BLACK PINE INSIGHTS

← Back to technology

The Regulatory Collapse

5 deep-dive 10 full 15 headlines

The Regulatory Collapse

The tech industry is entering a period of radical uncertainty where institutional guardrails are being dismantled faster than they can be rebuilt. Over the past 48 hours, we’ve seen the Supreme Court likely green-light Trump’s firing of an FTC commissioner, the Pentagon announce a military AI platform powered by Google, and the EU launch another antitrust investigation into Google’s AI practices. These aren’t separate events. They form a pattern: the rules of the game are being rewritten simultaneously from multiple directions, and nobody knows what the final shape will be.

The deeper signal is about power consolidation masquerading as deregulation. When you can fire independent regulators with impunity, when the military outsources AI to private vendors without competition, and when antitrust enforcement becomes geographically fragmented, you don’t get a freer market. You get a market where winners are determined by proximity to power rather than competitive merit. For founders and VCs, this creates both opportunity and existential risk depending on which side of the new power structures you land.

Deep Dive

The FTC’s Independence Collapses Under Presidential Pressure

The Supreme Court’s apparent willingness to allow Trump to fire FTC Commissioner Rebecca Kelly Slaughter signals the effective end of independent regulatory agencies in any meaningful sense. Conservative justices seem prepared to side with arguments that the president needs unfettered control over agencies enforcing federal law, even those specifically structured to require bipartisan leadership and protection from executive removal. If the Court rules as expected, it won’t just affect the FTC. It opens the door to gutting the independence of the SEC, CFPB, and any other multi-member agency designed to operate beyond a single administration’s reach.

For the tech industry, this is simultaneously liberating and destabilizing. Founders celebrated FTC antitrust enforcement against Meta, Amazon, and others, viewing it as necessary market discipline. But FTC independence also protected startups from capricious regulatory action tied to whoever happened to occupy the White House. Once that independence erodes, regulatory risk becomes a pure political calculation. A regulator sympathetic to your competitor now has a clearer path to action. The agency’s mandate becomes whatever the executive decides it should be on any given Tuesday.

What happens next depends on how aggressively the administration uses its newfound power. If they simply let Slaughter go and move on, the damage is contained to precedent. If they use the firing as a cudgel to signal that the FTC will now pursue cases aligned with administration priorities rather than competition law, the entire startup funding model shifts. VCs already price in regulatory risk; they’ll now price in political risk as their primary variable.

Google’s AI Training Becoming the Central Antitrust Battleground

The EU’s decision to open a formal antitrust investigation into Google’s use of web and YouTube content for AI training represents a fundamental challenge to how the largest tech companies view data collection and training. Brussels is specifically alleging that Google is using unpaid content from publishers and creators while simultaneously blocking competitors from accessing the same material. It’s not just about unfair extraction of value, though that matters. It’s about whether dominant platforms can use their scale to create moats that smaller AI competitors cannot overcome.

Google’s defense is telling: they’re pointing to technical controls like Google-Extended tokens and robots.txt compliance, essentially arguing they’ve given publishers the choice to opt out. But the EU’s investigation suggests this is insufficient. The real complaint seems to be that even when publishers technically can refuse, the leverage asymmetry makes refusal costly. If your content is removed from Google’s crawl, your search visibility tanks. The ability to choose is illusory when the alternative is economically devastating.

For the broader AI ecosystem, this investigation sets a precedent that will ripple across every company building models on web data. The implication is that simple technical consent mechanisms may not satisfy future regulatory requirements. This matters enormously because it changes the cost structure of training large AI models. If every piece of training data requires explicit compensation or licensing, the economics of model development shift dramatically. Smaller companies that can’t negotiate with millions of content creators get squeezed out. This isn’t just regulatory hassle; it’s structural. It could accelerate consolidation toward well-capitalized incumbents who can handle the legal and commercial complexity.

