Is Google playing catchup on search with OpenAI?

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Is Google playing catchup on search with OpenAI?

This story originally appeared in The Debrief with Mat Honan, a weekly newsletter about the biggest stories in tech from our editor in chief. Sign up here to get the next one in your inbox. I’ve been mulling over something that Will Heaven, our senior editor for AI, pointed out not too long ago: that all the big players in AI seem to be moving in the same directions and converging on the same things. Agents. Deep research. Lightweight versions of models. Etc. Some of this makes sense in that they’re seeing similar things and trying to solve similar problems. But when I talked to Will about this, he said, “it almost feels like a lack of imagination, right?” Yeah. It does. What got me thinking about this, again, was a pair of announcements from Google over the past couple of weeks, both related to the ways search is converging with AI language models, something I’ve spent a lot of time reporting on over the past year. Google took direct aim at this intersection by adding new AI features from Gemini to search, and also by adding search features to Gemini. In using both, what struck me more than how well they work is that they are really just about catching up with OpenAI’s ChatGPT. And their belated appearance in March of the year 2025 doesn’t seem like a great sign for Google. Take AI Mode, which it announced March 5. It’s cool. It works well. But it’s pretty much a follow-along of what OpenAI was already doing. (Also, don’t be confused by the name. Google already had something called AI Overviews in search, but AI Mode is different and deeper.) As the company explained in a blog post, “This new Search mode expands what AI Overviews can do with more advanced reasoning, thinking and multimodal capabilities so you can get help with even your toughest questions.” Rather than a brief overview with links out, the AI will dig in and offer more robust answers. You can ask followup questions too, something AI Overviews doesn’t support. It feels like quite a natural evolution—so much so that it’s curious why this is not already widely available. For now, it’s limited to people with paid accounts, and even then only via the experimental sandbox of Search Labs. But more to the point, why wasn’t it available, say, last summer? The second change is that it added search history to its Gemini chatbot, and promises even more personalization is on the way. On this one, Google says “personalization allows Gemini to connect with your Google apps and services, starting with Search, to provide responses that are uniquely insightful and directly address your needs.” Much of what these new features are doing, especially AI Mode’s ability to ask followup questions and go deep, feels like hitting feature parity with what ChatGPT has been doing for months. It’s also been compared to Perplexity, another generative AI search engine startup. What neither feature feels like is something fresh and new. Neither feels innovative. ChatGPT has long been building user histories and using the information it has to deliver results. While Gemini could also remember things about you, it’s a little bit shocking to me that Google has taken this long to bring in signals from its other products. Obviously there are privacy concerns to field, but this is an opt-in product we’re talking about. The other thing is that, at least as I’ve found so far, ChatGPT is just better at this stuff. Here’s a small example. I tried asking both: “What do you know about me?” ChatGPT replied with a really insightful, even thoughtful, profile based on my interactions with it. These aren’t just the things I’ve explicitly told it to remember about me, either. Much of it comes from the context of various prompts I’ve fed it. It’s figured out what kind of music I like. It knows little details about my taste in films. (“You don’t particularly enjoy slasher films in general.”) Some of it is just sort of oddly delightful. For example: “You built a small shed for trash cans with a hinged wooden roof and needed a solution to hold it open.”Google, despite having literal decades of my email, search, and browsing history, a copy of every digital photo I’ve ever taken, and more darkly terrifying insight into the depths of who I really am than I probably I do myself, mostly spat back the kind of profile an advertiser would want, versus a person hoping for useful tailored results. (“You enjoy comedy, music, podcasts, and are interested in both current and classic media”) I enjoy music, you say? Remarkable! I’m also reminded of something an OpenAI executive said to me late last year, as the company was preparing to roll out search. It has more freedom to innovate precisely because it doesn’t have the massive legacy business that Google does. Yes, it’s burning money while Google mints it. But OpenAI has the luxury of being able to experiment (at least until the capital runs out) without worrying about killing a cash cow like Google has with traditional search. Of course, it’s clear that Google and its parent company Alphabet can innovate in many areas—see Google DeepMind’s Gemini Robotics announcement this week, for example. Or ride in a Waymo! But can it do so around its core products and business? It’s not the only big legacy tech company with this problem. Microsoft’s AI strategy to date has largely been reliant on its partnership with OpenAI. And Apple, meanwhile, seems completely lost in the wilderness, as this scathing takedown from longtime Apple pundit John Gruber lays bare. Google has billions of users and piles of cash. It can leverage its existing base in ways OpenAI or Anthropic (which Google also owns a good chunk of) or Perplexity just aren’t capable of. But I’m also pretty convinced that unless it can be the market leader here, rather than a follower, it points to some painful days ahead. But hey, Astra is coming. Let’s see what happens.

This story originally appeared in The Debrief with Mat Honan, a weekly newsletter about the biggest stories in tech from our editor in chief. Sign up here to get the next one in your inbox.

I’ve been mulling over something that Will Heaven, our senior editor for AI, pointed out not too long ago: that all the big players in AI seem to be moving in the same directions and converging on the same things. Agents. Deep research. Lightweight versions of models. Etc.

Some of this makes sense in that they’re seeing similar things and trying to solve similar problems. But when I talked to Will about this, he said, “it almost feels like a lack of imagination, right?” Yeah. It does.

