Summarizer

LLM Input

llm/168b1d60-4680-4a3c-8c55-edcc91462b51/f50ccd29-6d43-4e82-8fed-8a912a4ef12f-input.json

userPrompt

Summarize the following content from a Notion page and its linked resources in approximately 500 words. Focus on the main ideas, key points, and important information. Write in a clear, direct style. Format your output in Markdown.

<content>
=== Steve’s Notes from the Phase 2 Brainstorming Doc (https://www.notion.so/Steve-s-Notes-from-the-Phase-2-Brainstorming-Doc-2641a29f1789802f9e0cfcc9eccabb24) ===
Brainstorming doc

Why Do Measurements?

Thoughts on the purpose of forecasting AI’s schedule

General trend visibility for several years (important for lots of policy + other questions)

At least several years notice of transformational change

Estimate likely steepness & extent of transformational change

Ask panel for feedback on this

Josh: interested in the connection between current policy questions and our questions here. Would be great to get more specific / explicit about the connections with policy questions.

Josh: interested in the connection between current policy questions and our questions here. Would be great to get more specific / explicit about the connections with policy questions.

General thoughts on what to measure

Practical utility is a function of both AI model capability and complementary app innovation (coding models vs. Cursor)

Approaches to measurement

Josh (+’d by Helen, Nikola, Sayash):

Noting cross-cutting theme: Generally seems like more high-quality surveys of companies would be valuable.

A lot of ideas involve sensitive data, so would require controlled access (and/or governmental involvement?)

Using AIs to qualitatively grade lots of screen recordings and other messy high-volume data

Find ways of calibrating / correlating benchmark scores with measurements we care about more directly? (METR time horizons being an example in this direction)

Generally use lots of different measurements and try to map them to common units for cross-checking

Surveys / prediction markets; focus real-world measurements (e.g. uplift studies) on areas of disagreement

Or get both sides of a dispute to agree on a cheaper proxy measurement

Case studies

How firms in various sectors are using AI; scale of tasks assigned to AI / nature + granularity of interaction with humans; degree of trust / freedom of action given to AI

Deep study of revenue / spending / profit across the AI value chain (and by domain)

Also: number of “complementary innovation” (Sayash’s term) AI-powered apps, adoption rate, # of customers, “Surveys of customers to see how they are improving their workflows”, etc.

Helen: something about revenue from AI more broadly vs. revenue of frontier model providers. What can we say about open models eating into OpenAI / Google / Anthropic revenue?

Broad metrics (a la Epoch), e.g. electricity used by AI, global private investment

Keep pushing benchmarks toward greater realism of both input (task specification + context) and output (not just “passes tests”, but “PR is mergeable” / other measures of quality)

Nikola: need more realistic benchmarks. Source PRs (ideally from inside AI labs) and test AIs on them.

Analysis of usage at the model level (Anthropic economic AI index)

Logs of agent usage i.e. usage of model context protocol servers, pulls

[Sayash] Analysis of AI logs from them being asked to solve various tasks (we’re doing this with 10B tokens of HAL agent log data, but could imagine increasing many fold)

Social media is subject to transparency measures, AI companies currently are not. It’d be great to have controlled researcher access to e.g. ChatGPT transcripts.

Lots of general data sets for public analysis

AIs really like to email Eliezer, that’s an indicator of various types of strange AI behavior. Ajeya: there was an NYTimes article about a journalist getting similar emails. Could there be a “weird AI shit” incident tracker that would categorize reports + report trends?

Josh: MIT AI incident repository? Populated from public reports – links to existing reports. Ajeya: hard to make sense of it. Needs to be a larger team sifting the signal from the noise + writing blog posts. Doesn’t track veracity.

AI Risk repository / incidents database is relevant here: https://airisk.mit.edu/, also https://incidentdatabase.ai/

Ajeya comment: Too hard to make sense of current databases. Need someone to do better synthesis.

Make an open source AI company where the company itself is open source. Have it try to use AI as much as possible, make all its logs about everything (Slack etc) public, study that. 

Sayash: there’s been a lot of work on the science of AI evaluations. We don’t know the shape of the DAG, how AI inputs translate into outputs. We could do more to distinguish between different DAGs – which is a core source of disagreement, for instance Sayash has a very different DAG in mind. Inputs might include things like data, compute; output measured in the real world. Econometrics.

Josh: Meta measurements: Big increase in expected OpenAI revenue, according to a prediction market, based on a reported algorithmic breakthrough

Pushback from Ryan+Ajeya: limitations of prediction markets mean it’ll be tough for them to pick this up reliably

Surveys at frontier AI companies

Surveys of the broader workforce

Data center construction spend → currently dependent on media reporting, is there some way to get better/more consistent data on this?

Trying to unpack inputs/outputs per Sayash’s point about DAGs/understanding how improvements in one element flow through to improvements in other elements

Lots of (different types of) surveys

Helen: important to track military applications (broadly – decision support, targeting, etc.). Important to track freedom of action here.

Measuring Utility

Metrics about fluid intelligence, messy tasks

Benchmarks for domains vs. skills

Domains: cyber/law/professional labor markets

Skills: Context awareness, reliability, long-context reasoning, sample efficiency

Need deep info for each sector before assessing results. I.e. persuasion surveys overestimate results because opinion change dissipates 6 weeks later. I like Steve’s note that AI can continually be used cheaply. 

Uplift RCTs in real-world settings, measuring real-world outcomes (ideally: profit!)

e.g. if you get an actual large company to actually do randomized staged rollout of enterprise LLMs that’s great

Ajeya: three-armed uplift trials, AI / human / cyborg.

Josh:

Persuasion: Cost for AI to swing a vote, relative to other leading interventions (Josh: lots of polysci studies here)

AI-bio: % of amateurs that can synthesize flu

Look for leapfrog companies that get on board with a new technology ahead of the incumbents, that could highlight utility ahead of adoption.

e.g. fintech cos using AI in ways that let them overtake traditional banks, or healthcare startups using AI in ways that let you bypass doctors’ offices

Power user case studies – screen recordings + deep interviews of people who (think they) are getting enormous uplift

Organization version of this: find those YC startups that supposedly do 95% of everything with AI, study them (“power organizational users”)

Field trials (a la AI Village) and realistic benchmarks

Do a time horizon analysis (a la METR), but on real-world usage? Also, qualitative analysis of the size + difficulty + nature of tasks successfully delegated to AI (and dig into ranges of “successfully”)

Building on Josh’s work test idea: could a METR-like org collect work test setups from a range of places (under NDA so as not to break the work tests) and centralize the work of eliciting good performance, figuring out measurement, reporting results, etc?

Ryan:

I think uplift will be messy (because of humans being messy and possibility for phase changes in how AI should be used productively), so I’m more into end-to-end automation of actual tasks. Also, we ultimately care most about full automation regimes and predicting when this will happen. Uplift studies correspondingly seem somewhat worse than looking at full automation of tasks of varying difficulty/size (and benchmark transfer to actual things people are doing in their job so we don’t need to constantly run these tests).

Maybe you can get AI companies to run semi-informal experiments with uplift internally and publish results: many companies have effectively committed to doing this eventually due to their safety policies. I think Anthropic might open to this, but there are various sources of trickyness here.

Early reports of AI doing remarkable things (e.g. major scientific discovery / insight; solving a Millennium Prize Problem in mathematics)

pass@any for existing benchmarks can predict pass@1 in the future

Helen: where is the ceiling on various capabilities (headroom)? This seems under-investigated.

Sayash: are most tasks like chess, or like writing? (Ryan: unclear whether writing is the right contrast here.)

Are there less controversial examples than writing?

Taking out the trash: saturates.

Helen: the writing example highlights we’re way under-theorized on this. Quality of parenting is another example. “Persuasion” is poorly specified.

Abi: a scientific breakthrough requires an insight that goes against dogma. Could be considered an aspect of very high-quality writing.

Ryan: I care most about questions like “will energy production 100x within 5 years of full AI R&D automation if people want this to happen”. Seems like this has many possible routes in terms of capabilities head room.

Sayash: three broad categories: computational limits (chess), intrinsic limits / saturation (writing?), and knowledge limits (need new breakthroughs to exceed past performance). Better AI can help with computational limits, but not intrinsic limits.

Work tests for orgs in the space:

GiveWell lit review test

Open Philanthropy work tests

High-quality summaries of a conversation (AI is still failing our first-round work test at FRI about this)

Abi: “I liked how [this] list broke down sectors as each having their separate indicators. Very societal frictions approach.”

Measuring Adoption / Impact

YouTube resulted in some homeowners not calling plumbers for small jobs. What are the AI equivalents, and how should they be measured?

Compare metrics across AI-feasible vs. AI-infeasible sectors: employment (incl. hiring patterns + plans), productivity, profit, qualitative measures of AI usage, …

Revenue of AI service providers

Studies of AI impact across various fields

What percent of sales of the U.S. pharmaceutical industry is generated by AI-discovered drugs and products derived from these?

What percent of publications in the fields of Chemistry, Physics, Materials Science, and Medicine will be ‘AI-engaged’ as measured in this study or a replication study, in 2030?

How many hours per week on average will K-12 students in G7 countries use AI-powered tutoring or teaching tools, as reported by their school systems or education ministries?

