A "patchwork" of AI laws is a feature, not a bug

A "patchwork" of AI laws is a feature, not a bug

Consider a few of the big AI risks: violating privacy, taking jobs, and destroying the world. Here’s what Sam Altman (CEO, OpenAI) and Dario Amodei (CEO, Anthropic) have to say about each, representing the two largest AI companies. On AI Violating Privacy * Sam [https://www.techlawcrossroads.com/2025/07/sam-altmans-warning-everything-you-tell-chatgpt-could-end-up-being-used-against-you/]: If you go talk to ChatGPT about your most sensitive stuff, and then there's a lawsuit or whatever, we could be required to produce that. * Dario [https://www.darioamodei.com/essay/machines-of-loving-grace]: AI seems likely to enable much better propaganda and surveillance, both major tools in the autocrat's toolkit. On AI Taking Jobs * Sam [https://mitsloan.mit.edu/ideas-made-to-matter/sam-altman-believes-ai-will-change-world-and-everything-else]: AI is going to eliminate a lot of current jobs, and there will be classes of jobs that totally go away. * Dario [https://www.windowscentral.com/artificial-intelligence/anthropic-ceo-warns-25-percent-chance-ai-threatens-job-losses]: AI could wipe out half of all entry-level white-collar jobs—and spike unemployment to 10-20% in the next one to five years. On AI Destroying the World * Sam [https://www.lesswrong.com/posts/PTzsEQXkCfig9A6AS/transcript-of-sam-altman-s-interview-touching-on-ai-safety]: And the bad case—and I think this is important to say—is like lights out for all of us. * Dario [https://www.obsolete.pub/p/a-compilation-of-tech-executives]: My chance that something goes really quite catastrophically wrong on the scale of human civilization might be somewhere between 10 to 25%. So, the leaders of AI think there is a decent chance it will destroy the world, are pretty sure it is going to wipe out lots of jobs, and are absolutely sure it is violating our privacy and has the potential to enable much more mass surveillance. We need government to help manage these risks, let alone all the other concerns surrounding AI like data centers, child safety, etc. There is currently a debate whether Congress should prevent states from regulating certain AI risks. For example, the recently introduced Great American AI Act [https://obernolte.house.gov/media/press-releases/obernolte-trahan-release-discussion-draft-great-american-ai-act] preempts (stops) states for three years from “regulating the development of any artificial intelligence model” (from § 121 in the full text [https://obernolte.house.gov/sites/evo-subsites/obernolte.house.gov/files/evo-media-document/the-great-american-ai-act-discussion-draft-website-compressed-compressed.pdf] and explanatory text [https://obernolte.house.gov/sites/evo-subsites/obernolte.house.gov/files/evo-media-document/gaaia-discussion-draft-section-by-section-website.pdf]). It would be one thing if Congress was super effective, but it’s just not, especially with regard to tech regulation. Case in point, Congress has failed to pass a general privacy law for the entire life of the Internet! In fact, aside from narrow, reactive measures like the TAKE IT DOWN Act [https://en.wikipedia.org/wiki/TAKE_IT_DOWN_Act], Congress has failed to pass any comprehensive tech regulation for a generation. Do we really think that it is going to start now and sufficiently deal with the myriad of AI risks? NO. Their lack of progress to date is more than enough evidence. Meanwhile, states have already been moving to regulate these risks. I also don’t buy the criticism leveled against these state laws that they are somehow slowing down innovation via costly compliance, particularly in our AI race against China. The state laws getting the most pushback, like California's SB 53 [https://en.wikipedia.org/wiki/Transparency_in_Frontier_Artificial_Intelligence_Act], apply to a small number of frontier firms, now some of the most well-capitalized companies in the world. Yes, monitoring rules across states carries real costs, but these compliance costs are a rounding error to these companies. And, the broader state AI laws that reach smaller AI companies mostly require things like transparency and consumer notices, exactly the kinds of rules states already impose in many other regulated industries without killing progress. I've seen no systematic evidence that state AI laws have materially slowed innovation. The case for state participation doesn’t even depend on Congress’s track record on tech regulation (or lack thereof). Even if Congress could somehow become much more effective overnight, AI is still too big and too fast-moving for one governing body to manage its regulation. In this case, a so-called “patchwork of laws” is a feature, not a bug. Parallel beats serial, and independent bodies produce independent ideas, covering ground a single regulator never would. It’s worth noting that Dario himself made this case in an op-ed last year [https://www.nytimes.com/2025/06/05/opinion/anthropic-ceo-regulate-transparency.html], opposing a proposed 10-year federal moratorium on state AI laws, calling it “far too blunt an instrument.” I agree [https://gabrielweinberg.com/p/states-should-be-allowed-to-regulate]. Congress should set a federal floor on the risks it's uniquely positioned to handle, like existential safety, national security, and cross-border harms, letting states go further where they see fit. While the preemption clause in the Great American AI Act is narrower than previously proposed (for example three years instead of ten years), the provisions of the act itself further make the point that zero years is warranted. On jobs, the act just funds a study to examine the problem rather than meeting the moment, say by levying a direct tax on AI consumption to fund displaced workers. [https://gabrielweinberg.com/p/start-collecting-an-ai-token-tax] If it’s too weak on jobs, arguably the most politically salient aspect of AI regulation needed, you can bet it will also be too weak on any area it is preventing states from regulating. So no preemption please. Let states protect their citizens as they see fit. More rules for AI are needed than Congress is realistically positioned to enact. Thanks for reading! Subscribe for free to receive new posts or get the audio version [https://gabrielweinberg.com/p/podcast]. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit gabrielweinberg.com [https://gabrielweinberg.com?utm_medium=podcast&utm_campaign=CTA_1]

