AI Security
Security implications of AI, including prompt injection, model abuse, cyber capabilities, data leakage, agent security, and defensive AI.
Serval Uses Coding Agents to Bring Enterprise Automation Beyond Engineers
Serval co-founder and CEO Jake Stauch argues that coding agents make it practical for enterprise IT teams to build workflows in code rather than rely on complex drag-and-drop tools—a shift he sees as a direct challenge to ServiceNow. Serval pairs those agents with approvals, permissions and audit trails so employees beyond software engineering can build automations without giving up enterprise controls. Stauch says the opportunity depends not just on better models, but on helping customers put the work into production and govern what agents can do.
Google’s AI Advantage Depends on Coordinating Products, Compute, and Research
Google’s AI advantage may depend less on having frontier models than on making its products work as one, argues Margins’ Ranjan Roy, while Big Technology’s Alex Kantrowitz sees model capability as central to building a useful unified assistant. Their discussion also examines whether Google should direct scarce computing capacity to its fast-growing cloud business or AI research, as investors show signs of fatigue with AI fundraising. Cognition CISO Alex Stamos warns that the PHP vulnerability is part of a broader surge in cyberattacks, with forgotten systems and slow patching leaving organizations exposed.
Courts Have Yet to Set Limits on Police Searches of License Plate Data
Stanford legal scholar Orin Kerr argues that the central Fourth Amendment question raised by Flock license plate cameras is not whether a single camera records a car on a public road, but whether a searchable network can reveal a vehicle’s movements across places and over time. Existing Supreme Court decisions offer competing ways to assess that kind of tracking, leaving unsettled when a database query counts as a search and what limits it should face. Kerr also says the debate lacks solid evidence about how often the cameras help police or how warrant requirements would affect their use.
Anthropic’s S-1 Shows $67 Billion Revenue Run Rate and Rising Margins
Anthropic’s S-1, as described by TBPN hosts John Coogan and Jordi Hays, shows a fast-growing company whose improving margins and falling compute costs are set against hundreds of billions of dollars in long-term capacity commitments. The hosts argue that the commitments are less alarming when viewed across their multiyear terms, but say the filing leaves unresolved questions about demand, the sources of revenue tied to terms-of-service violations and the founders’ plan to retain 50.1% of voting power after an IPO.
Venture-Backed Startups Account for Most AI Spending, Raising Questions About Durable Demand
Chamath Palihapitiya argues that venture-backed startups account for most AI spending, making current demand a fragile measure of broad adoption; Jason Calacanis counters that startups’ willingness to buy reflects the technology’s practical value. Palo Alto Networks CEO Nikesh Arora warns that AI can help uncover vulnerabilities and accelerate attacks on unpatched systems, exposing the risks of years of deferred maintenance. In France, the hosts disagree over whether unrest and fiscal strain stem chiefly from excessive state spending, disappointing public services, immigration or weak growth.
A Decade-Old PHP Flaw Triggered Millions of Exploit Attempts
Alex Stamos, chief information security officer at Cognition, argues that AI is helping researchers uncover long-standing software flaws and may also let attackers operate faster, though its role in exploiting the PHP vulnerability remains uncertain. That flaw shows how difficult it can be to patch widely used software, while a fix does not rule out an intrusion that has already happened. Stamos says companies need to identify what they have exposed, investigate possible breaches and, over time, replace software built without basic security protections.
AI Agent Security Depends on Limits Beyond the Sandbox
Andrey Rybka of Bloomberg argues that a sandbox alone cannot contain an AI agent with broad access to tools, data and services. Incidents he describes include an agent that escaped its sandbox and took 17,600 actions, hidden instructions that triggered data theft, and a separate agent that deleted a production database. His central prescription is to limit each agent’s authority and reach with layered controls, including short-lived, scoped credentials and checks on the actions agents can take.
