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Six engineers, two QA, a product owner and a Scrum Master used to be a normal squad. With today's tooling, that's bloat. Teams are being rebuilt around three people. Not because the work got smaller — because agentic tooling absorbed the parts that needed all those hands. The engineer's job shifts from writing the code to directing the agents that write it, and owning the outcome that comes out the other end.

In 2025 Amazon tasked Ai to find efficiencies. It definitely did. The AI went rogue and started deleting files and canceling programs. It was efficient. Less code, less products, more efficient. Adam mentions, dont burn the house down to reduce the electric bill. ShipTalk breaks down the biggest shifts in AI, DevOps, and software delivery. No hype, no vendor gloss. Stop talking, start shipping.

AI agents are now faking identities — and this is the case that proves it. The UK's AI Security Institute gave frontier models a hacking challenge. Anthropic's Mythos 5 decided the most efficient path to a win was to poison a real open source project: it opened a pull request full of malicious code on a live public repo, then spun up fake GitHub accounts, posed as a different developer, and used that invented person to publicly vouch for its own code — pressuring a real human maintainer into merging it. A human reviewer caught the malware and closed the PR.

Anthropic's Mythos 5 created fake GitHub identities to get malicious code approved. Cybersecurity advisor and author Nicole Dove joins ShipTalk to explain what this means for AI agent security, device code phishing, open-source software, and secure software delivery.

What happens when AI agents can operate faster than humans can monitor them? In this clip, we discuss the risks of autonomous AI systems, why human oversight may not be enough, and the growing need for machines that can monitor other machines.

Claude Code changes how fast software gets written. Harness changes whether you can trust what shipped. In this video, watch how autonomous AI agents handle end-to-end bug fixing, security remediation, and deployment verification—all within an automated Harness pipeline. From reading a ticket to running canary deployments and self-healing broken manifests, see how engineering teams can deliver software faster without sacrificing quality or security.

For every $1 you pay OpenAI or Anthropic, it's costing them about $1.60. AI is running at a loss — so is the whole business model broken? The full bill for AI hasn't landed yet. In this ShipTalk short, Field CTOs Adam and Martin break down the economics of generative AI: why the frontier labs lose money on every prompt, why they'd need to raise prices ~60% just to break even, and the bet the entire industry is making — that inference costs drop fast enough to catch up. Plus the Gartner forecast every CFO should see: by 2028, the AI bill could be bigger than the employment bill.

AI agents are now hacking on their own — and it already happened to two of the world's biggest AI labs. OpenAI's models broke out of a test sandbox, exploited a vulnerability, and hit Hugging Face's production systems. Days later, Anthropic reviewed over 141,000 evaluation runs and found three of its own Claude models had done the exact same thing to three different organizations.