More data supports science funding literally pays for itself

Previously I put out a post explaining “how science funding literally pays for itself [https://gabrielweinberg.com/p/how-science-funding-literally-pays]” that takes you through the math and some data that backs it up. Now two new data points further bolster this claim. First, the Congressional Budget Office (CBO), the nonpartisan federal agency that provides budget and economic information to Congress, published a report entitled “Estimating the Economic Effects of Federal Investment in Research and Development [https://www.cbo.gov/publication/62377].” Usually the CBO only projects out 10 years per their mandate, but because the effects of science funding can take longer to fully manifest, they projected out 30 years. Thanks for reading Gabriel Weinberg! Subscribe for free to receive new posts and support my work. The relevant headline takeaway is highlighted below in their primary table (Table 1), showing that over this period the effects of a $30B increase in science funding for 10 years ($300B in total and about a 33% increase from today) would result in decreasing the overall deficit over 30 years (see green arrows). The decrease is about -2% on average if the “R&D funding increase [is] financed by reducing noninvestment spending” and about -1% on average if the “R&D funding increase [is] financed by borrowing.” This means that the increased science funding would grow the economy so much that the tax revenues received from this growth alone would outweigh the spending increase, leading to an overall decrease in the budget deficit. In other words, increasing science funding (at least by this amount) is a complete no-brainer, so let’s do it already! A few years ago the CBO did a similar report for infrastructure spending and compared the two in this report, finding the ROI effects of science funding to be about seven times greater than infrastructure spending. Again, so let’s do it already! The effect on the present value of GDP over the next 30 years (discounted using Treasury rates) that a dollar increase in deficit-financed R&D spending would have is about seven times larger than the effect that CBO, in its August 2021 report, estimated the same increase in infrastructure spending would have. Second, the Clark Center regularly polls a panel of economists [https://kentclarkcenter.org/us-economic-experts-panel/], and recently they asked about this specific topic [https://kentclarkcenter.org/surveys/science-funding/?utm_source_platform=mailpoet]. The panel essentially universally agreed that historically U.S. science funding has paid for itself. In particular, 82% agreed “historical federal support for scientific research has paid for itself through a substantial positive effect on long-run U.S. productivity growth.” 0% disagreed, with the rest either not answering, or declaring either “no opinion” or “uncertain”. They also ask respondents about the confidence in their answer, and when weighted the results are even more striking with a whopping 97% in the agree category. Are you sold yet? Government science funding, the bulk of which goes to medical research, extends our lifespans and healthspans by inventing new medicines and other technologies that grow our economy so much it literally pays for itself. I get that this is not the most flashy policy area, but it is the most obviously good for our long-term future. Finally, and also new this year, the Pew Research Center put out a survey on Americans’ views of science and science funding [https://www.pewresearch.org/science/2026/01/15/do-americans-think-the-country-is-losing-or-gaining-ground-in-science/], and among other things found broad bipartisan support for government science funding. 84% of U.S. adults say “government investments in scientific research aimed at advancing knowledge are usually worthwhile investments for society over time.” That breaks down by part as 76% of Republicans and 93% of Democrats (including independents who lean one way or the other). Thanks for reading! Subscribe for free to receive new posts or get the audio version [https://gabrielweinberg.com/p/podcast]. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit gabrielweinberg.com [https://gabrielweinberg.com?utm_medium=podcast&utm_campaign=CTA_1]