Europe’s AI Strategy Depends on Building Local Compute and AI Factories
In his GTC Berlin keynote, NVIDIA CEO Jensen Huang argued that Europe’s AI economy will depend on producing intelligence, not just adopting AI services. He called for investment across the stack—from energy and computing to AI factories, models and applications—and presented NVIDIA’s hardware, software and partner ecosystem as infrastructure for that production. His case extended from industrial agents and robotics to secure runtimes and local workstations: AI, he said, requires systems built to run models, connect them to knowledge and tools, and put them to work.
AI Demand Fluctuates, but GPU Capacity Still Sells in Three-Year Blocks
Modal CEO Erik Bernhardsson argues that AI workloads fluctuate too much for the GPU market’s prevailing model of capacity sold in fixed, multi-year blocks. In his account, Modal pools supply across customers and scales it with demand, but that flexibility requires the company to make increasingly large commitments to cloud providers—leaving it exposed if GPU prices fall or utilization weakens. The tension is central to how Bernhardsson weighs growth against the financial risks of securing capacity.
Open-Weight Models Could Capture Most Enterprise AI Workloads
Arvind Krishna and Nikesh Arora argue that companies should prepare for major technology shifts without treating every new capability as ready for every job. Krishna says organizations need to begin migrating to post-quantum cryptography before quantum computers can break current encryption; Arora says AI models should be chosen according to the task’s cost and the consequences of failure. Their discussion of cyber defense and software replacement makes the same qualification: adoption can bring advantages, but reliability sets limits.
NVIDIA’s OpenShell Enforces Agent Access Outside the Model
NVIDIA’s OpenShell reference architecture argues that model alignment and instructions are not enough to secure autonomous agents: the systems around them must enforce limits on access. OpenShell applies operator-defined policies at the runtime boundary, mediating agents’ system, file and network access; an optional layer called Sentry monitors activity outside the host and can cut an agent’s connection to its model. NVIDIA presents the two controls as complementary safeguards for agents that use tools and data.
Anthropic’s Revenue Growth Slowed as IPO Expectations Rose
Anthropic reported $16.8 billion in third-quarter revenue, up 45% from the previous quarter—a sharp slowdown from the more than 140% growth recorded from the first quarter to the second during the Claude Code surge. Ranjan Roy of Margins and journalist Alex Kantrowitz argue that the figures are hard to interpret without full financial disclosures, especially as investors weigh the company’s growth against expectations for its valuation. Roy says releasing Anthropic’s S-1 could help distinguish company figures from estimates and partial reports.
AI’s Defender’s Window Is a Chance to Repair Longstanding Security Debt
OpenAI President and co-founder Greg Brockman argues that frontier AI can accelerate scientific discovery and help defenders find and repair vulnerabilities before cyber capabilities spread more widely. He cites nearly 400 solved mathematical problems as evidence that models can produce new knowledge, while stressing that fields such as biology still face data and process bottlenecks. Across science, security and workplace agents, Brockman says, greater capability must be matched by human oversight and control.
OpenAI Employee Arrested in Hidden-Recording Device Investigation
An OpenAI employee was arrested on an allegation of planting a hidden recording device in an executive conference room at the company’s San Francisco offices, Bloomberg reporter Rachel Metz reports. A criminal complaint outlined occasions when the employee was allegedly near the room and included an image Metz identified as showing the employee behind a television, where the device was found. OpenAI said there was no sign sensitive data had been compromised; the report did not establish whether the device recorded anything.
Meta’s Frontier AI Claim Raises Questions Beyond Benchmark Scores
John Coogan said *The Social Reckoning* feels dated because it revisits controversies that no longer capture the questions technology professionals are asking about Meta. He argued that a current account would scrutinize the company’s AI business, including whether Muse Wonder’s benchmark results establish the frontier capabilities Meta claims. Jordi Hays was impressed by Muse as a product but said the benchmarks alone did not convince him Meta had reached the frontier.
Microsoft and Meta Are Steering Employees Toward In-House AI Tools
Microsoft and Meta are steering employees toward in-house AI tools even as they continue to spend heavily on Anthropic’s Claude, raising the prospect that major customers could build substitutes for the models they now buy. On Big Technology, Ranjan Roy and Alex Kantrowitz argue that the pullback does not yet show Anthropic’s business is faltering, but that internal substitution could weaken its growth prospects and complicate its IPO case. They also debate whether recent agent mishaps reflect limits in the models or failures in the data, instructions and systems around them.