The Pentagon’s Google Gambit Signals Military AI Will Be Captured

The Department of Defense’s announcement of GenAI.mil, with Google’s Gemini as the first available model, is being spun as innovation. Defense Secretary Hegseth’s rhetoric about “putting the world’s most powerful frontier AI models directly into the hands of every American warrior” sounds democratic. But it’s actually a textbook example of how government contracting creates de facto monopoly control through institutional lock-in rather than explicit policy.

Google gets to embed its AI infrastructure directly into military operations. Pentagon officials already noted that other models will be available on the platform in the future, but the first-mover advantage is decisive. Every integration, every workflow optimization, every bureaucratic process built around Gemini becomes sunk cost. By the time the Pentagon gets around to evaluating competing models, the switching costs are prohibitive. Google has effectively captured the military AI market through the appearance of openness while creating conditions that make competition structurally difficult.

This matters for the commercial AI market because it demonstrates how government power can be used to advantage incumbent players while maintaining the rhetorical cover of competition and innovation. Smaller AI companies looking to sell to government now face a model where the most important buyer has already selected its primary vendor through a process that appeared neutral. The antitrust implications are significant: if Google can lock in government contracts through technical integration, the precedent extends to other verticals. Healthcare, finance, education. The winner in each sector becomes whoever manages to embed first with the most important institutional buyer.

Signal Shots

Jensen Huang’s Anti-Regulation Pitch to Trump — Nvidia’s CEO told President Trump in a November Oval Office meeting that fragmented state-level AI regulations threaten US competitiveness and could cause the country to lose the AI race against China. The argument is strategically sophisticated: frame regulation as a national security issue rather than consumer protection, and make the case that centralized federal control is preferable to the current patchwork. This sets up a high-stakes battle in 2025 between VCs and tech founders (who largely support light-touch federal preemption) and consumer advocates pushing for stronger state-level guardrails. The winner determines whether AI deployment accelerates or slows.

Nvidia’s Location Verification Tech Quietly Demonstrated to US Officials — According to Reuters reporting, Nvidia has privately demonstrated unreleased technology that can show which country its chips are operating in, apparently to address government concerns about illicit semiconductor trafficking and usage restrictions. The tech remains unreleased but the fact that it exists and has been shown to officials suggests government pressure is real and Nvidia is solving for regulatory compliance ahead of formal mandate. This is defensive innovation: building compliance infrastructure now to avoid more draconian restrictions later. Expect other chipmakers to follow, shifting supply chain complexity toward government verification layers.

Linux Foundation Creates Agentic AI Foundation for Vendor-Neutral Standards — Big Tech (Anthropic, Google, OpenAI, others) is forming the Agentic AI Foundation under Linux Foundation stewardship to standardize how AI agents interact with tools and each other. This is legitimate standards work, but it’s also a play to establish vendor-friendly norms before regulators can impose their own. If the AAIF’s standards become the de facto global baseline, it’s much harder for regulators to mandate competing approaches. This is the tech industry’s playbook: get ahead of regulation by writing your own rules and making them seem inevitable.

Slack’s Denise Dresser Joins OpenAI as Chief Revenue Officer — The Slack CEO and former enterprise tech executive is moving to OpenAI to lead enterprise revenue strategy and customer success. This is a significant hire because it signals OpenAI’s intent to compete directly with Google and Microsoft for enterprise AI spending. Dresser’s experience navigating large customer relationships and complex procurement processes gives OpenAI institutional knowledge it’s lacked. For the market: expect OpenAI to become more aggressive in enterprise sales in 2025, putting direct pressure on Microsoft and Google’s cloud divisions.

China Adds Domestic AI Chips to Government Approved Supplier List — Before Trump signaled flexibility on Nvidia exports, China added Huawei and Cambricon processors to its official government approved supplier list for the first time, encouraging domestic procurement. This suggests Chinese planners expected export restrictions to continue and moved to reduce dependency. Trump’s subsequent signal allowing some Nvidia exports to China may actually undermine these domestic players by keeping them on the bench. The geopolitical implication is murky: does Nvidia’s access reduce China’s incentive to build better domestic chips, or does it accelerate Chinese development by letting them benchmark against cutting-edge hardware?