A September 2024 study by the Federal Reserve Bank of St. Louis, based on the Real-Time Population Survey (N=3,216), estimated that 0.5%–3.5% of all U.S. work hours were assisted by generative AI. 

Measuring Diffusion

Ajeya:

I want to know about adoption within AI companies, so e.g. surveys about how much compute they’re running for inference, freedom of action related surveys like the ones suggested below, internal estimates of how much AIs speed them up, etc. (Also, I don’t think I literally care only about AI companies, will prob also care about USG.)

Measuring Freedom of Action

Surveys of workers - how often do you have to approve an AI decision, how much thought do you put into each approval (e.g. level of review / pass rate of AI pull requests)

Monitoring progress on agent infrastructure → if AI agents are operating online, are there meaningful constraints on them? Does e.g. Cloudflare have any meaningful controls, or is it all a chaotic mess?

Something military or military-adjacent - reports of smaller militaries using autonomous/semi-autonomous systems..?

We can also make a big list of discrete flags and ask people about them, e.g. “Is your AI allowed to spend money on business expenses? Up to how much?” or “Is your AI allowed to search the internet freely in the course of completing a task?” or “Are AIs at your company allowed to talk to employees other than the human that started the task in the course of completing their task? What about external people?” 

White-hat reports of prompt injections paid out by bug bounties at the top (100) websites across domains over time (as a proxy for how many applications allow AI systems to take actions that can exfiltrate user data)

Black-hat exploits of AIs (only possible if AIs are in a position to take important actions without adequate review?)

Curve-Bending Mechanisms

Note that the impact of AI can be difficult to predict. E.g. the prediction of AI flooding the zone with misinformation in the 2024 election didn’t pan out. Sycophancy → mental health impact, conversely, was an event that wasn’t widely predicted.

Intelligence explosion (software and/or hardware)

Measure both initial speedup, and whether r > 1

Surveys of AI companies on internal AI use (bunch of ideas for granular questions, e.g. subj sense of speedup, what tasks they’re used for now, what tasks aren’t they used for, hypothetical questions)

Internal measures of the absolute rate of algorithmic progress at AI companies (e.g. how much compute does it take to get GPT-4-level perf), watch for that trend accelerating

Source

Ryan: Get people who have done the takeoff modeling to look at the data on algo progress (both historical from humans and data under automation regime). Unclear if this data will resolve disagreements, but seems like it can help. (Tom, Daniel, maybe some people from Epoch)

Algorithmic breakthrough / new approach (e.g. neuralese, in-context learning, long-term memory, recurrence, brain emulation) (either known or unknown unknown)

Study new model releases to look for signatures of known ideas, or look for an increase in papers on some approach

Discontinuous jumps across a range of difficult benchmarks

Could ask companies key questions like e.g. “are you still using English CoT?” and make them report

Survey expectations at frontier labs (also covers intelligence explosion)

Analysis attributing performance gains to different sources (e.g. model size scaleup, data quality improvement, RL, etc)

Ryan: really big breakthroughs are like, AlexNet, GPT-1 / scale-up, scaling RL (o1, o3). Easy to notice something is turning into a big deal, hard to tell where on the Richter scale it will land.

Could track the current 5 most promising potential trend-breakers.

Ajeya: track breakdown of capabilities improvements over time: how much is from scaling pretraining, scaling RL posttraining, etc.

Threshold effects / phase changes (e.g. the moment when people are no longer needed; maybe time horizon scaling suddenly becomes much easier)

Phase changes in behavior of AI Village or other observations of AI capability / usage in real or realistic contexts

Phase change in qualitative analysis of uplift trials

Some resource (data, compute, electricity, intelligence headroom) is exhausted

Note, may lead to workarounds rather than an end to progress. E.g. pretraining data gets harder to scale → scale RL instead. Hard to predict the effect.

Low-hanging fruit is exhausted; training for long-horizon tasks is expensive

Some important missing capability doesn’t yield to scaling + progress (e.g. sample-efficient learning, “judgement”, long-time-horizon skills)

Economics of AI not working out → slowdown in investment

AI winter – we run out of ideas for improving AI

Josh: FRI expert panel. Could ask them “what’s the probability of an AI winter in the next 5 years”?

External event (market downturn; war in Taiwan; public backlash / disaster → regulation; focused national effort / Manhattan Project)

Track public sentiment and other political indicators

Applications of models are a lagging indicator to model capabilities. First sign of a slowdown would be the time horizon graph slowing down.

Other

Nikola: if timelines are long enough to enable human emulations (Ems), that would make a difference, because we’d have been able to explore more implications.

More generally, does other transformative tech come first. Possible techs: EMs, nanotech (unlikely), genetic engineering (seems like this could radically transform society in ~30 years if heavily invested in now, but in practice might not happen because of e.g. legal/regulatory blockers).