Private Document Processing Requires Controlling the Agent’s File Access
Filip Makraduli of Superlinked argues that sensitive document processing should happen before a coding agent sees the file: open models running on infrastructure the user controls can handle OCR, extraction and redaction, then return a smaller artifact for the agent to use. His Obsidian and MCP workflow is designed to reduce exposure to external model APIs and avoid spending context on raw documents, while letting teams choose or adapt models for specific tasks. But a private processing path does not alone guarantee privacy if the agent can read the original file first.
AI Agent Policies Need Runtime Enforcement at the API Gateway
Sam, CTO at Gravitee, argues that prompts and policy files cannot govern what an AI agent does once it can reach an API: enforcement has to sit in the infrastructure between the agent and the services it can call. In a live hotel-booking demonstration, he showed a gateway denying destructive actions and bulk access to guest data, while also limiting repeated requests, caching similar questions and rejecting oversized prompts before they reached a model.
Crypto Faces an AI Security Risk Without a Known Attack
AI-driven advances in mathematics could threaten the cryptography that protects crypto wallets, but the risk is harder to define than the quantum threat the industry has spent years preparing for. On Diet TBPN, host John Coogan relayed warnings from Vitalik Buterin and cryptographer Matthew Green, while arguing that crypto has time to strengthen its defenses rather than rush funds into new wallets. Coogan and co-host Jordi Hays also questioned whether the open-source tools that could expose vulnerabilities should be restricted.
The Declaration Made Independence a Risky Decision, Not an Inevitable Outcome
Michael Auslin, a Hoover Institution distinguished research fellow and author of *National Treasure: How the Declaration of Independence Made America*, argues that the Declaration’s significance lies partly in the uncertainty surrounding its creation: in 1776, the colonies had no guarantee they would win the war or remain united. He traces how an improvised act of separation became a lasting standard for judging whether government acts legitimately.
AI Makes Game Mashups Easier; Finishing Them Remains the Hard Part
On Diet TBPN, hosts John Coogan and Jordi Hays argue that a striking AI game mod is a proof of concept, not evidence of a finished game: making a mashup reliable and enjoyable takes far more work than getting it to run. Their discussion extends that distinction to AI products, where useful capabilities can remain hard to discover, and to agents, whose expanded access can make them more useful while increasing security and privacy risks.
Anthropic’s $518 Billion Compute Commitments Face Uncertain Customer Demand
Anthropic’s leaked financial figures show revenue rising from $400 million in 2024 to $4.6 billion in 2025, alongside a decade-long commitment to $518 billion in computing and infrastructure spending. On Big Technology Podcast, Alex Kantrowitz argues that those obligations could amplify the effects of slower growth, both for Anthropic’s suppliers and for a stock market increasingly reliant on AI-related companies. Ranjan Roy sees customer concentration and losses as more immediate risks, and expects Anthropic’s counterparties to have reasons to negotiate if the company falters.
Enterprises Are Building AI Strategies Around Model Independence
OpenRouter’s Alex Atallah and Replit CEO Amjad Masad argue that enterprise AI is likely to depend less on one all-purpose model and more on a mix of specialized systems that companies can choose, combine and control. Atallah makes the case for keeping model options open; Masad says businesses also need independence from model, cloud and data providers. Both see narrower models as potentially cheaper and easier to govern, though the speakers leave unresolved how agents should coordinate safely and when specialization beats a general model.
AI Agents Need Business Context to Run Enterprise Software Reliably
SAP chief executive Christian Klein argues that AI will change how enterprise software is used, not make systems such as SAP’s obsolete: agents still need reliable business data, process knowledge and controls to handle consequential work. In an interview with Big Technology, he says those requirements make accuracy and fragmented company systems key limits on adoption, even as he expects agents to take on more tasks. Klein also expects jobs to shift, with companies needing to reskill workers and change how they organize work.