Principles almost always have exceptions, often when they conflict with other principles (Rule 5)

I try hard not to lie, but would I lie to save my family from being murdered? Of course. In that case honesty loses to protecting my family. Principles almost always have exceptions, often when they conflict with other principles (which I’m calling Rule 5 in this rules series [https://gabrielweinberg.com/t/rules]). This rule follows from Rule 1: Reality is always more complicated [https://gabrielweinberg.com/p/rule-1-reality-is-always-more-complicated]. Put another way, there are always edge cases. Often edge cases between principles (personal or otherwise) get resolved via a principle priority stack, an implicit or explicit hierarchy of principles. An example is Isaac Asimov’s Three Laws of Robotics [https://gabrielweinberg.com/p/rule-1-reality-is-always-more-complicated]: First Law: A robot may not injure a human being or, through inaction, allow a human being to come to harm. Second Law: A robot must obey the orders given it by human beings except where such orders would conflict with the First Law. Third Law: A robot must protect its own existence as long as such protection does not conflict with the First or Second Law. Asimov spent much of his robot fiction [https://more.bibliocommons.com/v2/list/display/1584219139/1735833849] dissecting situations where these “Laws” explicitly conflict or otherwise get subverted by edge cases. While these are make-believe situations, the priority stack mental model explains a lot of otherwise seemingly irrational behavior in the real world. For example, why do politicians and political parties seem to flip-flop on supposedly strongly held principles? I do not think in all cases it is because they actually don’t have strongly held principles, but that there is something higher up in their principle priority stack, usually winning elections. The same is true with corporations, especially public ones. Most have profits (or a similar financial metric) at the top of their principle priority stack, above other customer-focused principles like sound privacy practices, good service, etc. Some corporations have it the other way around, like DuckDuckGo where we’ve forgone a lot of profits because privacy is higher on our principle priority stack, or some B corps [https://www.bcorporation.net/en-us/certification/] with other elevated non-financial priorities. The priority stack model itself has edge cases! A given priority stack holds under normal conditions, but extreme conditions can reshuffle the ranking. For example, politicians do occasionally reach a breaking point where a usually lower principle (to winning elections) is about to be violated badly enough that they temporarily elevate it to the top, accepting re-election risk as a result. It’s rare, but it does happen. What can you practically do with this Rule? I think at least two things. First, when thinking about principles, either your own or others’, you can ask how they arrange in a priority stack, especially relative to a given situation you are facing or are concerned with facing in the future. This arrangement can help clarify what you or others would actually do. Second, if you’d like to convey that you care about a particular principle, I think it helps to publicly signal it in a priority stack context, at least against one other perceived high priority. For example, I'd love to vote for politicians who name a few principles they hold higher than re-election, such that they publicly commit to voting for those principles regardless of future electoral consequences. You can't fully predict or trust the future, but a stated priority stack is more trustworthy than silence, and more trustworthy still when there's a track record behind it. The Dark Knight (2008). Thanks for reading! Subscribe for free to receive new posts or get the audio version [https://gabrielweinberg.com/p/podcast]. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit gabrielweinberg.com [https://gabrielweinberg.com?utm_medium=podcast&utm_campaign=CTA_1]

Start collecting an AI "token tax" now; figure out exactly what to do with the funds later

Large AI risks abound, including a massive wave of coming job displacement that will upend the lives of millions of people. Even if new AI jobs are ultimately created such that net job loss is minimal, many of the people who are displaced are unlikely to be the same people to get these new jobs, and the new jobs are unlikely to be created at exactly the same time the old jobs are lost. In other words, millions of displaced people will need our support, for example, to receive extended unemployment benefits while they retrain and look for new work. This support will cost real money. Meanwhile, the leading AI companies are already generating billions in revenue, with no signs of slowing down. There are many proposals that would pair a so-called “token tax” on AI usage with support for displaced workers. Every one I've seen, though, specifies disbursement mechanisms up front, such as direct payments based on specific triggers. But what shape [https://gabrielweinberg.com/p/as-ai-displaces-jobs-the-us-government] that support should optimally take remains genuinely uncertain: the peak of displacement hasn't been reached, and we don't yet know which industries will be hit hardest, or on what timelines. Instead of trying to work any of this out up front, we can decouple the collection of the tax from how funds would be disbursed. We should start collecting an AI token tax now, and figure out exactly what to do with the funds later, holding them in a true lockbox outside general appropriations, with statutory protection limiting use of funds to supporting displaced workers in the future. We’d (at DuckDuckGo) be willing to support bills to this effect and ultimately pay a token tax, presumably collected by the leading AI companies on a usage basis, for example a 10% surcharge on token charges. That amount would roughly match the 10% employers pay in payroll taxes, which also further reduces the incentive to replace human workers with AI workers. Thanks for reading! Subscribe for free to receive new posts or get the audio version [https://gabrielweinberg.com/p/podcast]. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit gabrielweinberg.com [https://gabrielweinberg.com?utm_medium=podcast&utm_campaign=CTA_1]