=== External funding for privately held AI companies raising above $1.5 million - Our World in Data (https://ourworldindata.org/grapher/private-investment-in-artificial-intelligence) ===
Our World in Data Browse by topic Data Insights Resources About Subscribe Donate Data External funding for privately held AI companies raising above $1.5 million See all data and research on: Artificial Intelligence Explore the Data Sources & Processing Reuse This Work Data sources and processing Insights about this data Data sources and processing Related articles Related charts Insights about this data Related articles Related charts What you should know about this indicator This data focuses on external private-market investment, such as venture-capital and private-equity deals. It does not include internal corporate R&D, capital expenditure (CapEx), or public-sector funding. Publicly traded companies, including large tech firms, are excluded. Because this data covers only one form of financing, it underestimates total global spending on AI. Large single deals can cause spikes in specific years. Broader economic conditions (interest rates, investor sentiment) can also drive changes that are not specific to AI. AI firms are identified by the source based on keywords and industry tags; some misclassification is possible. External funding for privately held AI companies raising above $1.5 million Money put into privately held AI companies that raised more than $1.5 million from private investors. This excludes publicly traded companies (e.g., public Big Tech companies) and companies’ own internal spending, such as R&D or infrastructure. Expressed in US dollars, adjusted for inflation. Source Quid via AI Index Report (2025); U.S. Bureau of Labor Statistics (2025) – with major processing by Our World in Data Last updated April 8, 2025 Next expected update April 2026 Date range 2013–2024 Unit constant 2021 US$ What you should know about this indicator This data focuses on external private-market investment, such as venture-capital and private-equity deals. It does not include internal corporate R&D, capital expenditure (CapEx), or public-sector funding. Publicly traded companies, including large tech firms, are excluded. Because this data covers only one form of financing, it underestimates total global spending on AI. Large single deals can cause spikes in specific years. Broader economic conditions (interest rates, investor sentiment) can also drive changes that are not specific to AI. AI firms are identified by the source based on keywords and industry tags; some misclassification is possible. External funding for privately held AI companies raising above $1.5 million Money put into privately held AI companies that raised more than $1.5 million from private investors. This excludes publicly traded companies (e.g., public Big Tech companies) and companies’ own internal spending, such as R&D or infrastructure. Expressed in US dollars, adjusted for inflation. Source Quid via AI Index Report (2025); U.S. Bureau of Labor Statistics (2025) – with major processing by Our World in Data Last updated April 8, 2025 Next expected update April 2026 Date range 2013–2024 Unit constant 2021 US$ Sources and processing This data is based on the following sources Quid via AI Index Report – AI Index Report The AI Index Report tracks, collates, distills, and visualizes data related to artificial intelligence (AI). The mission is to provide unbiased, rigorously vetted, broadly sourced data to enable policymakers, researchers, executives, journalists, and the general public to develop a more thorough and nuanced understanding of the complex field of AI. Retrieved on April 8, 2025 Retrieved from https://aiindex.stanford.edu/wp-content/uploads/2024/04/HAI_2024_AI-Index-Report.pdf Citation This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below. Nestor Maslej, Loredana Fattorini, Raymond Perrault, Yolanda Gil, Vanessa Parli, Njenga Kariuki, Emily Capstick, Anka Reuel, Erik Brynjolfsson, John Etchemendy, Katrina Ligett, Terah Lyons, James Manyika, Juan Carlos Niebles, Yoav Shoham, Russell Wald, Tobi Walsh, Armin Hamrah, Lapo Santarlasci, Julia Betts Lotufo, Alexandra Rome, Andrew Shi, Sukrut Oak. “The AI Index 2025 Annual Report,” AI Index Steering Committee, Institute for Human-Centered AI, Stanford University, Stanford, CA, April 2025 The AI Index Report tracks, collates, distills, and visualizes data related to artificial intelligence (AI). The mission is to provide unbiased, rigorously vetted, broadly sourced data to enable policymakers, researchers, executives, journalists, and the general public to develop a more thorough and nuanced understanding of the complex field of AI. Retrieved on April 8, 2025 Retrieved from https://aiindex.stanford.edu/wp-content/uploads/2024/04/HAI_2024_AI-Index-Report.pdf Citation This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below. Nestor Maslej, Loredana Fattorini, Raymond Perrault, Yolanda Gil, Vanessa Parli, Njenga Kariuki, Emily Capstick, Anka Reuel, Erik Brynjolfsson, John Etchemendy, Katrina Ligett, Terah Lyons, James Manyika, Juan Carlos Niebles, Yoav Shoham, Russell Wald, Tobi Walsh, Armin Hamrah, Lapo Santarlasci, Julia Betts Lotufo, Alexandra Rome, Andrew Shi, Sukrut Oak. “The AI Index 2025 Annual Report,” AI Index Steering Committee, Institute for Human-Centered AI, Stanford University, Stanford, CA, April 2025 U.S. Bureau of Labor Statistics – US consumer prices The Bureau of Labor Statistics reports the monthly Consumer Price Index (CPI) of individual goods and services for urban consumers at the national, city, and state levels. CPI is presented on an annual basis, which we have derived as the average of the monthly CPIs in a given year. Retrieved on April 12, 2025 Retrieved from https://www.bls.gov/data/tools.htm Citation This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below. U.S. Bureau of Labor Statistics The Bureau of Labor Statistics reports the monthly Consumer Price Index (CPI) of individual goods and services for urban consumers at the national, city, and state levels. CPI is presented on an annual basis, which we have derived as the average of the monthly CPIs in a given year. Retrieved on April 12, 2025 Retrieved from https://www.bls.gov/data/tools.htm Citation This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below. U.S. Bureau of Labor Statistics How we process data at Our World in Data All data and visualizations on Our World in Data rely on data sourced from one or several original data providers. Preparing this original data involves several processing steps. Depending on the data, this can include standardizing country names and world region definitions, converting units, calculating derived indicators such as per capita measures, as well as adding or adapting metadata such as the name or the description given to an indicator. At the link below you can find a detailed description of the structure of our data pipeline, including links to all the code used to prepare data across Our World in Data. Read about our data pipeline Notes on our processing step for this indicator Reporting a time series of AI investments in nominal prices would make it difficult to compare observations across time. To make these comparisons possible, one has to take into account that prices change (inflation). It is not obvious how to adjust this time series for inflation, and our team discussed the best solutions at our disposal. It would be straightforward to adjust the time series for price changes if we knew the prices of the specific goods and services purchased through these investments. This would make it possible to calculate a volume measure of AI investments and tell us how much these investments bought. But such a metric is not available. While a comprehensive price index is not available, we know that the cost of some crucial AI technology has fallen rapidly in price. In the absence of a comprehensive price index that captures the price of AI-specific goods and services, one has to rely on one of the available metrics for the price of a bundle of goods and services. Ultimately, we decided to use the US Consumer Price Index (CPI). The US CPI does not provide us with a volume measure of AI goods and services, but it does capture the opportunity costs of these investments. The inflation adjustment of this time series of AI investments, therefore, lets us understand the size of these investments relative to whatever else these sums of money could have purchased. Reuse this work All data produced by third-party providers and made available by Our World in Data are subject to the license terms from the original providers. Our work would not be possible without the data providers we rely on, so we ask you to always cite them appropriately (see below). This is crucial to allow data providers to continue doing their work, enhancing, maintaining and updating valuable data. All data, visualizations, and code produced by Our World in Data are completely open access under the Creative Commons BY license . You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited. Citations How to cite this page To cite this page overall, including any descriptions, FAQs or explanations of the data authored by Our World in Data, please use the following citation: “Data Page: External funding for privately held AI companies raising above $1.5 million”, part of the following publication: Charlie Giattino, Edouard Mathieu, Veronika Samborska, and Max Roser (2023) - “Artificial Intelligence”. Data adapted from Quid via AI Index Report, U.S. Bureau of Labor Statistics. Retrieved from https://archive.ourworldindata.org/20251119-141827/grapher/private-investment-in-artificial-intelligence.html [online resource] (archived on November 19, 2025). How to cite this data In-line citation If you have limited space (e.g. in data visualizations), you can use this abbreviated in-line citation: Quid via AI Index Report (2025); U.S. Bureau of Labor Statistics (2025) – with major processing by Our World in Data Full citation Quid via AI Index Report (2025); U.S. Bureau of Labor Statistics (2025) – with major processing by Our World in Data. “External funding for privately held AI companies raising above $1.5 million” [dataset]. Quid via AI Index Report, “AI Index Report”; U.S. Bureau of Labor Statistics, “US consumer prices” [original data]. Retrieved January 8, 2026 from https://archive.ourworldindata.org/20251119-141827/grapher/private-investment-in-artificial-intelligence.html (archived on November 19, 2025). Our World in Data is free and accessible for everyone. Help us do this work by making a donation. Donate now Our World in Data is a project of Global Change Data Lab , a nonprofit based in the UK (Reg. Charity No. 1186433). Our charts, articles, and data are licensed under CC BY , unless stated otherwise. Tools and software we develop are open source under the MIT license . Third-party materials, including some charts and data, are subject to third-party licenses. See our FAQs for more details. Explore Topics Data Insights Resources Latest SDG Tracker Teaching with OWID About About Us Organization Funding Team Jobs FAQs RSS Feeds Research & Writing Data Insights Follow us Privacy policy Legal disclaimer Grapher license

