The proposal could work even if countries were to only buy stocks of publicly traded companies in highly efficient secondary markets (and exclude IPOs and secondary public offerings), so that we do not affect the stock price or how much capital the company has at hand and thus doesn’t speed up AI progress.
Microsoft, Google, Amazon, Nvidia have quite a bit of exposure to Anthropic, DeepMind, OpenAI, and xAI.
it is relatively easy to build something ten times better if you can solve the cold start and ‘easier to just swipe’ issues to get enough scale, but that no one has a good idea how to do that part.
I had an idea on how to solve this: create a dating app that helps you promote your profile with social media targeted to your prefered demographic.
Is OpenAI gaming user numbers?
Some have questioned OpenAI’s recent weekly user numbers:[1]
Feb ’23: 100M[2]
Sep ’24: 200M[3] of which 11.5M paid, Enterprise: 1M[4]
Feb ’25: 400M[5] of which 15M paid, 15.5M[6] / Enterprise: 2M
One can see:
Surprisingly, increasingly faster user growth
While OpenAI converted 11.5M out of the first 200M users, they only got 3.5M users out of the most recent 200M to pay for ChatGPT
Where did that growth come from? It’s not from apps: the ChatGPT iOS app only has ~353M downloads total[7] and Apple’s Siri integration only launched in December.[8] Users come from developing countries.[9] For instance, India is now OpenAI’s second largest market, by number of users, which have tripled in the past year.[10]
Many complain about increasingly aggressive message rate limits for free ChatGPT accounts, notionally due to high compute costs. But maybe this is a feature and not a bug: especially in poor countries, people create multiple accounts to get around the message and image generation limits.[11],[12] OpenAI incentivizes this: they no longer ask for phone numbers during sign up.
Many new users might also use ChatGPT via WhatsApp[13] (a collaboration with Meta) perhaps using flip phones. OpenAI no longer asks for an email address during sign up.[14]
You can also use ChatGPT search without signing up at all now.[15]
What counts as a user? True, ChatGPT grew faster than the fastest growing company ever, but social media has a much stronger network effect ‘lock in’ consumers longterm, whereas users will presumably switch AI chatbots much faster if a cheaper product becomes available. Many use ChatGPT merely as a writing assistant.[16] While consumer markets for social media can be winner-take-all, enterprise customers, while having doubled recently, will be less loyal and will switch if competitors offer a cheaper product.[17]
So maybe there’s some very liberal counting of user numbers going on. Valuation goes up. Meanwhile hundreds of OpenAI’s current and ex-employees are cashing out.[18]
Also, competition has caught up and so, Microsoft, which owns half of OpenAI, wants others to invest.[19] Yet, OpenAI CFO just said $11B in revenue is ‘definitely in the realm of possibility’ in 2025 (they’re at ~$4B year-on-year currently) to get $40B from Softbank investment at a ~$300B valuation.[20] More recently this dropped to $30B and they scrambling to find others to co-invest in Stargate.
This is the standard playbook- recent examples include Roblox, which also inflated user numbers,[21] and Coinbase, which used to be lax with their KYC for obvious reasons and had inflated user numbers (it’s also literally a plot point in Succession).
Also cf:
The market expects AI software to create trillions of dollars of value by 2027 AI stocks could crash [1] The Generative AI Con
[2] ChatGPT sets record for fastest-growing user base—analyst note | Reuters
[3] OpenAI says ChatGPT’s weekly users have grown to 200 million | Reuters
[4] OpenAI hits more than 1 million paid business users | Reuters
[5] OpenAI tops 400 million users despite DeepSeek’s emergence
[6] https://archive.ph/wff26
[7] ChatGPT’s mobile users are 85% male, report says | TechCrunch
[8] Apple launches its ChatGPT integration with Siri.
[9] https://trends.google.com/trends/explore?date=today 5-y&q=chatgpt&hl=en
[10] India now OpenAI’s second largest market, Altman says | Reuters
[11] Anyone else have multiple accounts so they don’t have to wait to use gpt 4o and also so each one can have a separate memory : r/ChatGPT
[12] https://incogniton.com/blog/how-to-bypass-chatgpt-limitations
[13] ChatGPT is now available on WhatsApp, calls: How to access—Times of India
[14] OpenAI tests phone number-only ChatGPT signups | TechCrunch
[15] ChatGPT drops its sign-in requirement for search | The Verge
[16] [2502.09747] The Widespread Adoption of Large Language Model-Assisted Writing Across Society
[17] Satya Nadella – Microsoft’s AGI Plan & Quantum Breakthrough.
