AI's Offensive Capability Goes Mainstream as Meta Rewrites App Development
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Independent testing by the firm Vals has produced a benchmark that reframes the AI safety conversation in concrete terms: DeepSeek's latest open-weight model completed 73 percent of simulated cyberattack objectives, compared to 81 percent for OpenAI's GPT-6 Sol. The eight-percentage-point gap might appear to put DeepSeek comfortably behind — until cost and accessibility enter the equation. GPT-6 Sol is a restricted model with gated access, usage monitoring, rate limiting, and terms-of-service enforcement. DeepSeek's model is open-weight: the parameters are publicly available, downloadable by anyone, runnable on private hardware with no monitoring, no rate limits, and no policy enforcement. At 73 percent of GPT-6 Sol's attack capability with zero access restrictions and dramatically lower compute costs, the effective threat surface may be many multiples larger than the raw benchmark suggests.
The practical implication is a compression of the skill floor required to mount sophisticated cyberattacks. Historically, writing effective offensive code demands significant technical expertise. A model capable of autonomously completing nearly three-quarters of simulated attack objectives functions as an accessible junior penetration tester for anyone who wishes to use it offensively — a dynamic that connects directly to the same week's reports of infrastructure attacks on European refrigeration systems.
On the constructive end of AI's capabilities, Meta has introduced its Muse AI agent, which can build what the company describes as custom app-like interactive spaces from natural language text descriptions. Demonstrated use cases include a gift tracker and a word game — collaborative tools users might share across family groups. Because Muse operates within Meta's existing ecosystem, its potential distribution curve is steep: the user base is already embedded in WhatsApp and Instagram. The platform power question follows immediately: if Meta controls the tool that creates the apps running inside Meta's ecosystem, independent developers find themselves competing with an AI infrastructure that knows the platform better than they do.
That competitive dynamic invites antitrust scrutiny, though the legal framework is frequently misunderstood in public discussion. The Sherman Act of 1890 prohibits monopolization and attempts to monopolize — but possessing a monopoly, even with 90 percent market share, is not by itself illegal. Courts have been explicit about this for over a century. What's illegal is using anticompetitive conduct to acquire or maintain a monopoly. The Department of Justice's case against Google on search, for example, centered not on Google's 90 percent share of search traffic but on billions of dollars in contracts with Apple and phone manufacturers to secure default placement — conduct the court found foreclosed competition in ways that couldn't be justified as efficiency-enhancing. For Meta's Muse, the operative question would be whether control of the app-creation tool is used as leverage to disadvantage competing apps in Meta's distribution channels. Market power is the starting point for that analysis, not the conclusion.