Artificial Judgment
People think they know where AI's going enough to regulate it. Do they?
As Regulation Ban Looms, California Issues Frontier AI Study
California continues to research AI regulation, confident that it can strike the optimal balance between risk management and innovation.
‘“Well-crafted policies can simultaneously fulfill this obligation to consumers, allow states to carefully tailor policies to the specific needs of their constituents, and maintain critical pathways for federal action that provide a comparable degree of protection to consumers,” the authors of California's report wrote. “In pursuing this balance between innovation and safety, California has a unique opportunity to productively shape the AI policy conversation and provide a blueprint for well-balanced policies beyond its borders.”’
A 10-year moratorium on AI regulation is madness
Bad things are illegal, e.g., fraud. AI can be used for many things, including bad things. Therefore, we must strangle AI in the crib. Even though we have plenty of regulations seeking to control (or limit) the amount of bad activity we see.
Okay, then. That’s one argument.
Let’s see the other side. Because regulation isn’t costless. Stifling innovation for the most intriguing technology to arrive in our lifetimes even as our principal strategic competitor has no such qualms seems expensive to me.
‘One provision of the “big, beautiful bill” that the Senate must reject is its 10-year moratorium prohibiting state-law regulation of artificial intelligence. Although the Commerce Committee amended the provision to condition federal broadband funding on compliance with the moratorium, the effect is the same disservice to the American people.
‘Members of Congress would likely have to search a long time find voters in their states who favor giving Big Tech a decade to “see where AI leads,” while it would take no time to find voters who care about child pornography, online scams and threats to their economic interests — all of which may be caused or accelerated by AI. ‘
Bipartisan Senators Oppose 10-Year Ban on State AI Regulation
There’s a strong undercurrent of skepticism of Big Tech here. It’s been building for years. Lina Khan was in the vanguard of the previous Administration’s efforts. But the fight continues.
‘Senators Marsha Blackburn, R-Tenn., and Maria Cantwell, D-Wash., joined state Attorneys General Jonathan Skrmetti, R-Tenn., and Nick Brown, D-Wash., at a press briefing Wednesday to denounce the proposed 10-year moratorium.’
A New Paradigm for Fueling AI for the Public Good
Data is the currency of the 21st century. Or at least it might be one day.
‘A tragic irony shapes our current data infrastructure. Most of us share mountains of data with massive and profitable private parties—smartwatch companies, diet apps, game developers, and social media companies. Yet, AI labs, academic researchers, and public interest organizations best positioned to leverage our data for the common good are often those facing the most formidable barriers to acquiring the necessary quantity, quality, and diversity of data. Unlike OpenAI, they are not going to use bots to scrape the internet for data. Unlike Google and Meta, they cannot rely on their own social media platforms and search engines to act as perpetual data generators. And, unlike Anthropic, they lack the funds to license data from media outlets. So, while commercial entities amass vast datasets, frequently as a byproduct of consumer services and proprietary data acquisition strategies, mission-driven AI initiatives dedicated to public problems find themselves in a state of chronic data scarcity. This is not merely a hurdle—it is a systemic bottleneck choking off innovation where society needs it most, delaying or even preventing the development of AI tools that could significantly improve lives.’
The Invisible Price Tag of Yesterday’s Regulation
Strangely, regulations put in place one hundred years ago didn’t anticipate today’s technology.
‘Early 20th-century regulation was built for a capital-intensive, centralized grid designed for universal service and exploiting economies of scale and scope.
‘Three outdated regulatory assumptions now hinder innovation:
Embedded Asset Bias: Utilities profit from tangible infrastructure rather than digital solutions. A transformer can enter the rate base easily; software enabling EV charging flexibility struggles for recognition.
Rate-Case Latency: Multi-year rate-case cycles clash with rapidly evolving technology, such as weekly software updates orchestrating virtual power plants.
Static Customer Classes: Traditional residential, commercial, and industrial categories fail to accommodate modern organizational realities—hyperscale data centers running microgrids or widespread adoption of EVs and solar+storage.’