[18] Hundreds of OpenAI’s current and ex-employees are about to get a huge payday by cashing out up to $10 million each in a private stock sale | Fortune
[19] Microsoft Outsources OpenAI’s Ambitions to SoftBank
[20] OpenAI CFO talks possibility of going public, says Musk bid isn’t a distraction
[21] Roblox: Inflated Key Metrics For Wall Street And A Pedophile Hellscape For Kids – Hindenburg Research
This lag effect might amplify a lot more when big budget movies about SBF/FTX come out.
Yes, good catch, this is based on research from the World Value Survey—I’ve added a citation.
I checked. It’s 0.67.
This seems to come from European countries.
Yeah I actually do cite that piece in the appendix ‘GDP as a proxy for welfare’ where I list more literature like this. So yeah, it’s not a perfect measure but it’s the one we have and ‘all models are wrong but some are useful’ and GDP is quite a powerful predictor of all kinds of outcomes:
In a 2016 paper, Jones and Klenow used measures of consumption, leisure, inequality, and mortality, to create a consumption-equivalent welfare measure that allows comparisons across time for a given country, as well as across countries.[6]
This measure of human welfare suggests that the true level of welfare of some countries differs markedly from the level that might be suggested by their GDP per capita. For example, France’s GDP per capita is around 60% of US GDP per capita.[7] However, France has lower inequality, lower mortality, and more leisure time than the US. Thus, on the Jones and Klenow measure of welfare, France’s welfare per person is 92% of US welfare per person.[8]
Although GDP per capita is distinct from this expanded welfare metric, the correlation between GDP per capita and this expanded welfare metric is very strong at 0.96, though there is substantial variation across countries, and welfare is more dispersed (standard deviation of 1.51 in logs) than is income (standard deviation of 1.27 in logs).[9]
GDP per capita is also very strongly correlated with the Human Development Index, another expanded welfare metric.[10] If measures such as these are accurate, this shows that income per head explains most of the observed cross-national variation in welfare. It is a distinct question whether economic growth explains most of the observed variation across individuals in welfare. It is, however, clear that it explains a substantial fraction of the variation across individuals.
You can compute where energy is cheap, then send the results (e.g. weights, inference) on where ever needed.
But Amazon just bought rented half a nuclear power plant (1GW) near Pennsylvania, so maybe it doesn’t make sense now.
Gemini 1.5 Pro summary
This document explores recent developments in the AI landscape, focusing on language models and their potential impact on society. It delves into various aspects like capabilities, ethical considerations, and regulatory challenges.
Key Highlights:
Advancements in Language Models:
Claude 3 by Anthropic now utilizes tools, including other models, showcasing increased capability and potential risks like jailbreaking and influencing other AI systems.
Gemini 1.5 by Google is available to everyone with promises of future integrations, prompting discussions on its system prompt limitations and the need for more user control over responses.
GPT-4-Turbo receives substantial upgrades, especially in coding and reasoning, but concerns about transparency and potential performance variations remain.
OpenAI’s potential development of GPT-5 sparks debates on the reasons for its delay, emphasizing the importance of rigorous safety testing before release.
Ethical and Societal Concerns:
The increasing persuasiveness of language models raises questions about manipulation and misinformation.
The use of copyrighted material in training data raises legal and ethical concerns, with potential solutions like mandatory licensing regimes being explored.
The rise of AI-generated deepfakes poses challenges to information authenticity and necessitates solutions like watermarking and detection software.
Job application processes might be disrupted by AI, leading to potential solutions like applicant review systems and matching algorithms.
The impact of AI on social media usage remains complex, with contrasting views on whether AI digests will decrease or increase time spent on these platforms.
Regulatory Landscape:
Experts propose regulations for AI systems that cannot be safely tested, emphasizing the need for proactive measures to mitigate potential risks.
