According to a panel of hundreds of artificial intelligence researchers, the field is currently pursuing artificial general intelligence the wrong way. This insight was revealed at the Association for the Advancement of Artificial Intelligence (AAAI)’s 2025 Presidential Panel on the Future of AI Research. The lengthy report was put together by 24 AI researchers whose expertise ranges from the state of AI infrastructure to the social aspects of artificial intelligence.

The report included a main takeaway for each section, as well as a community opinion section where respondents were asked their own thoughts about the section.

The section on AI Perception vs. Reality, chaired by MIT computer scientist Rodney Brooks, referenced the Gartner Hype Cycle characterization, a five-stage cycle common for technology hype. In November 2024, Gartner estimated that hype for Generative AI had just passed its peak and was on the downswing, the report noted. 79% of respondents in the community opinion section stated that current public perceptions of AI’s capabilities do not match the reality of AI research and development, with 90% saying that the mismatch is hindering AI research—74% of that number saying that “the directions of AI research are driven by the hype.

I included the Gartner Hype Cycle as it is something they have used for years and is a generalization of all the hype up followed by disappointment across so many different fields, Brooks told Gizmodo in an email, and therefore its existence and accuracy over so many fields tells us something about how we should use caution in accepting the current levels of hype about what is coming, any second now, with some caution.

More on the AAAI’s 2025 lengthy report on Gizmodo

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Right now, in the tech industry paddock, there are 372 AI unicorns, according to PitchBook. How did you react to that number? Did you double-take? Did you recoil slightly, finding it shockingly high? Far too low? Or did you shrug as if to say that tracks? I don’t think there’s a right or wrong response here—my point, rather, is that it’s likely you’ll have some kind of reaction to that number. And that’s representative of the extent to which the AI boom has created so many undercurrents of optimism and skepticism. A unicorn, by the way, is a privately-held company valued at $1 billion or more. The term was coined by VC Aileen Lee in 2013.

The AI investing landscape is very competitive at the moment, said Kyle Stanford, PitchBook director of research for U.S. venture, via email. We are seeing around 1/3rd of completed deals go to companies in the AI space across all industries, so that encompasses much more of the market than just the handful of unicorns that dominate the headlines.

It’s worth breaking down the list. Though I expect it will look different in a year, it’s surprising for its breadth. In order, the top 10 by valuation as of their last (fully confirmed) funding round: ByteDance ($220 billion), OpenAI ($157 billion), Stripe ($70 billion), Databricks ($62 billion), Anthropic ($61.5 billion), xAI ($50 billion), Waymo ($45 billion), Epic Games ($31.5 billion), Canva ($26 billion), and CoreWeave ($23 billion).

And the next five names rounding out the top 15: China’s JD Digits, Anduril, Scale AI, Grammarly, and Ramp. Across these names alone, various industries are represented, including infrastructure, data analytics, language technology, social media, fintech, e-commerce, autonomous vehicles, defense tech, and design. This is a theme that runs all the way down the list, Aditi Maliwal, partner at Upfront Ventures, pointed out to me via email: 60-70% of the AI unicorns being funded are application layer companies specifically around real-world applications of AI—in healthcare, defense, robotics and autonomous systems.

More on Pitchbook’s 371 AI Unicorns on Fortune

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Apple has traditionally not been a big player in artificial intelligence model training or server infrastructure. But that might change pretty soon. Apple is officially in the large server cluster Gen AI game... and Super Micro Computer & Dell are the key server partners, Loop Capital Markets analyst Ananda Baruah said in an analyst note on Monday. Large server clusters are big groups of high-powered servers used to train and run complex AI models, like those used in generative AI.

The tech giant is reportedly placing roughly $1 billion worth of orders of Nvidia’s GB300 NVL72 server platform, including the company’s next-generation Blackwell Ultra chips, built by Super Micro and Dell. With each server costing around $3.7 million to $4 million, Baruah estimates that Apple is buying approximately 250 servers. Apple didn’t immediately respond to Quartz’s request for comment.

Baruah expects Apple to use these servers to run or train generative AI large language models. The move could have stemmed from the intense backlash Apple received in response to its decision to delay a much-anticipated generative AI upgrade of its voice assistant Siri. Apple began working on integrating advanced AI technology into its products as part of its Apple Intelligence initiative, which the company introduced last June at its annual developer conference, WWDC.

The tech giant first teased a so-called LLM Siri based on advanced large language models last year, in an effort to scale its generative AI capabilities and catch up to industry rivals like OpenAI and Amazon. Although an arrival date was never publicly set, LLM Siri was widely anticipated to come in an iOS 18.4 upgrade expected next month. Now, the AI-infused Siri will likely be unveiled next year. Apple pulled its previous ads featuring the capability.

More on Apple’s AI server purchase and AI efforts on Quartz

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We’re coming up on the one year anniversary since OpenAI released its first omni or multimodal model, GPT-4o back in May 2024, but that old standby still has some tricks up its sleeve. Case-in-point, today OpenAI finally turned on the native multimodal image generation capabilities of GPT-4o for users of its hit chatbot ChatGPT on the Plus, Pro, Team, and Free usage tiers, though the company said it would also soon be made available for Enterprise, Edu, and through its application programming interface (API).

