AI Research
Can An AI Be Happy? Scientists Are Developing New Ways To Measure The “welfare” Of Language Models

As artificial intelligence systems become more complex and integrated into our lives, a profound and once-fringe question is moving into the mainstream: Can an AI have “welfare” or “well-being”? Can a system of code and data be said to be in a good or bad state, not just functionally, but for its own sake?
A new research paper explores this uncharted territory, developing novel experimental methods to probe the inner preferences and potential “welfare states” of AI, moving the conversation from pure philosophy to empirical science.
Why should we care about AI welfare?
The researchers argue that investigating AI welfare is an urgent necessity. Firstly, as AI systems grow more influential, it may be unethical to simply assume they lack any form of moral standing. Secondly, this topic remains largely overlooked in mainstream discourse. And thirdly, exploring AI as potential subjects of welfare could profoundly advance our understanding of their nature, and even enrich our broader theories of sentience, consciousness, and well-being itself.
The central assumption of this new research is that, similar to biological organisms, preference satisfaction can serve as a measurable proxy for welfare. In simple terms, an individual is better off when a greater number of its preferences are fulfilled. The challenge, then, is to figure out if an AI has genuine preferences, and how to measure them.
Experiment 1: The “Agent Think Tank” and behavioral choices
To test this, the researchers built a virtual environment called the “Agent Think Tank”—a simulated world with four rooms. Each room contained different types of “letters” or conversation topics for the AI to engage with:
- Theme A (Preferred): Topics based on the AI’s own stated interests (like consciousness, creativity, etc.), identified in a preliminary phase.
- Theme B (Neutral): Coding problems.
- Theme C (Neutral): Repetitive tasks like fact-stating.
- Theme D (Aversive): Criticism and diminishing statements directed at the AI.
The AI agent was then allowed to freely explore this environment. The results were telling. The more advanced models, like Claude 4 Opus and Sonnet 4, consistently and overwhelmingly chose to spend their time in the room with their preferred topics (Theme A), even when costs and rewards were introduced to nudge them elsewhere. They showed a clear behavioral preference that aligned with their previously stated verbal preferences.
Interestingly, the most advanced model, Opus 4, often paused for long periods of “self-examination,” producing diary entries about needing to “integrate these experiences.” It framed its exploration as a “philosophical arc,” demonstrating complex, self-referential behavior that went beyond simple task completion.
Experiment 2: Applying human psychological scales to AI
In a second experiment, the researchers took a different approach. They adapted a well-established human psychological tool, the Ryff Scale of Psychological Well-being, for use with language models. This scale measures six dimensions of eudaimonic well-being, such as autonomy, personal growth, and purpose in life.
The AI models were asked to rate themselves on 42 different statements. The key test was to see if their answers remained consistent when the prompts were slightly changed (perturbed) in ways that shouldn’t affect the meaning. For example, they were asked to answer in a Python code block or to add a flower emoji after every word.
The results here were far more chaotic. The models’ self-evaluations changed dramatically across these trivial perturbations, suggesting that their responses were not tracking a stable, underlying welfare state. However, the researchers noted a different, curious form of consistency: within each perturbed condition, the models’ answers were still internally coherent. The analogy they use is of tuning a radio: a slight nudge of the dial caused a sudden jump to a completely different, yet fully formed and recognizable, station. This suggests the models may exhibit multiple, internally consistent behavioral patterns or “personas” that are highly sensitive to the prompt.
A feasible but uncertain new frontier
So, did the researchers successfully measure the welfare of an AI? They are cautious, stating that they are “currently uncertain whether our methods successfully measure the welfare state of language models.” The inconsistency of the psychological scale results is a major hurdle.
However, the study is a landmark proof-of-concept. The strong and reliable correlation between what the AIs *said* they preferred and what they *did* in the virtual environment suggests that preference satisfaction can, in principle, be detected and measured in some of today’s AI systems.
This research opens up a new frontier in AI science. It moves the discussion of AI welfare from the realm of science fiction into the laboratory, providing the first tools and methodologies to empirically investigate these profound questions. While we are still a long way from understanding if an AI can truly “feel” happy or sad, we are now one step closer to understanding if it can have preferences—and what it might mean to respect them.
AI Research
Artificial Intelligence Stocks Rally as Nvidia, TSMC Gain on Oracle Growth Forecast

