Radically different
Why does it take a new drug 10 years, on average, to come to market? Part of the reason lies in the dead time in the process.
Historically, trials have required tedious tabulations and repeated application submissions between phases, which is why 45% of the time from a Phase 1 trial until final submission is spent without any ongoing clinical trial in progress — idle time in the system.
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.
Elon Musk and OpenAI CEO Sam Altman head to trial this week in a case with sweeping consequences. Ahead of OpenAI’s IPO, the court could rule on whether the company can exist as a for-profit enterprise. It could even oust its leadership.
Musk, an OpenAI co-founder, claims he was deceived into bankrolling the firm under false pretenses. He’s seeking $134 billion in damages, the removal of Altman and president Greg Brockman, and the company’s restoration to a non-profit.
Find out how the trial could upend the global AI race.
—Michelle Kim
In a celebrated South Park episode, a community of gnomes sneak out at night to steal underpants. Why? The gnomes present their pitch deck. “Phase 1: Collect underpants. Phase 2: ? Phase 3: Profit.” It’s a business plan that captures the current state of AI.
Companies have built the tech (Step 1) and promised transformation (Step 3). But how they get there is still a big question mark. Read about the potential paths forward.
—Will Douglas Heaven
This story originally appeared in The Algorithm, our weekly newsletter giving you the inside track on all things AI. Sign up to receive it in your inbox every Monday.
For years, experts have warned that deepfakes could be deployed in malicious ways. These dangers are now here.
Cheap, accessible models now produce weaponized deepfakes—from sexually explicit images to political propaganda—that look startlingly real. They’re already inciting violence, changing minds, and sowing mistrust, with women and marginalized groups disproportionately affected.
Experts fear that they’re cratering trust and critical thinking. Here’s why they’re alarmed.
—Eileen Guo
Weaponized deepfakes are on our list of the 10 Things That Matter in AI Right Now, MIT Technology Review’s guide to what’s really worth your attention in the busy, buzzy world of AI.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 OpenAI has ended its exclusive partnership with Microsoft
The new deal allows OpenAI to court rivals such as Amazon. (Reuters $)
+ Microsoft will still license OpenAI’s tech, but no longer exclusively. (NYT $)
+ OpenAI is missing key growth targets ahead of its IPO. (WSJ $)
2 Google has signed a classified AI deal with the Pentagon
It permits AI use for “any lawful government purpose.” (The Information $)
+ Over 600 Google workers had called for a block on the deal. (QZ)
+ AI firms are set to train military versions of their models on classified data. (MIT Technology Review)
3 The EU has told Google to open Android to AI rivals
It wants to end Gemini’s built-in advantage. (Ars Technica)
+ Google calls the move an “unwarranted intervention.” (WSJ $)
+ A final decision is expected by the end of July. (Reuters $)
4 OpenAI is reportedly developing an AI-first smartphone
It would replace apps with agents. (TechCrunch)
+ Qualcomm and MediaTek may be developing its processors. (Gizmodo)
5 A brain implant for depression is moving into human testing
The FDA has approved a human study of the device. (Wired $)
+ BCIs have thus far struggled to reach the market. (MIT Technology Review)
6 A populist backlash against AI is gaining momentum in rural America
From Indiana to Idaho, voters are pushing back against the technology. (NYT $)
+ Anti-AI protests are expanding worldwide. (MIT Technology Review)
7 DeepSeek has priced its new model 97% below OpenAI’s GPT-5.5
It aims to attract more enterprises, developers, and agent-based users. (SCMP)
+ Here are three reasons why DeepSeek V4 matters. (MIT Technology Review)
8 AI now generates a third of new websites
A study found it’s making the web more cheery and less verbose. (404 Media)
9 Top talent is leaving Big Tech to launch their own AI startups
Meta, Google, and OpenAI are facing a brain drain. (CNBC)
10 Taylor Swift is trademarking her voice and image
The Grammy winner has been the target of numerous deepfakes. (NBC News)
+ A growing number of celebrities are fighting AI with trademarks. (BBC)
Quote of the day
—Judge Yvonne Gonzalez Rogers reacts to prospective jurors confessing their negative views of Elon Musk ahead of his legal battle with Sam Altman, The Verge reports.
One More Thing
When Joe Rogan falsely declared that “party drugs” were an “important factor in AIDS,” several million people were listening. He also asserted that AZT, the earliest drug used to treat AIDS, killed people “quicker” than the disease itself—another claim that has been disproven.
Such comments illustrate an unmistakable resurgence in AIDS denialism: a false collection of theories arguing either that HIV does not cause AIDS or that there is no such thing as HIV at all. By the dawn of the millennium, these claims had largely fallen out of favour. That changed when the coronavirus arrived.
Follow the digital path from Covid skepticism to the return of a deadly conspiracy theory.
—Anna Merlan
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.)
+ Explore the planets from your laptop with this live sky map.
+ This marathon DJ set from Daphni is an incredible journey through electronic music.
+ NASA’s stunning Artemis II wallpapers bring a high-res piece of deep space to your phone.
+ This fascinating GPS explainer breaks down how your phone figures out exactly where you are.
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.