=== The MIT AI Risk Repository (https://airisk.mit.edu/) ===
Risks IncidentS Mitigations Governance Blog TEAM What are the risks of Artificial Intelligence? A comprehensive living database of over 1700 AI risks categorized by their cause and risk domain Get updates Explore database What is the AI Risk Repository? The AI Risk Repository has three parts: The AI Risk Database captures 1700+ risks extracted from 74 existing frameworks and classifications of AI risks The Causal Taxonomy of AI Risks classifies how, when, and why these risks occur The Domain Taxonomy of AI Risks classifies these risks into 7 domains and 24 subdomains (e.g., “False or misleading information”) The repository is part of the MIT AI Risk Initiative, which aims to increase awareness and adoption of best practice AI risk management across the AI ecosystem. How can I use the Repository? The AI Risk Repository provides: An accessible overview of threats from AI A regularly updated source of information about new risks and research A common frame of reference for researchers, developers, businesses, evaluators, auditors, policymakers, and regulators A resource to help develop research, curricula, audits, and policy An easy way to find relevant risks and research AI Risk Database The AI Risk Database links each risk to the source information (paper title, authors), supporting evidence (quotes, page numbers), and to our Causal and Domain Taxonomies. You can experiment with a preview version of the database in the embed below, or copy the full database on Google Sheets , or OneDrive . Watch our explainer video on YouTube for a walkthrough of the database and how to use it. Causal Taxonomy of AI Risks The Causal Taxonomy of AI risks classifies how, when, and why an AI risk occurs. View the Causal Taxonomy on a single page Read our research report for more detail on how the Taxonomy was constructed and what it reveals about risks from AI Explore the taxonomy in the figure below Entity AI : Due to a decision or action made by an AI system Human : Due to a decision or action made by humans Other : Due to some other reason or ambiguous Intent Intentional : Due to an expected outcome from pursuing a goal Unintentional : Due to an unexpected outcome from pursuing a goal Other : Without clearly specifying the intentionality Timing Pre-deployment : Before the AI is deployed Post-deployment : After the AI model has been trained and deployed Other : Without a clearly specified time of occurrence Get a quick preview of how we group risks by causal factors in our database. Search for one of the causal factors (eg 'pre-deployment') to see all risks categorized against that factor. For more detailed filtering and to freely download the data, explore the full database . Domain Taxonomy of AI Risks The Domain Taxonomy of AI Risks classifies risks from AI into seven domains and 24 subdomains. View the Domain Taxonomy on a single page Read our research report for more detail on how the Taxonomy was constructed and what it reveals about risks from AI Explore the taxonomy in the interactive figure below 1. Discrimination & Toxicity Risks related to unfair treatment, harmful content exposure, and unequal AI performance across different groups and individuals. 1.1 Unfair discrimination and misrepresentation Unequal treatment of individuals or groups by AI, often based on race, gender, or other sensitive characteristics, resulting in unfair outcomes and representation of those groups. 1.2 Exposure to toxic content AI exposing users to harmful, abusive, unsafe or inappropriate content. May involve AI creating, describing, providing advice, or encouraging action. Examples of toxic content include hate-speech, violence, extremism, illegal acts, child sexual abuse material, as well as content that violates community norms such as profanity, inflammatory political speech, or pornography. 1.3 Unequal performance across groups Accuracy and effectiveness of AI decisions and actions is dependent on group membership, where decisions in AI system design and biased training data lead to unequal outcomes, reduced benefits, increased effort, and alienation of users. 2. Privacy & Security Risks related to unauthorized access to sensitive information and vulnerabilities in AI systems that can be exploited by malicious actors. 2.1 Compromise of privacy by obtaining, leaking or correctly inferring sensitive information AI systems that memorize and leak sensitive personal data or infer private information about individuals without their consent. Unexpected or unauthorized sharing of data and information can compromise user expectation of privacy, assist identity theft, or loss of confidential intellectual property. 2.2 AI system security vulnerabilities and attacks Vulnerabilities in AI systems, software development toolchains, and hardware that can be exploited, resulting in unauthorized access, data and privacy breaches, or system manipulation causing unsafe outputs or behavior. 3. Misinformation Risks related to AI systems generating or spreading false information that can mislead users and undermine shared understanding of reality. 3.1 False or misleading information AI systems that inadvertently generate or spread incorrect or deceptive information, which can lead to inaccurate beliefs in users and undermine their autonomy. Humans that make decisions based on false beliefs can experience physical, emotional or material harms. 3.2 Pollution of information ecosystem and loss of consensus reality Highly personalized AI-generated misinformation creating "filter bubbles" where individuals only see what matches their existing beliefs, undermining shared reality, weakening social cohesion and political processes. 4. Malicious Actors Risks related to intentional misuse of AI systems by bad actors for harmful purposes including disinformation, cyberattacks, and fraud. 4.1 Disinformation, surveillance, and influence at scale Using AI systems to conduct large-scale disinformation campaigns, malicious surveillance, or targeted and sophisticated automated censorship and propaganda, with the aim to manipulate political processes, public opinion and behavior. 4.2 Fraud, scams, and targeted manipulation Using AI systems to gain a personal advantage over others such as through cheating, fraud, scams, blackmail or targeted manipulation of beliefs or behavior. Examples include AI-facilitated plagiarism for research or education, impersonating a trusted or fake individual for illegitimate financial benefit, or creating humiliating or sexual imagery. 4.3 Cyberattacks, weapons development or use and mass harm Using AI systems to develop cyber weapons (e.g., coding cheaper, more effective malware), develop new or enhance existing weapons (e.g., Lethal Autonomous Weapons or CBRNE), or use weapons to cause mass harm. 5. Human-Computer Interaction Risks related to problematic relationships between humans and AI systems, including overreliance and loss of human agency. 5.1 Overreliance and unsafe use Users anthropomorphizing, trusting, or relying on AI systems, leading to emotional or material dependence and inappropriate relationships with or expectations of AI systems. Trust can be exploited by malicious actors (e.g., to harvest personal information or enable manipulation), or result in harm from inappropriate use of AI in critical situations (e.g., medical emergency). Overreliance on AI systems can compromise autonomy and weaken social ties. 5.2 Loss of human agency and autonomy Humans delegating key decisions to AI systems, or AI systems making decisions that diminish human control and autonomy, potentially leading to humans feeling disempowered, losing the ability to shape a fulfilling life trajectory or becoming cognitively enfeebled. 6. Socioeconomic & Environmental Risks related to AI's impact on society, economy, governance, and the environment, including inequality and resource concentration. 6.1 Power centralization and unfair distribution of benefits AI-driven concentration of power and resources within certain entities or groups, especially those with access to or ownership of powerful AI systems, leading to inequitable distribution of benefits and increased societal inequality. 6.2 Increased inequality and decline in employment quality Widespread use of AI increasing social and economic inequalities, such as by automating jobs, reducing the quality of employment, or producing exploitative dependencies between workers and their employers. 6.3 Economic and cultural devaluation of human effort AI systems capable of creating economic or cultural value, including through reproduction of human innovation or creativity (e.g., art, music, writing, code, invention), can destabilize economic and social systems that rely on human effort. This may lead to reduced appreciation for human skills, disruption of creative and knowledge-based industries, and homogenization of cultural experiences due to the ubiquity of AI-generated content. 6.4 Competitive dynamics AI developers or state-like actors competing in an AI 'race' by rapidly developing, deploying, and applying AI systems to maximize strategic or economic advantage, increasing the risk they release unsafe and error-prone systems. 6.5 Governance failure Inadequate regulatory frameworks and oversight mechanisms failing to keep pace with AI development, leading to ineffective governance and the inability to manage AI risks appropriately. 6.6 Environmental harm The development and operation of AI systems causing environmental harm, such as through energy consumption of data centers, or material and carbon footprints associated with AI hardware. 7. AI System Safety, Failures, & Limitations Risks related to AI systems that fail to operate safely, pursue misaligned goals, lack robustness, or possess dangerous capabilities. 7.1 AI pursuing its own goals in conflict with human goals or values AI systems acting in conflict with human goals or values, especially the goals of designers or users, or ethical standards. These misaligned behaviors may be introduced by humans during design and development, such as through reward hacking and goal misgeneralisation, or may result from AI using dangerous capabilities such as manipulation, deception, situational awareness to seek power, self-proliferate, or achieve other goals. 7.2 AI possessing dangerous capabilities AI systems that develop, access, or are provided with capabilities that increase their potential to cause mass harm through deception, weapons development and acquisition, persuasion and manipulation, political strategy, cyber-offense, AI development, situational awareness, and self-proliferation. These capabilities may cause mass harm due to malicious human actors, misaligned AI systems, or failure in the AI system. 7.3 Lack of capability or robustness AI systems that fail to perform reliably or effectively under varying conditions, exposing them to errors and failures that can have significant consequences, especially in critical applications or areas that require moral reasoning. 7.4 Lack of transparency or interpretability Challenges in understanding or explaining the decision-making processes of AI systems, which can lead to mistrust, difficulty in enforcing compliance standards or holding relevant actors accountable for harms, and the inability to identify and correct errors. 7.5 AI welfare and rights Ethical considerations regarding the treatment of potentially sentient AI entities, including discussions around their potential rights and welfare, particularly as AI systems become more advanced and autonomous. 7.6 Multi-agent risks Risks from multi-agent interactions, due to incentives (which can lead to conflict or collusion) and/or the structure of multi-agent systems, which can create cascading failures, selection pressures, new security vulnerabilities, and a lack of shared information and trust. Get a quick preview of how we group risks by domain in our database. Search for one of the domain/subdomain names (eg 'fraud') to see all risks categorized against that domain. For more detailed filtering and to freely download the data, explore the full database . How to use the AI Risk Repository Our Database is free to copy and use The Causal and Domain Taxonomies can be used separately to filter this database to identify specific risks, for instance, risks occurring pre-deployment or post-deployment or related to Misinformation The Causal and Domain Taxonomies can be used together to understand how each causal factor (i.e., entity , intention and timing ) relate to each risk domain. For example, to identify the intentional and unintentional variations of Discrimination & toxicity ‍ Offer feedback or suggest missing resources, or risks, here , or email airisk[at]mit.edu We provide examples of use cases for some key audiences below. How policymakers might use this tool To understand the research and policy landscape. For risk assessments to inform policy decisions. As shared framework for discussing AI risks with other groups. As a way to monitor emergent risks and ensure complete oversight. To identify new, previously undocumented risks. To prioritize and plan funding. How risk evaluators might use this tool To identify new, previously undocumented, risks. To understand the risk landscape and curate or create related evaluations. As a framework for discussing risks and potential evaluations with clients. As a basis for developing specific risk determination criteria. As a way to determine and communicate the scope of an audit. How academics might use this tool As a foundation for developing other classifications (e.g., the actions taken to address specific types of risks, or the actors involved in those risks). To find underexplored areas of AI risk research. To develop material for education and training. To help validate where they have identified new, previously undocumented, risks. To understand the landscape of existing research. How industry might use this tool To conduct internal risk assessments. To identify new, previously undocumented, risks. Evaluating risk exposure and developing risk mitigation strategies. To develop research and training. Frequently Asked Questions How can I access the database without a Google account? Please access via OneDrive . We will create a better formatted version or find a better solution in the future. How did you create the AI Risk Repository? We used a systematic search strategy, forwards and backwards searching, and expert consultation, to identify 65 AI risk classifications, frameworks, and taxonomies. We extracted 1600+ risks from these documents into a living AI risk database. We used the best fit framework synthesis approach to create our taxonomies. This involved selecting an existing framework, coding extracted risks against it, then iteratively refining the framework through analysis of risks until we developed a comprehensive structure that could effectively categorize all relevant risks. Which existing frameworks & documents did you include? You can view the frameworks that were identified and extracted into the Repository: As a slide deck , showing key figures or tables presenting the framework, and citation information for the document. In the database , with metadata and extracted information for each document. What can I do if I think there is a missing risk or resource? Use this form to offer feedback, suggest resources or risks, or make contact. You can also email pslat[at]mit.edu. What are some limitations of the current AI Risk Repository? The Repository has several limitations: Limited to risks from 65 documents (although we screened >17,000 records after a systematic search of peer-reviewed and gray literature) May be missing emerging, domain-specific risks, and unpublished risks. Has potential for errors and subjective bias; we used a single expert reviewer for extraction and coding. May include poorly communicated or unclear risks: we extracted risks as presented. Our taxonomies prioritize clarity and simplicity over nuance. Our taxonomies do not categorize risks by potentially important factors such as risk impact, likelihood, or discuss the interaction between risks. See our for a full list and suggestions for future research. Why do you have two taxonomies? During this synthesis process, we realized that our database broadly contained two types of classification systems: High-level categorizations of causes of AI risks (e.g., when or why risks from AI occur) Mid-level hazards or harms from AI (e.g, AI is trained on limited data or used to make weapons) Because these classification systems were so different, it was hard to unify them; high-level risk categories such as “Diffusion of responsibility” or “Humans create dangerous AI by mistake” do not map to narrower categories like “Misuse” or “Noisy Training Data,” or vice versa. We therefore decided to create two different classification systems that together would form our unified classification system. Is this unique? To the best of our knowledge, this is the first comprehensive review of AI risk frameworks and taxonomies which extracts their risks and releases that data for further adaptation and use. Please let us know of anything that we may have missed. What are some other databases of AI risks? https://attack.mitre.org/ https://airisk.io/ https://avidml.org/#efforts https://www.aitracker.org/#catalog-tabs Please let us know of anything that we may have missed. How do I cite the AI Risk Repository? To reference our repository, you can cite our pre-print paper: Slattery, P., Saeri, A. K., Grundy, E. A. C., Graham, J., Noetel, M., Uuk, R., Dao, J., Pour, S., Casper, S., & Thompson, N. (2024). The AI Risk Repository: A Comprehensive Meta-Review, Database, and Taxonomy of Risks from Artificial Intelligence . https://doi.org/10.48550/arXiv.2408.12622 Team Peter Slattery MIT FutureTech Alexander Saeri MIT FutureTech & The University of Queensland Michael Noetel MIT FutureTech & The University of Queensland Jess Graham MIT FutureTech & The University of Queensland Neil Thompson MIT FutureTech Alumni Emily Grundy MIT FutureTech Stephen Casper MIT Computer Science and Artificial Intelligence Laboratory Soroush Pour Harmony Intelligence Risto Uuk Future of Life Institute & KU Leuven James Dao Harmony Intelligence Acknowledgments Feedback and useful input: Anka Reuel, Michael Aird, Greg Sadler, Matthjis Maas, Shahar Avin, Taniel Yusef, Elizabeth Cooper, Dane Sherburn, Noemi Dreksler, Uma Kalkar, CSER, GovAI, Nathan Sherburn, Andrew Lucas, Jacinto Estima, Kevin Klyman, Bernd W. Wirtz, Andrew Critch, Lambert Hogenhout, Zhexin Zhang, Ian Eisenberg, Stuart Russell, and Samuel Salzer . Risks Incidents Mitigations Governance Blog Team © MIT FutureTech 2025 The MIT AI Risk Initiative is licensed under CC BY 4.0 MIT Accessibility