What got me thinking about this, again, was a pair of announcements from Google over the past couple of weeks, both related to the ways search is converging with AI language models, something I’ve spent a lot of time reporting on over the past year. Google took direct aim at this intersection by adding new AI features from Gemini to search, and also by adding search features to Gemini. In using both, what struck me more than how well they work is that they are really just about catching up with OpenAI’s ChatGPT. And their belated appearance in March of the year 2025 doesn’t seem like a great sign for Google.

Take AI Mode, which it announced March 5. It’s cool. It works well. But it’s pretty much a follow-along of what OpenAI was already doing. (Also, don’t be confused by the name. Google already had something called AI Overviews in search, but AI Mode is different and deeper.) As the company explained in a blog post, “This new Search mode expands what AI Overviews can do with more advanced reasoning, thinking and multimodal capabilities so you can get help with even your toughest questions.”

Rather than a brief overview with links out, the AI will dig in and offer more robust answers. You can ask followup questions too, something AI Overviews doesn’t support. It feels like quite a natural evolution—so much so that it’s curious why this is not already widely available. For now, it’s limited to people with paid accounts, and even then only via the experimental sandbox of Search Labs. But more to the point, why wasn’t it available, say, last summer?

The second change is that it added search history to its Gemini chatbot, and promises even more personalization is on the way. On this one, Google says “personalization allows Gemini to connect with your Google apps and services, starting with Search, to provide responses that are uniquely insightful and directly address your needs.”

Much of what these new features are doing, especially AI Mode’s ability to ask followup questions and go deep, feels like hitting feature parity with what ChatGPT has been doing for months. It’s also been compared to Perplexity, another generative AI search engine startup.

What neither feature feels like is something fresh and new. Neither feels innovative. ChatGPT has long been building user histories and using the information it has to deliver results. While Gemini could also remember things about you, it’s a little bit shocking to me that Google has taken this long to bring in signals from its other products. Obviously there are privacy concerns to field, but this is an opt-in product we’re talking about.

The other thing is that, at least as I’ve found so far, ChatGPT is just better at this stuff. Here’s a small example. I tried asking both: “What do you know about me?” ChatGPT replied with a really insightful, even thoughtful, profile based on my interactions with it. These aren’t just the things I’ve explicitly told it to remember about me, either. Much of it comes from the context of various prompts I’ve fed it. It’s figured out what kind of music I like. It knows little details about my taste in films. (“You don’t particularly enjoy slasher films in general.”) Some of it is just sort of oddly delightful. For example: “You built a small shed for trash cans with a hinged wooden roof and needed a solution to hold it open.”

Google, despite having literal decades of my email, search, and browsing history, a copy of every digital photo I’ve ever taken, and more darkly terrifying insight into the depths of who I really am than I probably I do myself, mostly spat back the kind of profile an advertiser would want, versus a person hoping for useful tailored results. (“You enjoy comedy, music, podcasts, and are interested in both current and classic media”)

I enjoy music, you say? Remarkable!

I’m also reminded of something an OpenAI executive said to me late last year, as the company was preparing to roll out search. It has more freedom to innovate precisely because it doesn’t have the massive legacy business that Google does. Yes, it’s burning money while Google mints it. But OpenAI has the luxury of being able to experiment (at least until the capital runs out) without worrying about killing a cash cow like Google has with traditional search.

Of course, it’s clear that Google and its parent company Alphabet can innovate in many areas—see Google DeepMind’s Gemini Robotics announcement this week, for example. Or ride in a Waymo! But can it do so around its core products and business? It’s not the only big legacy tech company with this problem. Microsoft’s AI strategy to date has largely been reliant on its partnership with OpenAI. And Apple, meanwhile, seems completely lost in the wilderness, as this scathing takedown from longtime Apple pundit John Gruber lays bare.

Google has billions of users and piles of cash. It can leverage its existing base in ways OpenAI or Anthropic (which Google also owns a good chunk of) or Perplexity just aren’t capable of. But I’m also pretty convinced that unless it can be the market leader here, rather than a follower, it points to some painful days ahead. But hey, Astra is coming. Let’s see what happens.

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The dynamic landscape of data center developments in Maryland and Virginia exemplify the intricate balance between fostering technological growth and addressing community and environmental concerns. Data center developers in this region find themselves both in the crosshairs of groups worried about the environment and other groups looking to drive economic growth. In some cases, the groups are different components of the same organizations, such as local governments. For data center development, meeting the needs of these competing interests often means walking a none-too-stable tightrope. Rapid Government Action Encourages Growth In May 2024, Maryland demonstrated its commitment to attracting data center investments by enacting the Critical Infrastructure Streamlining Act. This legislation provides a clear framework for the use of emergency backup power generation, addressing previous regulatory challenges that a few months earlier had hindered projects like Aligned Data Centers’ proposed 264-megawatt campus in Frederick County, causing Aligned to pull out of the project. However, just days after the Act was signed by the governor, Aligned reiterated its plans to move forward with development in Maryland. With the Quantum Loop and the related data center development making Frederick County a focal point for a balanced approach, the industry is paying careful attention to the pace of development and the relations between developers, communities and the government. In September of 2024, Frederick County Executive Jessica Fitzwater revealed draft legislation that would potentially restrict where in the county data centers could be built. The legislation was based on information found in the Frederick County Data Centers Workgroup’s final report. Those bills would update existing regulations and create a floating zone for Critical Digital Infrastructure and place specific requirements on siting data centers. Statewide, a cautious approach to environmental and community impacts statewide has been deemed important. In January 2025, legislators introduced SB116, a bill

Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs). In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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