated models than we have now. Or is that just something that some people will always do?

It’s possible. But my point is that a responsible developer has to do our best to try and detect these patterns emerging in people as quickly as possible and not take it for granted that people are going to be able to disentangle those kinds of experiences themselves.

When I read your post about seemingly conscious AI, I was struck by a line that says: “We must build AI for people; not to be a digital person.” It made me think of a TED Talk you gave last year where you say that the best way to think about AI is as a new kind of digital species. Can you help me understand why talking about this technology as a digital species isn’t a step down the path of thinking about AI models as digital persons or conscious entities?

I think the difference is that I’m trying to offer metaphors that make it easier for people to understand where things might be headed, and therefore how to avert that and how to control it.

Okay.

It’s not to say that we should do those things. It’s just pointing out that this is the emergence of a technology which is unique in human history. And if you just assume that it’s a tool or just a chatbot or a dumb— you know, I kind of wrote that TED Talk in the context of a lot of skepticism. And I think it’s important to be clear-eyed about what’s coming so that one can think about the right guardrails.

And yet, if you’re telling me this technology is a new digital species, I have some sympathy for the people who say, well, then we need to consider welfare.

I wouldn’t. [He starts laughing.] Just not in the slightest. No way. It’s not a direction that any of us want to go in.

No, that’s not what I meant. I don’t think chatbots should have welfare. I’m saying I’d have some sympathy for where such people were coming from when they hear, you know, Mustafa Suleyman tell them that this thing he’s building was a new digital species. I’d understand why they might then say that they wanted to stand up for it. I’m saying the words we use matter, I guess.

The rest of the TED Talk was all about how to contain AI and how not to let this species take over, right? That was the whole point of setting it up as, like, this is what’s coming. I mean, that’s what my whole book [The Coming Wave, published in 2023] was about—containment and alignment and stuff like that. There’s no point in pretending that it’s something that it’s not and then building guardrails and boundaries that don’t apply because you think it’s just a tool.

Honestly, it does have the potential to recursively self-improve. It does have the potential to set its own goals. Those are quite profound things. No other technology we’ve ever invented has that. And so, yeah, I think that it is accurate to say that it’s like a digital species, a new digital species. That’s what we’re trying to restrict to make sure it’s always in service of people. That’s the target for containment.

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A few weeks ago, I set out on what I thought would be a straightforward reporting journey. 

After years of momentum for AI—even if you didn’t think it would be good for the world, you probably thought it was powerful enough to take seriously—hype for the technology had been slightly punctured. First there was the underwhelming release of GPT-5 in August. Then a report released two weeks later found that 95% of generative AI pilots were failing, which caused a brief stock market panic. I wanted to know: Which companies are spooked enough to scale back their AI spending?

I searched and searched for them. As I did, more news fueled the idea of an AI bubble that, if popped, would spell doom economy-wide. Stories spread about the circular nature of AI spending, layoffs, the inability of companies to articulate what exactly AI will do for them. Even the smartest people building modern AI systems were saying the tech has not progressed as much as its evangelists promised. 

But after all my searching, companies that took these developments as a sign to perhaps not go all in on AI were nowhere to be found. Or, at least, none that were willing to admit it. What gives? 

There are several interpretations of this one reporter’s quest (which, for the record, I’m presenting as an anecdote and not a representation of the economy), but let’s start with the easy ones. First is that this is a huge score for the “AI is a bubble” believers. What is a bubble if not a situation where companies continue to spend relentlessly even in the face of worrying news? The other is that underneath the bad headlines, there’s not enough genuinely troubling news about AI to convince companies they should pivot.

But it could also be that the unbelievable speed of AI progress and adoption has made me think industries are more sensitive to news than they perhaps should be. I spoke with Martha Gimbel, who leads the Yale Budget Lab and coauthored a report finding that AI has not yet changed anyone’s jobs. What I gathered is that Gimbel, like many economists, thinks on a longer time scale than anyone in the AI world is used to. 

“It would be historically shocking if a technology had had an impact as quickly as people thought that this one was going to,” she says. In other words, perhaps most of the economy is still figuring out what the hell AI even does, not deciding whether to abandon it. 

The other reaction I heard—particularly from the consultant crowd—is that when executives hear that so many AI pilots are failing, they indeed take it very seriously. They’re just not reading it as a failure of the technology itself. They instead point to pilots not moving quickly enough, companies lacking the right data to build better AI, or a host of other strategic reasons.

Even if there is incredible pressure, especially on public companies, to invest heavily in AI, a few have taken big swings on the technology only to pull back. The buy now, pay later company Klarna laid off staff and paused hiring in 2024, claiming it could use AI instead. Less than a year later it was hiring again, explaining that “AI gives us speed. Talent gives us empathy.” 

Drive-throughs, from McDonald’s to Taco Bell, ended pilots testing the use of AI voice assistants. The vast majority of Coca-Cola advertisements, according to experts I spoke with, are not made with generative AI, despite the company’s $1 billion promise. 

So for now, the question remains unanswered: Are there companies out there rethinking how much their bets on AI will pay off, or when? And if there are, what’s keeping them from talking out loud about it? (If you’re out there, email me!)

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Building an AI CMO That Becomes Your Strategic Marketing Partner by Social Media Examiner

Are you treating AI like an intern when it could be your chief marketing officer? Wondering how to transform AI from a basic task assistant into a strategic partner for your business? In this article, you’ll discover how to build your very own AI chief marketing officer that can help you analyze data, provide strategic […]

The post Building an AI CMO That Becomes Your Strategic Marketing Partner appeared first on Social Media Examiner.

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