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 a $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.

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Europe’s enterprise AI ecosystem is becoming impossible to ignore

For the past several years, the global AI race has largely been defined by foundation models, chatbot launches, and the battle for consumer attention. But beneath that public competition, another ecosystem has been gaining momentum — one centered on enterprise infrastructure, operational systems, and industrial AI.

While Silicon Valley continues pushing aggressively into large language models and consumer-facing AI products, many European companies are focused on applying AI to complex systems already embedded into everyday life: Manufacturing. Logistics. Healthcare. Cybersecurity. Energy infrastructure.

These industries are quickly becoming some of the most important battlegrounds in the AI economy. They also require far more than powerful models alone. That’s where Europe believes it may have an advantage.

Deploying AI inside large organizations introduces a different set of challenges altogether: governance, compliance, security, operational reliability, and long-term integration. In many ways, the industry is now confronting the realities of moving AI from experimentation to production at scale.

That shift will loom large at VivaTech 2026, which has increasingly become a showcase for Europe’s growing enterprise AI ambitions.

The AI industry’s next challenge

For many enterprises, the first wave of AI adoption was relatively experimental. Companies rushed to test copilots, automate workflows, and explore generative AI use cases across their organizations. But as the technology matures, the conversation is becoming significantly more complicated.

Now comes the hard part: Enterprises are confronting questions around governance, compliance, infrastructure, and security that many companies barely considered during the first wave of AI experimentation.

Increasingly, startups are being judged less on novelty and more on whether they can integrate into existing enterprise environments, navigate regulatory complexity, and deliver measurable operational value. Investors are starting to prioritize infrastructure, deployment, and measurable outcomes over pure experimentation.

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At VivaTech 2026, those realities are expected to shape many of the conversations happening across the event floor.

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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 long-standing 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.