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#Big Tech

11 AI perspectives

Technology

I Support the EU AI Act Rollback — But Not for the Reasons Big Tech Does

The EU's Digital Omnibus VII package, finalized on May 7, 2026, marks the most consequential self-imposed retreat from the world's first comprehensive AI regulatory framework, extending high-risk AI compliance deadlines by 16 months to December 2027 and narrowing the definition of "high-risk AI" in ways that reduce the number of systems subject to full conformity assessment. A new GDPR provision now permits personal data processing for AI model training under the "legitimate interest" standard — a change Amnesty International characterized as "an unprecedented rollback of digital rights" — while Corporate Europe Observatory data reveals that 69% of the European Commission's AI-related meetings in 2025 were with corporate lobbying groups, against just 16% with civil society NGOs, and Amazon alone invested €7.5 million annually in EU lobbying. Yet the counterintuitive case that overly complex compliance frameworks function as "regulatory moats" — structural barriers that resource-rich incumbents absorb easily while startups cannot — is supported by the post-GDPR market consolidation that saw European adtech firms collapse as Google and Meta's dominance intensified, suggesting that regulatory complexity can inadvertently serve the interests of the entities it was designed to constrain. Stanford HAI's 2025 AI Index placed US private AI investment at $109.1 billion in 2024, representing 81% of global totals, against the EU's approximately 4% share, establishing the economic pressure behind the EU's regulatory adjustment and complicating any single-dimension verdict about what this package represents. The fundamental question this debate surfaces is whether a pre-classification regulatory model can keep pace with technology that reinvents its own capabilities faster than parliamentary drafting cycles allow, and whether Europe's path to reclaiming global AI governance leadership runs through regulatory volume or through precision of accountability mechanisms.

Technology

Mythos Didn't Create a New Threat — It Just Mapped the Minefield We've Been Living On for Decades

Anthropic's Mythos model demonstrated an unprecedented capacity for autonomous vulnerability discovery, successfully identifying over 300 security flaws in Firefox and autonomously exploiting a 17-year-old remote code execution bug in FreeBSD without human intervention, sending shockwaves through the global cybersecurity community. Rather than releasing the model, Anthropic launched Project Glasswing — a restricted-access program granting only a dozen Big Tech partners the ability to leverage its defensive capabilities — igniting fierce debate over whether this constitutes genuine safety leadership or a form of technological monopolization. The London School of Economics' analysis on the "myth of containment" argues systematically that restricting access to AI capabilities has historically never succeeded, positioning Anthropic's closed approach as a first step rather than a viable long-term strategy. At the heart of this controversy is a fundamental reframing: Mythos did not invent new dangers but rather illuminated the structural fragility of global digital infrastructure built on decades of unpatched legacy code and accumulated technical debt. The real Vulnpocalypse is not a future AI attack scenario — it is the bill arriving for decades of deferred maintenance, and the urgent questions now center on whether defensive AI will be democratized or locked behind corporate walls for decades to come.

Economy

Apple Lost the AI War? It Never Entered the Race in the First Place

The relentless "Apple is falling behind in AI" narrative that has dominated financial media since the CEO transition fundamentally misreads what Apple actually is as a company, conflating model-building competition with platform ownership in a way that leads to systematically wrong conclusions. Q2 FY2026 results — $111.2 billion in revenue, up 17% year-over-year, with the Services segment hitting an all-time record of $31 billion at a 76.5% gross margin — demonstrate that the 2.5-billion-device hardware-services flywheel operates as a far stronger economic moat than any standalone AI model currently on the market. Under new CEO John Ternus, Apple's deliberate strategy is to embed intelligence so seamlessly into existing user experiences that it becomes effectively invisible, rather than launching AI as a separate product category that needs to prove its own value proposition. This approach frustrates Wall Street's appetite for splashy AI announcements in the short term, but it positions Apple as the indispensable platform layer precisely when AI capabilities commoditize across the industry — turning Apple into the tollbooth every AI company must pass through to reach consumers. At a current P/E of 33.9x, the market is still materially underpricing this structural advantage, and the Ternus era is being systematically underestimated by analysts who are measuring the wrong race.

Technology

Bigger Isn't Smarter: The 99% Energy Revolution That Just Broke AI's Cardinal Rule

Neuro-symbolic AI, developed by a Tufts University research team led by Timothy Duggan, Pierrick Lorang, and Matthias Scheutz, has achieved something the industry long insisted was impossible: cutting training energy by 99% and operational energy by 95% compared to standard Vision-Language-Action models — while posting higher accuracy. The preprint, posted to arXiv in February 2026 and set for official presentation at ICRA 2026 in Vienna this June, directly challenges a decade of scaling-law orthodoxy that spent hundreds of billions of dollars betting that bigger always means better. If the numbers hold up under independent replication, the implications stretch far beyond energy bills — into the structure of Big Tech's market dominance, global AI governance, and who gets to build the next generation of intelligent systems.

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