Unlike the previous generative AI image model available in ChatGPT — OpenAI’s DALL-E 3, a classic diffusion transformer model that was trained to reconstruct images from text prompts by removing noise from pixels — this new image generator is part of the same model that spits out text and code, as OpenAI trained the entire model to understand all these forms of media at once.

OpenAI president Greg Brockman had long ago previewed this native capability of GPT-4o back in May 2024, but for reasons that still remain unknown publicly, the company held onto it until now — following the public release of what many AI power users saw as a similar feature from Google AI Studio with its Gemini 2 Flash Experimental model.

This has resulted in a much higher quality image generator that produces far more lifelike images and accurate text baked in, and it’s already impressing users — one of whom calls the quality insane. By the same token (pun intended), OpenAI still hasn’t said precisely what data GPT-4o’s image generation capabilities were trained on — and given the history of the company and other model providers, it likely includes many artworks scraped from the web, some of which are presumably copyrighted, which is likely to anger the artists behind them.

More on OpenAI’s GTP 4o native image generation on VentureBeat

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I don’t know what your social media feed looks like these days, but mine is filled with March Madness, memes, and a relentless catalog of AI startups’ impressive (and rapidly attained) revenue figures. Here is a very incomplete selection of what I’m talking about here:

In less than three years, Midjourney’s annual recurring revenue (or ARR) soared from zero to $200 million. Over about 24 months, ElevenLabs, a voice AI leader, saw its ARR soar from zero to near $100 million. In three months, Lovable, an AI app-building startup, went from zero to $17 million in ARR.

And, perhaps most famously, AI coding juggernaut Cursor went from nothing to $100 million in ARR in one year. (For the uninitiated, ARR is the metric that counts how much predictable revenue a company can anticipate generating over one year, frequently from subscriptions or contracts.) [Editor’s Note] Also, Perplexity is said to have recently attained $100M ARR, and is fundraising on that milestone.

These are just some of the numbers I’ve watched glaze excitedly around social media. And the excitement seems very fair—bolstered by AI, companies seem to have the capacity to grow faster than we’ve ever seen before. But I did wonder: How seriously should I be taking revenue growth that’s just so darn fast? Can something that good materialize that quickly?

The resounding answer is a firm ‘it depends,' said Rebecca Lynn, Canvas Ventures managing director. This is really where our job as investors becomes both interesting and hard. We see, time and again, that even pre-revenue companies can raise a lot of money, but can’t necessarily get their demo into the wild.

We’re really careful about that. Lynn continued: Now, the next piece is revenue—we’ve seen a lot of companies scale very quickly, hit something like $30 million, and then flatline. So what you really have to ask is: Where are the end consumers? We really need to understand their usage, because you want to see that revenue is not ephemeral.

More about AI’s Annual Recurring Revenue on Fortune

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AI-powered search startup Perplexity is said to be in early talks to raise up to $1 billion in a new funding round valuing the startup at $18 billion. A person familiar with the matter told Reuters on Thursday, as the Nvidia-backed startup looks to grow and capture soaring demand for its search tools.

Bloomberg, citing a person familiar with the matter, reported on Thursday that Perplexity’s annual recurring revenue has now reached $100 million. Perplexity’s valuation has soared in recent years. This new round would double Perplexity’s current valuation — $9 billion — as of December. In April 2024, Perplexity was valued at $1 billion.

Rumors of a new round come as competition in the AI-powered search space heats up.

On Thursday, Anthropic added web search to its consumer AI chatbot, Claude. Earlier in March, Google launched an early version of a chat-based AI search engine, called AI Mode, for a limited group of testers.

Perhaps because its core market is becoming increasingly crowded, Perplexity is trying to expand into new areas. The startup recently teased the launch of an “agentic” browser called Comet, and it has ramped up its enterprise offerings, recently launching an AI search engine to help businesses retrieve information from internal documents.

More on Perplexity’s reported fundraising efforts on TechCrunch

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Jensen Huang is betting companies will continue to spend billions of dollars with Nvidia. His confidence rests on two key reasons — though investors may need extra convincing. Kickstarting Nvidia's annual GTC conference in a San Jose arena on Tuesday — a developer bash now dubbed the Super Bowl of AI — the billionaire CEO pointed to powerful new chips and Nvidia's role in the industry's shift to inference, which is when AI performs tasks, as future demand drivers.

Huang's big reveal was a new generation of Rubin chips, known as GPUs, that are expected to land next year and serve as a successor to the Blackwell and Hopper lines that have had the likes of Elon Musk, Sam Altman, and Mark Zuckerberg queuing up.

For context, an ultra version of Rubin — set for a release in the second of 2027 — would have 14 times the performance of an ultra version of Blackwell, which is expected to be released in the second half of this year. Huang promised further advances through 2028 by teasing a line of chips named after the physicist Richard Feynman. If such trends continue, these chips are expected to blow Rubin's performance out of the water once they arrive.

