Meta Admits to Stealing Android Users' Private Search Data, and Business Bonus: Tools Without Rules--AI Compliance Is a Mirage Without Enforcement
Plus Tuesday's Tech News
Essential read from The Washington Post:
“Meta found a new way to violate your privacy. Here’s what you can do.”
You knew it was going to happen….it shouldn’t be a surprise. Check out this new article from The Washington Post, which reports that Meta has been knowingly collecting information from Android users' browsing activity. This information includes which websites users visited, what they viewed, what they clicked on, and what they purchased.
This was accomplished by Meta’s use of an open backdoor in the Android security architecture. A user’s browsing activity was then routed to the user’s Facebook and Instagram profiles and incorporated into files Meta maintains for those users. Those profiles are then used to sell goods and products back to the Android users.
Meta has confessed to the activity and stated that they are no longer using this particular data collection method. Google is investigating the matter further.
No laws were broken, but Google’s privacy policies were violated. It’s simply business as usual for Meta—collecting and monetizing individuals’ private information to further its bottom line. Another example of why Big Tech’s self-regulation doesn’t work.
Tools Without Rules — AI Compliance Is a Mirage Without Enforcement
Every enterprise software pitch these days seems to promise the same thing: AI compliance in a box. Whether it’s a flashy dashboard for bias audits, an automated fairness tool, or a governance module that claims to offer “regulatory-grade explainability,” the message is consistent—plug this in and your AI is safe, ethical, and lawful. But scratch the surface and you’ll find something else entirely. Most of these tools amount to little more than compliance theater—designed to check boxes and dazzle auditors, not to constrain power or protect the public.
This isn’t a tech failure. It’s a governance failure. AI compliance isn’t falling short because we lack dashboards. It’s falling short because we lack rules—and more critically, any incentive to enforce them. The infrastructure of oversight simply doesn’t exist, and in its place we’re getting a lot of sliders, scorecards, and smoke.
The Toolification of Governance
Software vendors are very good at listening to what corporate compliance officers and regulators want to hear. That’s why every AI product—regardless of what it actually does—now comes bundled with some feature labeled “responsible” or “trustworthy.” Ask for explainability? You’ll get a confidence score tucked into a tooltip. Ask for fairness? Here’s a dashboard that compares outcomes for different demographic groups, conveniently updated on a quarterly basis. Ask for accountability? The model card includes an “Ethics Lead,” whose name you’ll find buried in the appendix.
These aren’t guardrails. They’re marketing features dressed as governance. They exist to demonstrate intent, not to enforce outcomes. And in many cases, these tools are so customizable, so abstract, and so detached from real-world decisions that they give the illusion of control while allowing the underlying system to continue unchecked.
This is the toolification of governance: the belief that wrapping AI systems in a glossy layer of UX sliders and audit dashboards is enough to earn a regulatory gold star. It’s governance by interface—not by law.
Risk by Design
Let’s be blunt: if a company can override its own risk thresholds at will, or hide key aspects of its model behavior behind trade secret protections, then no amount of tooling will safeguard the public. The issue isn’t a lack of tooling—it’s that the tools are designed, owned, and operated by the very entities they’re meant to constrain.
It’s like asking a hedge fund to write its own insider trading rules—and then enforce them with a spreadsheet it built itself.
In other words: the foxes are building the henhouse monitoring systems.
What makes it worse is that many of these tools are actively marketed to external stakeholders—regulators, investors, customers—as evidence that everything is working fine. That’s like a chemical plant claiming it’s safe because it installed a fancy sensor, even though no one outside the company is allowed to inspect it. Without independent audits, mandatory disclosures, and regulatory teeth, these compliance tools are cosmetic. They’re designed for reassurance, not restraint.
The Enforcement Gap
Regulators today aren’t just outgunned—they’re out-infrastructured. Most government agencies don’t have the technical capability to independently verify what an AI system is doing under the hood, much less how it evolves over time. That’s why so many rules end up focusing on documentation, paperwork, and after-the-fact impact reports, rather than preventing harm in the first place.