Fal Raises \(140M at \)4.5B Valuation Led by Sequoia — The AI infrastructure startup, focused on serverless computing for generative AI, tripled its valuation with a fresh funding round that included secondary sales for early investors. Fal is raising in an environment where infrastructure plays are becoming the safest bet for VCs: they don’t compete directly with foundation model companies, they solve real operational problems, and they benefit from all competing platforms. Expect more capital flowing toward infrastructure, middleware, and tools in the next 18 months as foundation model margins compress.

Scanning the Wire

  • Coupang CEO resigns over South Korea’s largest data breach — The e-commerce giant lost personal data on 30+ million people, and the CEO took personal accountability rather than hide behind corporate structures. This sets an embarrassing precedent for other tech leaders facing similar incidents, making it harder for executives to survive breaches without significant consequence.

  • Australia bans teens under 16 from social media despite widespread skepticism — The law passed but nobody realistically believes it will work; VPN usage spiked during the rollout. The real signal is that regulatory willingness to ban platforms (or age cohorts) is now live in democracies, not just authoritarian countries. Tech companies can no longer assume democratic governments won’t simply exclude them from markets.

  • Intel exploring semiconductor manufacturing joint venture with Tata in India — Intel is moving away from pure manufacturing and looking for outsourced packaging partners outside traditional semiconductor hotbeds. This is defensive: building geopolitical supply chain diversity before US-China tensions force a choice.

  • Court throws out Trump wind development ban as “arbitrary and capricious” — A federal court rejected Trump’s executive order blocking wind development on grounds that “because Trump said so” is not legal justification. This establishes that even a friendly judiciary has limits on executive overreach. Tech regulation implications are significant: you can’t just ban technologies or companies by fiat, you need regulatory reasoning. It’s a speed bump on deregulation theater.

  • Australia porn traffic drops after age verification implementation, VPN usage peaks — Age checks reduced porn traffic to a “lower level” but 900K VPN users are still accessing it. Policy effectiveness is real but incomplete; people find workarounds. The regulatory lesson: you can meaningfully reduce usage, but not eliminate it. Privacy and compliance infrastructure becomes the actual market.

  • Study finds Instacart uses AI to show different prices to different users for the same item — Approximately 200 shoppers across four cities saw algorithmic price discrimination in real time. Instacart claims it’s running tests, but this is exactly the type of “surveillance pricing” that regulators are scrutinizing. Expect formal action if the practice is systematic rather than experimental.

  • App tracking ICE raids sues US over Apple removal — ICEBlock, which alerts users to ICE enforcement activity, claims government pressure led Apple to remove it. This is an early test case for whether platforms will resist government censorship demands or comply. The outcome shapes whether citizen surveillance resistance tools can exist on mainstream platforms.

  • Facial recognition tested at Orlando International Airport for departure gates — The TSA is rolling out facial recognition at departure points, expanding biometric surveillance from entry to exit. This normalizes continuous identity verification in airports and creates infrastructure that could spread to other venues. Once the technology is standard, opt-out becomes nearly impossible.

Outlier

Instacart’s AI pricing suggests a dystopian consumer future is already here — The study showing Instacart algorithmically prices identical items differently to different users in real time isn’t a bug or a test. It’s the future these systems were designed to enable. Once you have behavioral data, pricing optimization engines, and enough scale, price discrimination isn’t just possible, it’s economically mandatory. Your willingness to pay becomes the price you pay. This doesn’t require malice; it requires only that an algorithm is more efficient at extracting consumer surplus than fixed pricing. The dystopian part is that we’re discovering this is already happening at scale, casually, in grocery apps. Once this practice spreads to healthcare, housing, and transportation, the consumer economy becomes fragmented into algorithmic price classes based on data the companies have but you don’t.

See you tomorrow. The regulatory ground is shifting faster than most observers can track. Watch for Supreme Court decisions on agency independence and antitrust standing, both arriving imminently. They’ll reshape what’s legally possible for tech companies in 2025.