Published 10 June 2026 · Updated 17 September 2026
The Anthropic, OpenAI and the race space is moving fast, and this latest development proves it. I’m getting bored of AI hype, but the Anthropic silent play is worth taking seriously. For months, the AI industry has been chasing market cap and IPO valuation while quietly giving cyber criminals the same tools they sell us as protection. That double-dealing is now out in the open, and everybody who handles customer data should pay attention.
Here’s what changed in the past week. Anthropic confidentially filed a draft registration statement with the SEC on 1 June 2026, the same day it expanded Project Glasswing to more than 150 organisations across 15 countries. The pitch is noble: secure critical software before AI-enabled hackers cause widespread damage. The reality is more complicated. Anthropic is giving a limited number of partners access to Mythos, its cybersecurity-focused preview model, to find zero-days before broader deployment. That puts participating vendors inside the inner ring of a very exclusive pre-IPO sales cycle.
The timing is not an accident. Anthropic closed a $65 billion Series H round with a post-money valuation near $965 billion, annualised revenue running at roughly $47 billion, and an IPO target in October 2026. Security isn’t the side effect, it is the revenue story. Claude Security and the Compliance API plug Anthropic into CrowdStrike, Palo Alto Networks, Okta and Zscaler. If your company already uses one of those platforms, you are likely already being presented with Anthropic’s pitch.
On the other side, OpenAI is dealing with internal chaos that would be farcical if the stakes were not so high. The latest reporting puts the CFO at odds with the CEO over a $600 billion capital envelope for the next five years. Add a Ronan Farrow investigation that paints Sam Altman as a ruthless operator, leadership departures, and a media acquisition clearly aimed at controlling the narrative, and you have a leadership team distracted during the most important product cycle it will ever run. That internal volatility matters to customers evaluating long-term contracts for AI systems.
What this means for your business, right now, is straightforward. AI is faster at finding code flaws than legacy scanning tools, but speed does not equal safety. If your development team uses AI coding assistants, you need code review processes that assume vulnerabilities are being introduced faster than they can be caught. The Mythos pipeline is proof that AI can now find high-severity issues at scale. That capability is not reserved for builders with good intentions.
Also worth worrying about: the consumer side is equally exposed. AI chatbot results are already leading shoppers to scam sites. Privacy complaints are spiking in multiple jurisdictions, and the issue is not metadata alone. Targeted synthetic content, including deepfake videos, has progressed enough to trick executives and investors, not just teenagers. UK regulators are calling policy dystopian. European consumers are considering legal action over data handling. This is not a future problem.
The practical advice from this round is simple. First, verify which AI tools in your stack are logging or training on your data. Second, require that any cybersecurity claim made by an AI vendor include third-party audit results, not marketing slides. Third, think carefully about the lock-in cost of betting your architecture on a single provider.
We are at the end of the era where AI failures were somebody else’s problem. The same models that stand to reshape productivity can also reshape breach outcomes for the worse.
> Wall Street is about to price AI as a security product. The question is whether the security actually improves before the lock letter is signed.
Related Reading
- The AI Safety Net is Full of Holes: What 2026 Taught Us So Far
- AI Dethroned: Stolen Passwords, Hackers, Verizon
- Mythos, APRA, and ASIC: Are Australian Enterprises Ready?
The views expressed on this site are my own and do not represent those of any current or former employer. Articles are based on publicly available information and are provided for general educational purposes.
How this analysis is researched, sourced and corrected: Editorial standards and corrections. Spotted an error? Contact Philip Hall.