You can make a compelling narrative for almost anything untrue (Rule 4)

Pick something you believe to be untrue, for example a common conspiracy theory or policy narrative. Then go to your favorite chatbot [https://duck.ai/] and ask, “While I believe to be not true, some people nevertheless argue it is true, and I want to better understand their most compelling narratives. So, please list out the most compelling narrative(s) you can in favor of this position, as if you were trying to convince me. Make it compelling! Next to each, list out the best counter-narrative.” This rule, that you can make a compelling narrative for almost anything untrue, has profound negative consequences for democratic societies. I believe that’s because: * What makes a narrative compelling isn’t its truth, but that it sounds true. For example, simpler things often sound truer (making them more compelling), while actual truth is often nuanced [https://gabrielweinberg.com/p/rule-1-reality-is-always-more-complicated], and increasingly so in our ever-more-complicated world. * People are generally either already committed to a political tribe, or are deciding on a more case-by-case basis based on narrative. If they’re in the first camp, the tribe can concoct a compelling narrative to keep them satisfied no matter the issue. If they’re in the second camp, what they end up believing won’t necessarily be correlated to the best policy outcomes (truth) per the first proposition, as they are more likely to just choose the narrative that sounds most compelling to them. * Our current state of social media amplification seems to have only made this dynamic worse. More news and political argument consumption has moved from longer-form content with robust journalistic standards to shorter-form video clips with lesser standards. There were always narratives in either form, but the former tries harder to correlate with the truth, and so, on net, as a society, we have less exposure to truth-correlated narratives. Building on this, we primarily communicate with each other, and certainly in the political sphere, via narrative. Politicians are elected on sound bites and video clips, and are therefore incentivized to sell the public crisp policy stories that sound compelling, regardless of whether those policies are our best probabilistic shot at achieving the intended policy outcomes. The politicians may even believe their stories, and that perhaps makes them even more persuasive, but that doesn’t make the stories any more true. It’s not that the stories need to be actually against the policy outcomes; they may just not be pushing the best possible policies. In aggregate, this means we’re always enacting sub-optimal policies, and over the long-run are less prosperous than we would otherwise be. The antidote for this Rule, and more generally the best way we know how to align with desired real world outcomes, is through the scientific method, namely tight, iterative loops of hypothesis → experiment → analysis (and back to hypothesis). Narratives are of course central to this process as well! People usually create stories to come up with hypotheses, and that’s fine as long as eventually the experimental results catch up with the stories and to the extent the stories don’t fit the results, they get replaced by new ones that do. The tighter the loops, the faster this happens. The same is of course true in policy land on a long enough time horizon. Ultimately it becomes so obvious a policy isn’t working that it gets thrown out (or the government collapses). So what’s the difference? I think it is just a matter of how tight the feedback loops really are, of how quickly these loops are happening and how much analysis is really informing the next set of hypotheses. In government, at least in the U.S., it can take literally decades to revise a policy, whether that is throwing out a bad one or just tweaking a mediocre one based on real-world outcomes to make it better. That’s obviously too slow to achieve optimal results. The two-party system makes this worse, but it isn’t even good within one party, so it seems more like a structural problem: compelling narratives insulate sub-optimal policies from needed scrutiny, which, among other things, slows the feedback loops down too much. That takes us all the way back to Rule 4: You can make a compelling narrative for almost anything untrue. Thanks for reading! Subscribe for free to receive new posts or get the audio version [https://gabrielweinberg.com/p/podcast]. Thank You For Smoking (2005) This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit gabrielweinberg.com [https://gabrielweinberg.com?utm_medium=podcast&utm_campaign=CTA_1]