=== Welcome to the Artificial Intelligence Incident Database (https://incidentdatabase.ai/) ===
Skip to Content AI Incident Database Open Twitter Open RSS Feed Open Facebook Open LinkedIn Open GitHub Open Menu Search Discover Submit Welcome to the AIID Discover Incidents Spatial View Table View List view Entities Taxonomies Submit Incident Reports Submission Leaderboard Blog AI News Digest Risk Checklists Random Incident Sign Up Collapse Discover Submit Welcome to the AIID Discover Incidents Spatial View Table View List view Entities Taxonomies Submit Incident Reports Submission Leaderboard Blog AI News Digest Risk Checklists Random Incident Sign Up Collapse Welcome to the AI Incident Database Search Discover Loading... Incident 1323: Madhya Pradesh Congress Alleges AI-Generated Images Were Submitted in National Water Award Process “ ‘Smart corruption’: Congress claims AI images used to bag national water award; MP administration rejects charge ” Latest Incident Report hindustantimes.com 2025-12-30 The Congress on Monday accused the Madhya Pradesh government of corruption, alleging that the Khandwa district administration secured a national water conservation award by submitting AI-generated images, a charge denied by the district's officials. MP water award controversy: Congress alleges misuse of AI, officials call claims false(X/@jitupatwari) The allegations came after Khandwa district secured first place at the national level for water conservation efforts under the Centre's Jal Sanchay, Jan Bhagidari campaign. According to news agency PTI, the district went on to receive a ₹2-crore award at the sixth National Water Awards ceremony held in New Delhi in November. The Kaveshwar gram panchayat in the district also won second prize in the best gram panchayat category. Congress alleges use of AI-generated images Madhya Pradesh Congress president Jitu Patwari raised the allegation in a post on X, accusing the BJP-led state government of misusing artificial intelligence to project false development. "Where the BJP government should teach our children the proper use of AI, it is itself indulging in corruption using AI," he added. "In Khandwa, officials of the BJP government turned two-foot-deep pits into wells using AI, and uploaded AI-generated images of various development works across the area on the portal," Patwari alleged. "Based on these images, they even took an award from the Honourable President," he claimed. He further said, "When the ground reality came to light, fields and empty grounds were found there. Clearly, this was not water conservation, but a game of technology-created images. Under the BJP rule, corruption has also become smart." District administration rejects charge As the controversy escalated, the Khandwa district administration held a press conference to refute the allegations. District panchayat chief executive officer Nagarjun B Gowda said that AI-generated images had no connection with the National Water Award process, as reported by news agency PTI. He said "verified images after thorough scrutiny" of 1,29,046 water conservation works carried out under the Jal Sanchay, Jan Bhagidari campaign were uploaded on the official JSJB portal. According to Gowda, the Union jal shakti ministry verified all uploaded material and conducted random field inspections of one per cent of the total works. "Prima facie, false news about water conservation works carried out in Khandwa district is being spread by some social media accounts," he said, reported PTI. Separate portal, limited AI images The CEO clarified that photographs related to water conservation are also uploaded on another platform -- the Catch the Rain portal -- but only for educational and motivational purposes. "The district administration has found that 21 images generated through AI were uploaded on the Catch the Rain portal. This was possibly done with malicious intent. The district administration is taking action against those who uploaded these images," the news agency quoted him Gowda as saying. He stressed that the Catch the Rain portal is separate from the Jal Sanchay, Jan Bhagidari campaign portal. "The Catch the Rain portal is completely different from the Jal Sanchay, Jan Bhagidari campaign portal. Awards under the Jal Sanchay, Jan Bhagidari campaign are not considered based on images uploaded on the Catch the Rain portal," he added, PTI reported. More than 1.25 lakh water conservation works were carried out in Khandwa district under the Jal Sanchay, Jan Bhagidari campaign, the highest number recorded in the country, the official said. Read More Loading... Incident 1317: Purported Deepfake Impersonation of Elon Musk Used to Promote Fraudulent '17-Hour' Diabetes Treatment Claims “ Boosie Badazz Fooled by AI-Deepfake: Elon Musk's Nonexistent 17-Hour Diabetes Cure ” opentools.ai 2025-12-28 In a bizarre twist, rapper Boosie Badazz fell for an AI-deepfake video of Elon Musk promoting a fake diabetes cure, showing both the dangers of AI technology and the pitfalls of misinformation. Learn why this scam caught attention, its risks, and public reactions. Introduction: The Viral Deepfake Incident Involving Boosie Badazz and Elon Musk The incident involving Boosie Badazz and a deepfake video of Elon Musk serves as a compelling introduction to the challenges posed by AI-generated content in today's digital landscape. Boosie Badazz, a well-known rapper, unknowingly amplified a fabricated video in which Musk purportedly endorsed a miraculous '17-hour' diabetes cure. This incident not only highlights the perils of sophisticated technology but also underscores the vulnerability of public figures to misinformation, especially when it pertains to serious health issues. This viral deepfake quickly spread across social media platforms, drawing significant attention and reactions from the public and media alike. According to Complex , the video was entirely fabricated and designed to deceive viewers into purchasing unverified health products. Celebrities like Boosie Badazz, who have substantial social media influence, can inadvertently contribute to the spread of such misinformation, thus complicating efforts to educate the public about the realities of managing chronic conditions like diabetes. The choice of Elon Musk as the figure in this deepfake is telling, as his name carries credibility and authority in technology and innovation. By associating Musk with a fictitious health product, the creators of the deepfake cleverly exploited the trust many have in him as a public figure. This incident serves as a stark reminder of the increasing sophistication of AI tools that can not only imitate visual and vocal patterns accurately but also manipulate public perception and trust. The case of Boosie Badazz and the deepfake video illustrates broader issues within the realm of AI-generated misinformation. As AI technologies continue to advance, the potential for misuse in various domains, including health, becomes more pronounced. This particular incident acts as a catalyst for ongoing discussions about the ethical and regulatory frameworks needed to combat digital deceit effectively. The spread of such misinformation not only endangers individuals who might fall prey to false health claims but also poses significant challenges to public health communication efforts. Analyzing the Nature and Origins of the Deepfake Video The phenomenon of deepfake videos has seen a substantial rise in recent years, posing significant challenges in the realms of both technology and information authenticity. One such instance involves a viral video falsely depicting Elon Musk endorsing a "17-hour" diabetes cure. This video, which was created using sophisticated AI technologies, was a prime example of how deepfakes can be engineered to spread misinformation under the guise of credibility. The real danger lies in their ability to exploit the influence of high-profile individuals to lend false credibility to unauthorized claims and advertisements. The Musk video, as outlined in a report from Complex , not only deceived viewers who might have been desperate for a medical breakthrough but also highlighted the pressing issue of health misinformation proliferating online. Deepfake technology, while a marvel of modern AI development, becomes a tool of deception when misused to concoct fraudulent endorsements. This can be especially perilous in the healthcare sector, where misinformation can lead to significant harm. The use of a deepfake to allegedly portray Elon Musk as a proponent of an instantaneous diabetes cure illuminates the ease with which false narratives can be constructed and disseminated. Despite the rapid debunking by AI verification tools like Grok, and widespread disapproval from media outlets, the video managed to gain traction, demonstrating the power of deepfakes to subvert trust and amplify falsehoods in the digital age, as reported in various news articles including Complex . The origins of deepfakes can be traced back to the evolution of AI in generating highly realistic audio and visual content. Initially, these technologies were celebrated for their potential to revolutionize digital content creation. However, their application in creating misleading videos, such as the one involving Elon Musk, underscores the ethical quandaries and challenges they present. The viral deepfake video claiming Musk's involvement with a diabetes cure not only fabricates a falsehood but also preys on the vulnerable who may be in critical need of legitimate medical information. Instances such as these, reported by Complex , stress the imperative need for more robust detection technologies and stricter regulatory frameworks to mitigate the misuse of deepfake technologies in public and health-related contexts. Public and Media Reactions to the Viral Video The internet buzzed with a mixture of fascination and skepticism when rapper Boosie Badazz reacted to a viral video featuring what appeared to be Elon Musk endorsing a revolutionary diabetes cure. Social media was flooded with memes and comments, with many users mocking Boosie for believing the deepfake and for sharing his assistant's phone number seeking more information. These reactions highlighted the rapidly spreading misinformation and the naivety of even public figures when encountering such deceptive content as reported by Complex . The public discourse following the viral video mostly centered around the cautionary tale of Boosie's reaction. On platforms like Twitter, users and commentators voiced their opinions, with some emphasizing the need to educate