That's certainly one way of keeping customers with deep pockets coming back.

But after Nvidia suffered a $600 billion market capitalization loss in a single day in January following panic over DeepSeek, Huang was ready to offer another reason for why demand would stay — and it was a simple one. This is where almost the entire world got it wrong, Huang said during his GTC keynote, addressing the market's negative reactions to Nvidia after DeepSeek revealed a model that appeared to power its reasoning abilities in a phase called inference with fewer chips.

The amount of computation we need at this point as a result agentic AI, as a result of reasoning, is easily a 100 times more than we thought we needed this time last year, Huang added.

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For at least two decades, Software as a Service -- aka, SaaS -- has been at the center of the technology universe, with applications fully accessible via the internet. Now, artificial intelligence is turning this arrangement on its head, leveraging applications autonomously to deliver services. They're calling it Services as Software.

AI is creating this new world for technology professionals and their business counterparts. Historically, businesses bought software and hired human professionals to operate it, wrote Frank Diana, Tata Consultancy Services futurist, in a recent post. AI is flipping that model. Increasingly, businesses will design their processes around AI from the start -- not as an add-on, but as the default worker. This shift will impact nearly every function.

An HFS survey finds that 60% of 1,000 enterprises surveyed are already looking to procure services as technology offerings. The same amount plan to replace some or all of their professional services with some form of AI within the next three to five years.

Similar trends are in play as routine IT maintenance, HR, procurement, accounting, and customer service work are becoming much easier to replicate in advancing GenAI and agentic software, supported by public and private cloud capabilities to secure and scale transaction volumes, stated HFS authors Saurabh Gupta and Phil Fersht.

What once required teams of specialists is increasingly being handled by AI-powered systems capable of executing tasks autonomously, Diana said. This transformation isn't just about automation -- it's about redefining how businesses consume and deliver services. The emerging model marks a profound departure from the past: software is no longer a tool for human workers; in many cases, it is the worker.

More on Service as a Software on ZDNET

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All eyes will be on Nvidia’s GPU Technology Conference this week, where the company is expected to unveil its next artificial intelligence chips. Nvidia chief executive Jensen Huang said he will share more about the upcoming Blackwell Ultra AI chip, Vera Rubin platform, and plans for following products at the annual conference, known as the GTC, during the company’s fiscal fourth quarter earnings call.

On the earnings call, Huang said Nvidia has some really exciting things to share at the GTC about enterprise and agentic AI, reasoning models, and robotics. The chipmaker introduced its highly anticipated Blackwell AI platform at last year’s GTC, which has successfully ramped up large-scale production, and made billions of dollars in sales in its first quarter, according to Huang.

Analysts at Bank of America said in a note on Wednesday that they expect Nvidia to present attractive albeit well-expected updates on Blackwell Ultra, with a focus on inferencing for reasoning models, which major firms such as OpenAI and Google are racing to develop. The analysts also anticipate the chipmaker to share more information on its next-generation networking technology, and long-term opportunities in autonomous cars, physical AI such as robotics, and quantum computing.

In January, Nvidia announced that it would host its first Quantum Day at the GTC, and have executives from D-Wave and Rigetti discuss where quantum computing is headed. The company added that it will unveil quantum computing advances shortening the timeline to useful applications. The same month, quantum computing stocks tanked after Huang expressed doubts over the technology’s near-term potential during the chipmaker’s financial analyst day at the Consumer Electronics Show, saying useful quantum computers are likely decades away.

More on NVIDIA’s GPU Technology Conference on Quartz

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Big Tech companies are eyeing small modular reactors as an AI energy solution — even as some remain unsure about its potential. There's no official standard that defines a small modular reactor, or SMR, according to two leading nuclear experts who spoke with Business Insider. The industry consensus is that they produce less power — around 300 megawatts instead of the 1,000 megawatts, or 1 gigawatt — than a traditional reactor.

The SMR playing field is also relatively small. Data compiled from CB Insights and Pitchbook showed that over the past eight years, only four companies received about 92% of the nearly $3.2 billion in equity funding that went to SMR-focused firms. Those companies include X-energy, Bill Gates' Terrapower, NuScale, and the Paris-based Newcleo.

It's new, and it's old at the same time, Allison MacFarlane, former US Nuclear Regulatory Commission chairman, said of SMRs in an interview with Business Insider. MacFarlane was referring to how many SMR designs have yet to be demonstrated in real life, but the technology the reactor is based on has been around for many decades.

Tech companies like Alphabet, Amazon, and Microsoft are investing in the technology with the hope of finding a sustainable and reliable energy source to meet the increasing power demands of artificial intelligence and data centers.

In a March report, the Center for Strategic & International Studies estimated that new energy demands for all US data centers will reach 40 to 90 gigawatts by 2030. That's equivalent to simultaneously powering four to nine New York Cities during peak demands in the middle of the summer.

More on Small Modular Nuclear Reactors on Business Insider

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