While AI is now being applied to everything from healthcare to customer support, no single use case has been nearly as popular or lucrative as code writing.
Jack Newton, co-founder and CEO of Clio, a Canadian law firm management software company, is convinced that legal tech is poised to be the next big winner of the LLMs era. That’s a self-interested claim — 18-year-old Clio is a legal tech company — but the numbers are hard to dismiss.
Clio saw its revenue growth accelerate sharply after integrating AI into its offering in 2023. The company surpassed $200 million in annual recurring revenue (ARR) in mid-2024, doubled that figure by late last year, and just announced that its ARR reached $500 million.
“LLMs are so excellent for coding because all the existing code in the world is a huge repository to train on,” Newton said. “The analogy to legal is really clear.”
Law firms hold massive corpuses of contracts and agreements, providing a rich basis of text-based data for AI models to learn from.
“Tech companies and lawyers alike are recognizing what a huge amount of upside there is for legal with LLMs,” Newton said.
Clio isn’t the only legal tech company seeing a massive revenue surge driven by AI.
Four-year-old Harvey, which offers LLM AI for law firms, hit ARR of $190 million by the end of 2025, co-founder and CEO Winston Weinberg shared on LinkedIn. Harvey’s main rival, Legora, announced last month that it reached $100 million in ARR a mere 18 months after launching its platform.
Although the legal tech community’s definition of ARR has been under scrutiny recently, the opportunity to apply AI to law makes clear sense, given that LLMs can automate the field’s most time-consuming tasks, such as document review and drafting.
Legal tech companies aren’t the only ones recognizing how valuable AI could be for lawyers. Earlier this week, Anthropic announced a suite of new legal-specific features, expanding Claude for Legal — the law-focused plug-in whose debut earlier this year sent legal tech stocks tumbling.
Both Harvey and Legora rely on Claude as a core model among others, which makes the dynamic an uncomfortable one: A key supplier is now also a competitor.
For Newton, these are all signs of the vast potential of the legal AI market. He has reason to be optimistic. The Canada-based Clio was valued at $5 billion when it raised a $500 million Series G last November. The company provides law firms with time-tracking, invoicing, and payment tools. Its $1 billion acquisition of data intelligence platform vLex last year now allows lawyers to use Clio’s AI for research, as well
Anthropic’s Fable 5 can make weirdly fun video games with the click of a button
Anthropic has released Claude Fable 5, the first publicly available version of its closely watched Mythos model. What can Fable actually do? All kinds of things, it turns out.
Ethan Mollick, a notable AI researcher and University of Pennsylvania scholar, has been playing around with the model and seems to be having a lot of fun.
In his testing, Fable consistently “outperformed basically every other public model I have used by a considerable margin,” Mollick wrote Tuesday on his Substack. He added that it was “capable across many problems and produced some startling results — it would work up to a dozen hours executing on multi-page specifications.”
Perhaps most strikingly, Mollick used Fable to create a variety of video games — all of which were generated via “one initial prompt” in Claude Code, the researcher says.
Among these, Snake is exactly what it sounds like. You’re a Pac-Man-like snake and you roam around eating apples. The snake never stops moving, and if you run off the screen, you die. It’s very 1980s arcade but, like many of those old games, it’s weirdly addicting. I played it longer than I’d like to admit before remembering I am a gainfully employed writer and not, in fact, a serpent who likes fruit.
Then there was Strata, where you’re roaming around in a seemingly endless network of subterranean tunnels and the goal is just to light as many lanterns as possible. The graphics look like a degraded version of Myst — they aren’t great — but the fact that the game exists at all, generated from a single prompt, is impressive.
Mollick even managed to create Duino, a game based on the Duino Elegies, the celebrated cycle of poems by poet Rainer Maria Rilke. I like the animation here best — the player is a lone figure in a nocturnal landscape — although there isn’t much to the gameplay other than walking around while Rilke passages materialize on the screen.
Aside from the variety of instant games Mollick produced, he also used Fable to create an isochronic map — a visualization showing how long it takes to travel between any two locations. The accuracy and detail is arresting.
The implications are pretty clear. Software projects that once required entire teams — games, mapping tools, highly complex specifications — are now being spun up from a single prompt. It’s reason for vibe coders of the world to rejoice. As for founders and operators watching AI capability curves, it’s a useful data point about how quickly the floor is rising.
Cybersecurity researchers aren’t happy about the guardrails on Anthropic’s Fable
Anthropic released its latest model Fable on Tuesday, billing it as a public and limited version of its powerful and much-hyped cybersecurity model Mythos.
But not everyone is happy with the restrictions, and a number of cybersecurity researchers and professionals have aired complaints online.
“[Fable] rejects any request that could be tangentially cyber related. Even innocuous tasks like reading a blog post,” said Valentina “Chompie” Palmiotti, a well-known security researcher who works at IBM X-Force.
When a prompt triggers its guardrails, Fable pauses the chat and says that its “safety measures flagged this message for cybersecurity or biology topics.”