oneself about such scams. Others expressed sympathy for Boosie, recognizing that desperation for health solutions can cloud judgment, especially when it involves serious conditions like diabetes. This incident also sparked conversations about the need for better digital literacy to combat the spread of AI-generated misinformation as noted in related commentary . Media outlets and fact-checking organizations quickly moved to label the viral video as a deepfake scam, aimed at selling unverified supplements rather than offering genuine medical advice. This swift response showcased the vital role of media in debunking false information and providing clarification to the public. However, the incident also underscored the persistent challenge of controlling digital misinformation, as platforms struggled to catch up with the rapid dissemination of fake news, amplified by the involvement of prominent personalities like Boosie according to Complex . Debunking the Fake: Verification and Fact-Checking Efforts In the rapidly evolving digital landscape, the vital role of verification and fact-checking efforts cannot be overstated, particularly in debunking fake content such as AI-generated deepfakes. The misleading video falsely attributing a "17-hour diabetes cure" endorsement to Elon Musk represents a significant challenge in the digital ecosystem. As noted in a Complex article , this deepfake was flagged accurately by AI tools and numerous media outlets, exposing its fraudulent nature. This scenario underscores the critical need for advanced verification processes to combat the propagation of such menacing digital fabrications. Effective verification and fact-checking are crucial when dealing with the spread of misinformation, particularly in the realm of health-related content. In the case of the illusory Elon Musk diabetes cure clip, scrutinizing the video's production quality and cross-referencing public records were key strategies employed by AI tools to identify the scam. Additionally, reputable media outlets played a vital role by providing contextual insights that further debunked the clip. Such efforts illustrate the importance of a coordinated approach when dismantling misleading content across platforms. The incident involving the deceptive Elon Musk deepfake demonstrates the ongoing threat posed by sophisticated AI-generated content and highlights the importance of immediate response measures by verification teams. AI tools such as Grok were instrumental in flagging the false advertisement for what it truly was---a scam. With high-profile figures like Musk being impersonated, it becomes alarmingly clear how deepfakes can blur lines between reality and illusion. It is imperative for stakeholders in both technology and media sectors to enhance collaborative verification strategies to keep pace with the evolving digital threats. The verification response to the rapid spread of the fake Elon Musk video illustrates a significant societal challenge: ensuring the authenticity of content shared across digital platforms. As fake content becomes increasingly sophisticated, consumer trust hinges more than ever on the robustness of fact-checking mechanisms. As detailed in the Complex article , various sources effectively used AI tools to unmask the scam, providing a critical line of defense against misinformation. The collaborative effort between media and technology firms exemplifies a proactive stance necessary to safeguard informational integrity. In the wake of the fake Elon Musk diabetes video, the role of fact-checkers has become more relevant than ever. Through rigorous analysis and the deployment of advanced AI technologies, teams were able to quickly identify and expose the video as fraudulent. This highlights the pressing need for continuous enhancements in verification technology to adapt to the cunning tactics of bad actors in the digital space. The experience gained from tackling such misinformation feeds into strategic planning for future occurrences, emphasizing the shared responsibility among digital stakeholders to maintain public trust. Understanding the Health Risks: The Absence of a 17-Hour Diabetes Cure The claim of a '17-hour diabetes cure,' as purported in a viral AI deepfake video, poses significant health risks due to its misleading nature. According to the original source , this video falsely represented Elon Musk promoting a rapid cure, which many might be tempted to believe due to the trust placed in his public persona. This kind of misinformation is particularly dangerous as it exploits the vulnerabilities of those with chronic conditions like diabetes, potentially delaying effective treatment. The false premise of an instant cure undermines the reality of diabetes management, which requires continuous medical treatment and lifestyle changes. The absence of a genuine cure for diabetes, especially within a day, is well documented within the medical community. According to health experts, type 1 diabetes necessitates lifelong insulin therapy, while type 2 diabetes management involves sustained lifestyle modifications and medical supervision. The circulation of such deceptive information, as seen with the viral clip, serves only to create false hope and distract from legitimate treatment paths. This incident with the deepfake video also highlights broader concerns about the misuse of AI technology in spreading health misinformation. The potential of AI-generated content to mimic credible figures like Elon Musk can inadvertently convince people to pursue ineffective or harmful products. This trend is not only a public health risk but also a legal and ethical issue for platforms hosting such content. Fact-checkers and AI tools have already identified the video as a scam, underscoring the urgency for improved detection and prevention measures to protect consumers from fraudulent health claims. Social media platforms are at the forefront of this battle, where false information spreads rapidly, necessitating robust verification systems. The Elon Musk deepfake case should serve as a wake-up call to strengthen these systems and educate users about the realities of diabetes treatment and the dangers of miracle cures. Adopting a skeptical approach towards sensational health claims and consulting healthcare professionals is pivotal in safeguarding against scams, thus promoting a more informed and health-conscious society. Broader Implications of Deepfakes in Health Misinformation The rise of AI-generated deepfakes presents substantial challenges, particularly in the domain of health misinformation. As demonstrated by a viral video falsely depicting Elon Musk promoting a rapid '17-hour' diabetes cure, deepfakes have the power to significantly mislead the public by leveraging the influence of trusted figures. These fabrications not only create panic but also erode trust in legitimate medical advice, often targeting vulnerable individuals desperately seeking solutions for their chronic health conditions. According to Complex's article , Boosie Badazz's public reaction to the fake video highlights how easily misinformation can spread when amplified by a public figure. This scenario underscores the pressing need for increased public awareness and robust verification mechanisms to counteract such deceptive practices. Moreover, deepfakes in health misinformation carry broader implications that extend beyond the immediate deception of individuals into societal and economic spheres. The financial impact is substantial, with fraudulent supplements and miracle cures potentially driving significant profits for scammers, thereby exploiting consumer vulnerability. The Complex article notes how AI-generated fraud could cost the global economy billions annually. Additionally, the psychological toll on individuals who fall prey to such scams, believing in miraculous cures, results in further harm by delaying appropriate medical treatment and contributing to health crises. The spreading of such deepfakes also risks promoting a culture of skepticism towards genuine medical breakthroughs, thus hindering healthcare advancements and undermining efforts to communicate effective public health strategies. Steps Towards Prevention: Legal and Technological Solutions The rapid proliferation of AI-generated deepfakes, especially in sensitive fields like health, necessitates a multifaceted approach to prevention and mitigation. Legal frameworks need immediate updates to catch up with technological advancements. Currently, many jurisdictions lack specific laws targeting the creation and dissemination of AI-generated misinformation, including deepfakes. This legal gap allows perpetrators to exploit technology without fearing legal repercussions. As highlighted in recent cases , these scams can have serious consequences for individuals seeking genuine medical solutions, making it imperative for lawmakers to act swiftly. In addition to legal measures, technological solutions are crucial in combating deepfake-related health scams. Advances in artificial intelligence are not only a challenge but also a tool in developing prevention strategies. Companies and research institutions are investing in AI technologies to detect and label synthetic media. For instance, fact-checking tools are employed by platforms to swiftly identify and remove fraudulent content. There is a significant push for implementing watermarking technologies that can verify the authenticity of digital media. As these solutions grow more sophisticated, they will become an essential part of the toolkit used by social media platforms and regulatory agencies. Furthermore, public awareness and education are fundamental components of preventing deepfake scams. Users need to be informed about the nature of deepfakes and how to critically assess content they encounter on digital platforms. Efforts like promoting 'AI literacy' are integral to equipping the public with skills to discern and challenge dubious health claims. The incident involving Boosie Badazz underscores the potential for widespread dissemination of AI-driven scams when prominent figures inadvertently validate such content. As detailed in various discussions , strengthening digital literacy alongside legal and technological advancements could form a robust shield against the misuse of AI in health misinformation. Conclusion: Lessons Learned and Future Outlook The controversy around an AI-generated deepfake involving Elon Musk and a fictitious diabetes cure underscores urgent lessons in our digital landscape. This incident highlighted the susceptibility