Transparency in AI development, including timelines and safety protocols, is crucial for informed policy decisions.
The introduction of the AI Copyright Disclosure Act aims to address copyright infringement concerns and ensure transparency in data usage.
Canada’s investment in AI infrastructure and safety initiatives highlights the growing focus on responsible AI development and competitiveness.
Additional Points:
The document explores the concept of “AI succession” and the ethical implications of potentially superintelligent AI replacing humans.
It emphasizes the importance of accurate and nuanced communication in discussions about AI, avoiding mischaracterizations and harmful rhetoric.
The author encourages active participation in shaping AI policy and emphasizes the need for diverse perspectives, including those of AI skeptics.
Overall, the document provides a comprehensive overview of the current AI landscape, highlighting both the exciting advancements and the critical challenges that lie ahead. It emphasizes the need for responsible development, ethical considerations, and proactive regulatory measures to ensure a safe and beneficial future with AI.
Claude Opus AI summary:
The attached document is an AI-related newsletter or blog post by the author Zvi, covering a wide range of topics related to recent developments and discussions in the field of artificial intelligence. The post is divided into several sections, each focusing on a specific aspect of AI.
The main topics covered in the document include:
Recent updates and improvements to AI models like Claude, GPT-4, and Gemini, as well as the introduction of new models like TimeGPT.
The potential utility and limitations of language models in various domains, such as mental health care, decision-making, and content creation.
The increasing capabilities of AI models in persuasive writing and the implications of these advancements.
The release of the Gemini system prompt and its potential impact on AI development and usage.
The growing concern about deepfakes and the “botpocalypse,” as well as potential solutions to combat these issues.
The ongoing debate surrounding copyright and AI, with a focus on the use of copyrighted material for training AI models.
The ability of AI models to engage in algorithmic collusion when faced with existing oligopolies or auction scenarios.
The introduction of new AI-related legislation, such as the AI Copyright Disclosure Act, and the need for informed policymaking in the AI domain.
The importance of safety testing for advanced AI systems and the potential risks associated with developing AI that cannot be adequately tested for safety.
The ongoing debate between AI alignment researchers and AI accelerationists, and the potential for accelerationists to change their stance as AI capabilities advance.
A challenge issued by Victor Taelin to develop an AI prompt capable of solving a specific problem, which was successfully completed within a day, demonstrating the rapid progress and potential of AI.
The controversial views of Richard Sutton on the inevitability of AI succession and the potential for human extinction, as well as the debate surrounding his statements.
The growing public concern about AI posing an existential risk to humanity and the need for informed discussion and action on this topic.
Throughout the document, the author provides commentary, analysis, and personal opinions on the various topics discussed, offering insights into the current state of AI development and its potential future implications. The post also includes various tweets, quotes, and references to other sources to support the points being made and to provide additional context to the discussion.
cf
“The Bootleggers and Baptists effect describes cases where an industry (e.g. bootleggers) agrees with prosocial actors like regulators (e.g. baptists) to regulate more (here ban alcohol during the prohibition) to maximize profits and deter entry. This seems to be happening in AI where the industry lobbies for stricter regulation. Yet, in the EU, OpenAI lobbied to water down EU AI regulation to not classify GPT as ‘high risk’ to exempt it from stringent legal requirements.[1] In the US, the FTC recently said that Big Tech intimidates competition regulators.[2] Capture can also manifest by passively accepting industry practices, which is problematic in high-risk scenarios where thorough regulation is key. After all, AI expertise gathers in particular geographic communities. We must avoid cultural capture when social preferences interfere with policy, since regulators interact with workers from regulated firms. Although less of a concern in a rule-based system, a standard-based system would enable more informal influence via considerable regulator discretion. We must reduce these risks, e.g. by appointing independent regulators and requiring public disclosure of regulatory decisions.”
“Big Tech also takes greater legal risks by aggressively and (illegally) collecting data with negative externalities for users and third parties (similarly, Big Tech often violates IP [3] while lobbying against laws to stop patent trolling, claiming they harm real patents, but actually, this makes new patents from startups worth less and more costly to enforce.)[4] “
Microsoft, Google, Amazon, Nvidia have quite a bit of exposure to Anthropic, DeepMind, OpenAI, and xAI.