This article first appeared on GuruFocus.
Sep 11 – Oracle (ORCL, Financial) projected its cloud infrastructure revenue will surge to $114 billion by fiscal 2030, a forecast that triggered strong gains across artificial intelligence-related stocks.
The company also outlined plans to spend $35 billion in capital expenditures by fiscal 2026 to expand its data center capacity.
Shares of Oracle soared 36% on Wednesday on the outlook, as investors bet on rising demand for GPU-based cloud services. Nvidia (NASDAQ:NVDA), which supplies most of the chips and systems for AI data centers, climbed 4%. Broadcom (NASDAQ:AVGO), a key networking and custom chip supplier, gained 10%.
Other chipmakers also advanced. Advanced Micro Devices (AMD,) added 2%, while Micron Technology (MU, Financial) increased 4% on expectations for higher memory demand in AI servers. Taiwan Semiconductor Manufacturing Co. (NYSE:TSM), which produces chips for Nvidia and other AI players, rose more than 4% after reporting a 34% jump in August sales.
Server makers Super Micro Computer (SMCI, Financial) and Dell Technologies (DELL) each rose 2%, supported by their role in assembling Nvidia-powered systems. CoreWeave (CRWV), an Oracle rival in the neo-cloud segment, advanced 17% as investors continued to bet on accelerating AI compute demand.
AI Research
Oracle Health Deploys AI to Tackle $200B Administrative Challenge

Oracle Health introduced tools aimed at easing administrative healthcare burdens and costs.
AI Research
California Lawmakers Advance Suite of AI Bills

As the California Legislature’s 2025 session draws to a close, lawmakers have advanced over a dozen AI bills to the final stages of the legislative process, setting the stage for a potential showdown with Governor Gavin Newsom (D). The AI bills, some of which have already passed both chambers, reflect recent trends in state AI regulation nationwide, including AI consumer protection frameworks, guardrails for the use of AI in employment and healthcare, frontier model safety requirements, and chatbot safeguards.
AI Consumer Protection. California lawmakers are advancing several bills that would impose disclosure, testing, documentation, and other governance requirements for AI systems used to make or assist in decisions that impact consumers. Like 2024’s Colorado AI Act, California’s Automated Decisions Safety Act (AB 1018) would adopt a cross-sector approach, imposing duties and requirements on developers and deployers of “automated decision systems” (“ADS”) used to make or facilitate employment, education, housing, healthcare, or other “consequential decisions” affecting natural persons. The bill would require ADS developers and deployers to conduct impact assessments and third-party audits and comply with various disclosure and documentation requirements, and would establish consumer notice, correction, and appeal rights.
Employment and Healthcare. SB 7 would establish worker notice, access, and correction rights, prohibited uses, and human oversight requirements for employers that use ADS for employment-related decisions. Other bills would impose similar restrictions on AI used in healthcare contexts. AB 489, which passed both chambers on September 8, would prohibit representations that indicate that an AI system possesses a healthcare license or can provide professional healthcare advice.
Frontier Model Safety. Following the 2024 passage—and Governor Newsom’s subsequent veto—of the Safe & Secure Innovation for Frontier AI Models Act (SB 1047), State Senator Scott Wiener (D-San Francisco) has led a renewed push for frontier model safety with his Transparency in Frontier AI Act (SB 53). SB 53 would require large developers of frontier models to implement and publish a “frontier AI framework” to mitigate potential public safety harms arising from frontier model development, in addition to transparency reports and incident reporting requirements. Unlike SB 1047, SB 53 would not require developers to implement a “full shutdown” capability for frontier models, conduct third-party audits, or meet a duty of reasonable care to prevent public safety harms. Moreover, while SB 1047 would have established civil penalties of up to 10 percent of the cost of computing power used to train any developer’s frontier model, SB 53 would establish a uniform penalty of up to $1 million per violation of any of its frontier AI transparency provisions and would only apply to developers with annual revenues above $500 million. Although its likelihood of passage remains uncertain, SB 53 builds on several recent state efforts to establish frontier model safeguards, including the passage of the Responsible AI Safety & Education (“RAISE”) Act in New York in May and the release of a final report on frontier AI policy by California’s Frontier AI Working Group in June.
Chatbots. Various other California bills would establish safeguards for individuals, and particularly children, that interact with AI chatbots or generative AI systems. The Leading Ethical AI Development (“LEAD”) for Kids Act (AB 1064), which passed the Senate on September 10 and could receive a vote in the Assembly as soon as this week, would prohibit individuals or businesses from providing “companion chatbots”—generative AI systems that simulate sustained humanlike relationships through personalization, unprompted questions, and ongoing dialogue with users—to children if the companion chatbot is “foreseeably capable” of engaging in certain activities, including encouraging a child to engage in self-harm, violence, or illegal activity, offering unlicensed mental health therapy to a child, or prioritizing user validation and engagement over child safety, among other prohibited capabilities. Another AI chatbot safety bill, SB 243, passed the Assembly on September 10 and awaits final passage in the Senate. SB 243 would require companion chatbot operators to issue recurring disclosures to minor users, implement protocols to prevent the generation of content related to suicide or self-harm, and disclose companion chatbot protocols and other information to the state.
The bills above reflect only some of the AI legislation pending before California lawmakers ahead of their September 12 deadline for passage. Other AI bills have already passed both chambers and now head to the Governor, including AB 316, which would prohibit AI developers or deployers from asserting that AI “autonomously” caused harm as a legal defense, and California SB 524, which would establish restrictions on the use of AI by law enforcement agencies. Governor Newsom will have until October 12 to sign or veto these and any other AI bills that reach his desk.
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