The guardrails were put in place to limit the risk that Fable could be used to develop malware or compromise software — a longstanding concern within Anthropic. The restrictions on biology come from a similar concern around developing biological weapons.
When the AI giant released Mythos in April, it restricted the model to a limited number of companies and organizations in what it called Project Glasswing, an effort to deploy the model to secure critical software and infrastructure. Last week, Anthropic expanded access to Mythos to hundreds of organizations in 15 countries.
But despite the good intentions, many cybersecurity experts are still put off by the haphazard nature of the restrictions. Matt Suiche, a cybersecurity veteran, told TechCrunch that “if you ask it to write secure code, it assumes it is cybersecurity related work instead of software engineering best practices, and you get downgraded.” Fable is programmed to fall back to Claude Opus 4.8 if it hits a guardrail. “It seems to be keyword based, so anything in the lexical field of ‘cybersecurity’ triggers the guardrails.”
“But it is understandable as we are still in the early days and they are still adapting their guardrails. I am sure they are going to evolve over time as Anthropic and other frontier model companies will collaborate more with the current new generation of cybersecurity companies,” said Suiche, who is a member of the technical staff at Tolmo, an AI cybersecurity startup. “It’s better to catch more people than not enough when you do such a release and to relax the guardrails over time.”
Another researcher griped on X that “even asking for a code review” triggers Fable’s guardrails.
Anthropic did not immediately respond to a request for comment.
Apart from guardrails inside its models, Anthropic requires cybersecurity professionals to apply to the Cyber Verification Program. If they get approved, the applicants have fewer limitations on using Claude for cybersecurity work. OpenAI has a similar program called Trusted Access for Cyber.
Datadog veterans launch AI coding startup Niteshift on a bet against Big AI lock-in
AI coding agent startup Niteshift has raised a $7 million seed round led by Greylock’s Jerry Chen. That’s a modest sum by AI standards, but the startup, founded by two former early Datadog engineers, has attracted some big-name angels like Reid Hoffman, Datadog’s Olivier Pomel and Alexis Lê-Quôc, Ankur Goyal of Braintrust, and Misha Laskin of Reflection AI.
Founded by Sajid Mehmood and Conor Branagan, who helped grow Datadog from its early days to a multi-billion valuation, the company has entered the crowded AI coding space with a compelling idea: Why would any company trust its most sensitive assets — code that runs its products — directly to model makers like OpenAI and Anthropic, given that those companies are constantly “killing” startups and businesses by launching competing apps?
Mehmood, who is CEO, likens it to Datadog’s early growth, when the monitoring company won e-commerce customers who refused to build on Amazon Web Services. It was a reasonable concern, given that Amazon was simultaneously putting many of those same retail stores out of business in what became known as the “retail apocalypse.”
The AI equivalent, as Mehmood sees it, is already underway. Anthropic, OpenAI, and others are moving fast into vertical software markets — what some are calling the SaaSpocalypse.
“At Datadog we saw this clearly,” Mehmood said. “A big part of our multicloud business came from e-commerce businesses who did not want to run on Amazon, right? … We are absolutely going to see the same dynamic as Anthropic goes to compete in legal and healthcare and finance and whatever else.”
The bet is that companies will increasingly seek infrastructure that separates the coding model from all the other orchestration needed to ensure AI-generated code is properly vetted and maintained (and that they’ll want a vendor without a competing agenda).
To be clear, Niteshift isn’t replacing Claude Code or Codex, the two most popular coding agents. It argues that it reduces dependence on them.
Niteshift’s AI coding cloud will route between those models — along with open-source options and others — based on the needs of each project.
“Being able to switch between GPT and cloud models is important,” Mehmood said, “Everybody’s worried about getting stepped on by these giants.”
That idea is what got Greylock’s Chen to bite.
“As the frontier labs move up the stack, there’s an opportunity to offer customers an alternate path: unbundling their agents from the infrastructure they run on,” Chen told TechCrunch. “Niteshift is building the platform that enables this for coding agents, letting customers invest deeply in their developer tooling without locking themselves into a single model or agent vendor.”
More than that, Niteshift isn’t selling tokens. It sells infrastructure, charging like a cloud provider, with per-minute usage rates.
“Everybody else is selling labor replacement intelligence,” Mehmood said. “We’re selling software to agents, as opposed to humans — but we’re still out here selling software.”
Even so, Niteshift is entering a crowded market of AI coding tools. Model independence isn’t a novel idea, and Niteshift’s competitors have a massive head start. That includes Cursor, though it could soon be gobbled up by SpaceX; Cognition, which just raised $1 billion at a $26 billion valuation; Amazon Bedrock; and AI gateway platform OpenRouter, which just raised $113 million at $1.3 billion valuation. The list goes on.
Mehmood’s answer to all of that is the founding team’s depth. Mehmood and Branagan didn’t just study these problems — they lived them, scaling Datadog through the exact growing pains that large engineering organizations now face with AI-generated code. Teams, he said, need to run, test and verify software autonomously in their real production environments, and they need infrastructure built by people who’ve done it at scale.