Even the EU AI Act, often cited as the most comprehensive framework on the books, leans heavily on self-assessments, post-market monitoring, and internal risk classifications. It’s a start—but it still assumes that companies will be honest about systems they have every incentive to obscure.
In the United States, the FTC and DOJ have made bold public statements about enforcing algorithmic accountability. But without real structural tools—like independent sandbox environments, code escrow requirements, or regulator-run model evaluations—they’re effectively showing up to a Formula 1 race with a bus pass. The speed, complexity, and opacity of modern AI far outpace the current architecture of enforcement.
What Real AI Oversight Would Look Like
Let’s imagine what meaningful AI oversight could actually look like. Real compliance tools would:
Be open to third-party audit and not locked behind proprietary code;
Log model changes over time, including retraining events and dataset shifts;
Include red-teaming protocols led by independent experts, not internal PR teams;
Trigger automated alerts when model outputs shift in statistically significant ways;
Provide regulator access to raw outputs in high-risk areas like lending, employment, and criminal justice.
That kind of oversight infrastructure won’t be built accidentally. It requires deliberate action from lawmakers and regulators. It means moving beyond checklists and UI mockups toward systemic change. It means treating AI oversight like a public good—not a sales pitch.
Until Then, It’s Just a Show
In today’s environment, AI governance tooling is mostly theater: a well-lit stage with all the props of accountability and none of the script. Everyone plays their part—the AI ethics officer, the product compliance lead, the regulatory affairs liaison—but the story never changes.
Unless and until governments build real power to inspect, audit, and restrain these systems, the only thing our compliance tools will successfully regulate is our illusions. We’ll keep pretending the dashboard means control. We’ll keep mistaking confidence scores for truth. And in the meantime, the real risks—bias, opacity, manipulation, and abuse—will continue unimpeded.
Because tools without rules are just window dressing.
Meta goes nuclear to power AI with clean electrons
Summary: Meta has signed a 20-year power-purchase agreement with Constellation Energy to keep the Clinton Clean Energy Center—a 1.1 GW nuclear plant in Illinois—running until 2047, helping renew its license and supplying clean electricity for AI/data centers. As data-hungry AI creates massive energy demand, this is one of the most concrete big‐tech/nuclear partnerships yet.
Tamara’s Take: Big Tech just asked nukes to foot its electric bill—because apparently “let there be light” didn’t come with enough juice for its AI empire.
The future of AI will be governed by protocols no one has agreed on yet
As AI evolves from human-controlled tools to autonomous agents, companies like Google and Anthropic are racing to codify inter‑agent communication, data‑use, and reasoning protocols. But there's zero consensus—and legal suits like OpenAI’s copyright battle prove it's urgent
Tamara's Take: The Wild West of AI, but with suits drafting the cheat sheet at their leisure.
Peter Kyle asked Google to “sense check” UK AI policy
UK Technology Secretary Peter Kyle enlisted DeepMind’s Demis Hassabis to review government AI initiatives. Documents show over 160 meetings between Labour and big tech—drawing ire from creatives like McCartney, who worry this is one big public-private echo chamber.
Tamara's Take: When your advisor doubles as your campaign donor—transparency is not included.
Sam Altman says AI chats should be as private as "#talking to a lawyer"—but a ruling could force ChatGPT to keep them forever
A NYT lawsuit demands OpenAI store all users' deleted ChatGPT conversations indefinitely. Altman compares chats to confidential doctor–lawyer sessions and says forcing retention would break trust.
Tamara's Take: Your AI therapist keeps no secrets... unless it's subpoenaed. Oops.
States rebuff federal ban on AI laws
In defiance of a proposed 10‑year federal ban, Texas passed its own AI/data‑privacy law, requiring transparency for AI use, biometric consent, and state‑level oversight via a new AI council.
Tamara's Take: Texas takeover: “Federally-forbidden? Y’all missed a spot.”
A ban on state AI laws could smash Big Tech’s legal guardrails
The House OK’d a Trump-era budget rider blocking state AI regulation for a decade. Critics—including Ro Khanna and civil rights groups—warn it would wipe out safeguards on facial recognition, algorithmic fairness, child protection, and more.
Tamara's Take: One law to rule them all—right into Big Tech’s lap.