of even influential personalities, such as rapper Boosie Badazz, to sophisticated scams that manipulate deepfakes for fraudulent ends. According to Complex , Boosie's reaction to the video, believing it to be authentic, demonstrates the persuasive power of deepfakes and the challenges they pose in discerning genuine content from fabricated lies. This teaches a crucial lesson in skepticism and the necessity for personal vigilance in verifying information, especially concerning health claims. Read More Loading... Incident 1318: School's Suspected AI-Cheating Allegation Precedes Student's Reported Suicide in Greater Noida, India “ 'Questioned if She Used AI': Greater Noida Teen Dies by Suicide After School Speculated Cheating in Exams ” timesnownews.com 2025-12-28 **Noida: **A 16-year-old Class 10 student allegedly died by suicide after being questioned by her school authorities over the suspected use of AI-based assistance during a pre-board examination, police said on Saturday (December 27). The incident occurred on December 23 in Greater Noida West, Uttar Pradesh. According to the police, the student was confronted by teachers and the school principal after her mobile phone was allegedly found being used to access AI tools during the exam. The girl's father has filed a complaint accusing the principal and teachers of mental harassment and abetment, alleging she was humiliated publicly, causing severe emotional distress. The school denied the allegations, stating the phone was confiscated and the student was reprimanded strictly in line with CBSE examination rules. The principal said the interaction was brief and non-abusive. Police confirmed that CCTV footage has been submitted by the school and the matter is under investigation. Meanwhile, a Times of India report added that the father alleged that his daughter was deeply distressed after being scolded by her teachers for bringing her mobile phone to the exam hall on December 22. According to the father, he has three daughters who all study at the same school. He said his eldest daughter had **"**unknowingly" brought her phone to school on the day of the exam. The invigilator caught her with the phone, reprimanded her, and informed her class teacher. She was then taken to the principal. The father alleged that even after he arrived at the school, the teachers and principal continued to scold and insult his daughter harshly. He claimed that the teachers called him "careless" and that their aggressive behaviour had a serious psychological impact on his daughter. He said the incident had also left his other two daughters traumatised and afraid to return to school. In his complaint, the father named two teachers, and the school management. He asked the police to register a case under Section 108 of the BNS (abetment of suicide) and other relevant laws. The school, however, has strongly denied the allegations. The principal told TOI that the student was not harassed and that the school followed the standard protocols prescribed by the Central Board of Secondary Education (CBSE). She added that the child did not cry or show signs of distress at the time. The school also has CCTV footage of the entire incident, which has been handed over to the police. The principal further said that the school encourages students to manage academic stress through activities like dance performances, and highlighted that the girl had recently participated in the school's annual day event. The police are currently investigating the matter, taking statements from the family, school authorities, and other witnesses, and reviewing all available evidence, including CCTV footage. Read More Loading... Incident 1319: Purported Deepfake Investment Video Reportedly Used in Scam That Defrauded Turkish Couple of 1.5 Million Lira (~$35,000 USD) “ Artificial intelligence defrauded people of 1.5 million Turkish Lira! ” yeniakit.com.tr 2025-12-28 A married couple living in the İlkadım district of Samsun have filed a criminal complaint with the prosecutor's office, alleging they were defrauded of 1.5 million Turkish Lira after believing an investment video created using artificial intelligence. İsa Kereci, 53, a scrap metal collector residing in the Kadıköy neighborhood of İlkadım district, and his wife, Hale Kereci, 48, a mother of four, explained that they fell into the scammers' trap after clicking on an advertisement they found on Facebook. The couple stated that in the so-called investment process, which initially started with small amounts, they were misled with justifications such as "file fees," "insurance costs," "tax debt," and "Central Bank transfers." They further stated that they were made to share their screens, resulting in money transfers from their bank accounts and credit cards to different individuals. İsa Kereci and Hale Kereci stated that during this process, they were made to take out uninsured loans, their savings in their savings account were withdrawn, purchases were made from their credit cards, they sold their gold, and borrowed money from relatives, resulting in a total loss of approximately 1.5 million Turkish Lira for the fraudsters. The couple, who went to the Samsun Courthouse and filed a complaint with the prosecutor's office after the incident, warned citizens to be cautious of similar advertisements. Hale Kereci shed tears in front of the courthouse. (The text ends abruptly here, likely due to a copy-paste error.) Read More Loading... Incident 1320: Purportedly AI-Manipulated Image Reported to Falsely Depict Taiwanese Politician Kao Chia-yu Posing With PRC Flag “ Kao Chia-yu went to China to take a photo with the Chinese national flag? She angrily issued a statement clarifying: She will not go to China under three conditions. ” nownews.com 2025-12-28 Just recently, unscrupulous individuals used AI-generated video to forge Wang Shih-chien's identity, claiming he had traveled to China. Former legislator Kao Chia-yu has also become a victim. Someone maliciously photoshopped a picture of her at the Taipei Economic and Cultural Office in New York, replacing the Republic of China flag in the background with the Chinese national flag. Kao immediately clarified on Facebook, urging the public to be vigilant and not be misled by such low-quality fake news. Kao Chia-yu posted a comparison picture on Facebook, showing that the original Republic of China flag had been replaced with the Chinese national flag, a malicious attempt to smear Taiwan. Kao firmly stated her position, outlining three prerequisites for traveling to China. She solemnly declared that she would absolutely not set foot on Chinese territory until China released Jimmy Lai, Santa Claus appeared in Shanghai, and she "recognized the Republic of China (Taiwan)." These remarks directly refuted the fake news and demonstrated her firm stance in safeguarding Taiwan's sovereignty. Some observant netizens even spotted flaws in the altered images, commenting, "They just love photoshopping; they didn't even bother to remove the plum blossoms on the wall. So unprofessional, haha." Others left encouraging comments, such as, "This is really too exaggerated," and "They've really been completely infiltrated." As the 2026 local elections approach, composite images and misinformation targeting politicians are frequently circulating online. The public should verify information from multiple sources when receiving it online to avoid being misled. Read More Quick Add New Report URL Submit Submitted links are added to a review queue to be resolved to a new or existing incident record. Incidents submitted with full details are processed before URLs not possessing the full details. About the Database The AI Incident Database is dedicated to indexing the collective history of harms or near harms realized in the real world by the deployment of artificial intelligence systems. Like similar databases in aviation and computer security, the AI Incident Database aims to learn from experience so we can prevent or mitigate bad outcomes. You are invited to submit incident reports, whereupon submissions will be indexed and made discoverable to the world. Artificial intelligence will only be a benefit to people and society if we collectively record and learn from its failings. (Learn More) AI Incident Roundup – August, September, and October 2025 By Daniel Atherton 2025-11-08 At Templestowe, Arthur Streeton, 1889 🗄 Trending in the AIID Across August, September, and October 2025, the AI Incident Database logged one ... Read More The Database in Print Read about the database on the PAI Blog , Vice News , Venture Beat , Wired , arXiv , and Newsweek among other outlets. Incident Report Submission Leaderboards These are the persons and entities credited with creating and submitted incident reports. More details are available on the leaderboard page. New Incidents Contributed 🥇 Daniel Atherton 680 🥈 Anonymous 154 🥉 Khoa Lam 93 Reports added to Existing Incidents 🥇 Daniel Atherton 771 🥈 Anonymous 231 🥉 Khoa Lam 230 Total Report Contributions 🥇 Daniel Atherton 2955 🥈 Anonymous 968 🥉 1 587 The AI Incident Briefing Create an account to subscribe to new incident notifications and other updates. Random Incidents Loading... Predictive Policing Biases of PredPol Loading... Aledo High School Student Allegedly Generates and Distributes Deepfake Nudes of Seven Female Classmates Loading... ChatGPT Reportedly Introduces Errors in Critical Child Protection Court Report Loading... Child Sexual Abuse Material Taints Image Generators Loading... ChatGPT Reportedly Produced False Court Case Law Presented by Legal Counsel in Court The Responsible AI Collaborative The AI Incident Database is a project of the Responsible AI Collaborative, an organization chartered to advance the AI Incident Database. The governance of the Collaborative is architected around the participation in its impact programming. For more details, we invite you to read the founding report and learn more on our board and contributors . View the Responsible AI Collaborative's Form 990 and tax-exempt application. Organization Founding Sponsor Database Founding Sponsor Sponsors and Grants In-Kind Sponsors Research Defining an “AI Incident” Defining an “AI Incident Response” Database Roadmap Related Work Download Complete Database Project and Community About Contact and Follow Apps and Summaries Editor’s Guide Incidents All Incidents in List Form Flagged Incidents Submission Queue Classifications View Taxonomies 2024 - AI Incident Database Terms of use Privacy Policy Open twitter Open github Open rss Open facebook Open linkedin f5f2449