In February, I picked up a flyer at an anti-AI march in London. I can’t say for sure whether or not its writers meant to riff on South Park’s underpants gnomes. But if they did, they nailed it: “Step 1: Grow a digital super mind,” it read. “Step 2: ? Step 3: ?”
Produced by Pause AI, an international activist group that co-organized the protest, it ended with this plea to the reader: “Pause AI until we know what the hell Step 2 is.”
In the South Park episode “Gnomes,” which first aired in 1998, Kenny, Kyle, Cartman, and Stan discover a community of gnomes that sneak out at night to steal underpants from dressers. Why? The gnomes present their pitch deck. “Phase 1: Collect underpants. Phase 2: ? Phase 3: Profit.”
The gnomes’ business plan has since become one of the greats among internet memes, used to satirize everything from startup strategies to policy proposals. Memelord in chief Elon Musk once invoked it in a talk about how he planned to fund a mission to Mars. Right now, it captures the state of AI. Companies have built the tech (Step 1) and promised transformation (Step 3). How they get there is still a big question mark.
As far as Pause AI is concerned, Step 2 must involve some kind of regulation. But exactly what it will call for and who will enforce it are up for debate.
AI boosters, on the other hand, are convinced that Step 3 is salvation and tend to glaze over the middle bit. They see us racing toward sunny uplands on the back of an “economically transformative technology,” as OpenAI’s chief scientist, Jakub Pachocki, put it to me a few weeks ago. They know where they want to go—more or less: It’s hazy up there and still some way off. But everyone’s taking a different route. Will they all make it? Will anyone?
For every big claim about the future, there is a more sober assessment of how the rubber meets the road—one that quells the hype. Consider two recent studies. One, from Anthropic, predicted what types of jobs are going to be most affected by LLMs. (A takeaway: Managers, architects, and people in the media should prepare for change; groundskeepers, construction workers, and those in hospitality, not so much.) But their predictions are really just guesses, based on what kinds of tasks LLMs seem to be good at rather than how they really perform in the workplace.
Another study, put out in February by researchers at Mercor, an AI hiring startup, tested several AI agents powered by top-tier models from OpenAI, Anthropic, and Google DeepMind on 480 workplace tasks frequently carried out by human bankers, consultants, and lawyers. Every agent they tested failed to complete most of its duties.
Why is there such wide disagreement? There are a number of factors. For a start, it’s crucial to consider who is making the claims (and why). Anthropic has skin in the game. What’s more, most of the people telling us that something big is about to happen have reached that conclusion largely on the basis of how fast AI coding tools are getting. But not all tasks can be hacked with coding. Other studies have found that LLMs are bad at making strategic judgment calls, for example.
What’s more, when they’re deployed, the tools aren’t just dropped into a cleanroom. They need to work in places contaminated with people and existing workflows. And sometimes adding AI will make things worse. Sure, maybe those workflows need to be torn up and refashioned around the new technology for it to achieve transformative status, but that will take time (and guts).
That big hole? It’s right where Step 2 should be. The lack of agreement on exactly what’s about to happen—and how—creates an information vacuum that gets filled by the latest wild claim of the week, evidence be damned. We’re so unmoored from any real understanding of what’s coming and how it will be deployed that a single social media post can (and does) shake markets.
We need fewer guesses and more evidence. But that’s going to require transparency from the model makers, coordination between researchers and businesses, and new ways to evaluate this technology that tell us what really happens when it’s rolled out in the real world.
The tech industry (and with it the world’s economy) rests on the held-out promise that AI really will be transformative. But that is not yet a sure bet. Next time you hear bold claims about the future, remember that most businesses are still figuring out what to do with their underpants.
An oral medicine for hair loss successfully spurred hair growth in a late-stage trial, startup Veradermics announced Monday.
Veradermics assessed the pill in two ways: by how many hairs grew within a square centimeter of the scalp, on average, and by how satisfied participants were with the results. Over the course of six months, men who took the drug, known as VDPHL01, either once or twice daily had between 30 and 33 more hairs per square centimeter of scalp. Men in the placebo group grew approximately seven additional hairs.
Between 79% and 86% of men taking VDPHL01 said they saw improvement, along with between 72% and 84% of the clinical trial investigators — results that pleased Reid Waldman, a dermatologist turned Veradermics’ chief executive.
A long-acting injectable treatment for plaque psoriasis helped 63% of patients achieve complete skin clearance in a mid-stage clinical trial, its maker, Oruka Therapeutics, reported Monday.
Separately, Oruka said an updated analysis of blood exposure levels supported the potential for the drug, ORKA-001, to be injected just once per year.
Oruka still needs to conduct late-stage clinical trials, but Monday’s results bolster ORKA-001’s potential to become a new treatment for the autoimmune skin disease with remission rates equal to or greater than current commercial blockbusters Skyrizi and Bimzelx, while requiring fewer injections.
Nature Medicine, Published online: 24 April 2026; doi:10.1038/s41591-026-04354-1
In a biomarker-driven trial evaluating radiotherapy with erlotinib, everolimus or dasatinib in patients with newly diagnosed diffuse intrinsic pontine glioma, the primary endpoint of overall survival was not met, but features associated with long-term survival were defined, and everolimus emerged as a potential candidate for further testing.