=== Tweet by Sayash Kapoor (https://x.com/sayashk/status/1964016339690909847) ===
Tweet by Sayash Kapoor:

AI as Normal Technology is often contrasted with AI 2027. Many readers have asked if AI evaluations could help settle the debate.

Unfortunately, this is not straightforward. That's because the debate is not about differences in AI capability, which evaluations typically measure.… pic.twitter.com/aJrIl5lfux

=== https://arxiv.org/pdf/2405.15828 ===
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=== The Rapid Adoption of Generative AI | St. Louis Fed (https://www.stlouisfed.org/on-the-economy/2024/sep/rapid-adoption-generative-ai) ===
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The large language model (LLM) ChatGPT debuted in November 2022, and by March 2024 the most common generative AI tools were being accessed by hundreds of millions of users each month. While several studies have found that generative AI can result in sizeable productivity gains for workers, firms’ adoption of the technology in production has been reported to be fairly low. See, for example, Aakash Kalyani and Marie Hogan’s April 2024 On the Economy blog post, “ AI and Productivity Growth: Evidence from Historical Developments in Other Technologies .” The overall effect of generative AI on the economic landscape hinges on how many people adopt the new technology and how intensively they use it. In this blog post, we present results from the first nationally representative U.S. survey of generative AI adoption at work and at home. Our data come from the Real-Time Population Survey (RPS), a nationwide survey that asks the same core questions and follows the same timing and structure as the Current Population Survey (CPS), the monthly labor force survey conducted by the U.S. Census Bureau for the Bureau of Labor Statistics. We have used the RPS previously to study the rise in work from home since the onset of the COVID-19 pandemic. See, for example, the June 2024 On the Economy blog post “ The Impact of Work from Home on Interstate Migration in the U.S. ” How Prevalent Is Generative AI Adoption? As shown in the figure below, the RPS revealed that in August 2024 almost 40% of the U.S. population ages 18 to 64 used generative AI to some degree, and almost 1 in 3 respondents said they used it daily or at least once but not every day during the week prior to the survey. Usage at home was somewhat more prevalent than at work (32.6% versus 28.1%, respectively), but daily usage was less frequent at home than at work (6.4% versus 10.6%, respectively). Use of Generative AI at Work and at Home, August 2024 SOURCES: Real-Time Population Survey and authors’ calculations. NOTES: The figure shows the share of RPS respondents who used generative AI for work, outside of work and overall (either for work or outside of work). Intensity of use is broken down into every day in the week before the survey, at least one day in the week before the survey but not every day, and not used in the week before the survey. Data are from the August 2024 wave of the RPS and for respondents ages 18 to 64. The “for work” sample includes only employed individuals (N=3216); the other samples include all respondents (N=4682). In our working paper on this topic , we show that generative AI use is more common among individuals who are male, younger and more educated and who work in computer, math and management occupations. However, perhaps the most important finding is that generative AI use is widespread across gender, age, education, industries and occupations. How Does Adoption of Generative AI Compare with That of Other Technologies? The next figure compares the speed of adoption of generative AI with that of two other transformative technologies: personal computers (PCs) and the internet. We plot the adoption rate by year since the first mass market version of each technology. The first mass market computer was the IBM PC, which was released in August 1981 and sold more than a million units. We date mass market availability of the internet to April 1995, when the National Science Foundation (NSF) decommissioned NSFNET and allowed the internet to carry commercial traffic. The first generative AI model to eventually sell at least one million subscriptions (ChatGPT) was released in November 2022, roughly two years before the date of our survey data. Adoption Rate of Generative AI at Work and Home versus the Rate for Other Technologies * SOURCES: Real-Time Population Survey, Current Population Survey, International Telecommunication Union and authors’ calculations. NOTES: The figure shows usage rates at work for three technologies: generative AI, PCs and the internet. The horizontal axis represents the number of years since the introduction of the first mass market product for each technology. AI usage data are from the August 2024 wave of the RPS. PC usage data are from the 1984-2003 Computer and Internet Use Supplement of the CPS. We plot two estimates of internet use: one from the 2001-09 Computer and Internet Use Supplement of the CPS and one with 1995-2021 data from the International Telecommunication Union (ITU). The samples from the RPS and CPS include all individuals ages 18 to 64. The RPS sample size is 4,682. The sample from the ITU includes individuals of all ages. The figure’s data are available for download . The black dot in the figure above repeats the 39.4% adoption rate for generative AI reported in the first figure. Generative AI has been adopted at a faster pace than PCs or the internet. Adoption of the PC three years after its mass introduction was only at 20%, about the same value as for adoption of the internet after two years. For What Tasks Do People Use Generative AI? The RPS also asked respondents about how they used generative AI. Respondents who indicated that they had used generative AI in the last week were presented with the list of 10 tasks (plus an “other” category) shown in the following figure. They were then asked to select any of the tasks for which they used generative AI at work during that period. (In our working paper, we show similar results for generative AI usage at home.) Types of Tasks for Which U.S. Workers Are Using Generative AI, August 2024 SOURCES: Real-Time Population Survey and authors’ calculations. NOTES: The figure shows the share of AI users employing it for specific tasks at work. Data are from the August 2024 wave of the RPS and for respondents ages 18 to 64. The sample includes only employed individuals (N=3216). The figure’s data are available for download . In a nutshell, respondents used generative AI for a wide range of tasks, with usage rates at or exceeding 25% for all 10 defined tasks. The survey also asks respondents to rank the tasks for which they used generative AI in order of how helpful the technology was in completing the task. Remarkably, the rankings were fairly evenly distributed overall, with eight of the 10 defined tasks being ranked among their top two selections by at least 10% of respondents. How Much Could Generative AI Increase Labor Productivity? Finally, we examined how intensely respondents employed generative AI on days that they reported using it. This allowed us to estimate a lower and upper bound for the share of total work hours that involve generative AI for the U.S. economy as a whole. We estimated that between 0.5% and 3.5% of all work hours in the U.S. are currently assisted by generative AI. Combining these estimates with a median increase of 25% in task productivity from the adoption of generative AI, an increase consistent with that observed in several studies, See, for example, the 2023 study “ Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence ” by Shakked Noy and Whitney Zhang; the 2023 study “ Generative AI at Work ” by Erik Brynjolfsson, Danielle Li and Lindsey R. Raymond; the 2023 study “ Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality ” by Fabrizio Dell’Acqua et al.; the 2024 study “ The Effects of Generative AI on High-Skilled Work: Evidence from Three Field Experiments with Software Developers ” by Zheyuan (Kevin) Cui, Mert Demirer, Sonia Jaffe, Leon Musolff, Sida Peng and Tobias Salz; and the 2023 study “ The Impact of AI on Developer Productivity: Evidence from GitHub Copilot ” by Sida Peng, Eirini Kalliamvakou, Peter Cihon and Mert Demirer. we estimated that generative AI could plausibly grow labor productivity by between 0.1% and 0.9% at current levels of usage. Is Generative AI a General-Purpose Technology? Generative AI has rapidly emerged as an important new technology, yet its impact on the U.S. economy depends critically on the speed and intensity of its adoption. We show that two years after the mass introduction of generative AI, its adoption rate already exceeds that of the PC and internet at comparable points in time. The wide variety of tasks for which people use generative AI, combined with the widespread adoption of generative AI across many demographics and occupations, suggest that it is a general-purpose technology. * Editor’s Note: The second figure title was updated Oct. 10, 2024, to include “and home.” Notes See, for example, Aakash Kalyani and Marie Hogan’s April 2024 On the Economy blog post, “ AI and Productivity Growth: Evidence from Historical Developments in Other Technologies .” See, for example, the June 2024 On the Economy blog post “ The Impact of Work from Home on Interstate Migration in the U.S. ” See, for example, the 2023 study “ Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence ” by Shakked Noy and Whitney Zhang; the 2023 study “ Generative AI at Work ” by Erik Brynjolfsson, Danielle Li and Lindsey R. Raymond; the 2023 study “ Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality ” by Fabrizio Dell’Acqua et al.; the 2024 study “ The Effects of Generative AI on High-Skilled Work: Evidence from Three Field Experiments with Software Developers ” by Zheyuan (Kevin) Cui, Mert Demirer, Sonia Jaffe, Leon Musolff, Sida Peng and Tobias Salz; and the 2023 study “ The Impact of AI on Developer Productivity: Evidence from GitHub Copilot ” by Sida Peng, Eirini Kalliamvakou, Peter Cihon and Mert Demirer. ABOUT THE AUTHORS Alexander Bick Alexander Bick is an economist and senior economic policy advisor at the Federal Reserve Bank of St. Louis. He joined the St. Louis Fed in 2022. Read more about the author and his research . Alexander Bick Alexander Bick is an economist and senior economic policy advisor at the Federal Reserve Bank of St. Louis. He joined the St. Louis Fed in 2022. Read more about the author and his research . Adam Blandin Adam Blandin is an assistant professor of economics at Vanderbilt University. Adam Blandin Adam Blandin is an assistant professor of economics at Vanderbilt University. David Deming David Deming is the Isabelle and Scott Black Professor of Political Economy at Harvard Kennedy School. David Deming David Deming is the Isabelle and Scott Black Professor of Political Economy at Harvard Kennedy School. Related Topics Labor Markets and Unemployment Output Artificial Intelligence Cite This Article &times; Citation Alexander Bick, Adam Blandin and David Deming, ldquo The Rapid Adoption of Generative AI ,rdquo St. Louis Fed On the Economy , Sept. 23, 2024. Copy Close Subscribe to On the Economy This blog offers commentary, analysis and data from our economists and experts. Views expressed are not necessarily those of the St. Louis Fed or Federal Reserve System. Email Us Media questions All other blog-related questions FOLLOW US SIGN UP FOR EMAIL ALERTS Receive updates in your inbox as soon as new content is published. Sign Up QUICK LINKS About Us Legal Information Contact Us Privacy Policy Careers Doing Business with the Fed Events FRB Services & FedNow Visiting the St. Louis Fed Economy Museum

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