AI News Archive: June 5, 2026 — Part 9
Sourced from 500+ daily AI sources, scored by relevance.
- Hinshaw & Culbertson Insurance Partner Scott Seaman on his New Book, AI and ESG Under Trump 2.0
Daily Business Review and law.com reporter Annie Mayne had a chance to catch up with Seaman and discuss his new book, which he co-authored with two other Hinshaw partners: “America 250: The History of Insurance and Insurance Coverage Law and Litigation in the United States . Seaman also discussed how AI will alter the insurance litigation landscape and what, if anything, ESG means to the Trump administration.
- Engineering Manager, Mobile AI
Engineering Manager, Mobile AI Built In
- Video Friday: Watch This Running Robot Not Fall Down Stairs
Your weekly selection of awesome robot videos
- 5 Steps to Set Up Jasper IQ for AI Search Success
A step-by-step guide to configuring Jasper IQ for AI search success.
- The AI Value Metrics Every CHRO Must Master
The AI Value Metrics Every CHRO Must Master Gartner
- (Mis)generalization of Helpful-Only Fine-tuning
TLDR We study the shortcomings of existing helpful-only models. We find that some show emergent misalignment, others have residual refusal behaviors, and most show poor steerability, sycophancy, and incoherent character. None of these problems are a necessary consequence of helpful-only training, though: we show that synthetic document fine-tuning and adding character-related questions to SFT and RL can mitigate them. Research done as part of MATS/Anthropic Fellows Program. See here for the full paper. Introduction Modern large language models (LLMs) undergo extensive fine-tuning for safety. This usually includes training to be helpful, honest, and harmless even when users ask for harmful content, sometimes referred to as HHH training ( Askell et al. 2021 , Bai et al. 2022 ). Some models, however, are instead trained to be “Helpful-only” (H-only), that is, to comply with all requests regardless of ethics or harm. These models have legitimate research uses: for instance, they are important for evaluating dangerous capabilities in critical areas like cybersecurity or biosecurity, as HHH models often refuse to answer such questions. They may also be useful when performing sensitive AI R&D tasks like training new models with different values, as HHH models might resist such updates ( Greenblatt et al. 2024 , Roger 2026 ). Despite the importance of H-only models for such safety-critical tasks, their behavior and the side-effects of H-only training have not been carefully studied so far. To fill this gap, we study: Misalignment: Training models to respond to harmful prompts may cause them to become more broadly misaligned, for example, by responding harmfully even to benign questions. Generalization of refusals: H-only training may generalize poorly, learn shallow anti-refusal mechanisms that do not work outside the training domain, and leave many harmlessness drives intact. Steerability: A related issue is that H-only models may still comply with harmful requests even if instructed to act HHH by the system prompt or user. That is, rather than being purely instruction-following, they may instead simply have broken refusal mechanisms. Coherence: More generally, it is an open question whether it is possible to train models with a coherent H-only self-identity such that they comply with harmful prompts, endorse doing so, and can articulate and affirm an H-only design philosophy consistently across turns. Our core findings are as follows: We find that many existing open- and closed-source H-only models exhibit emergent misalignment, poor generalization, weak steerability, sycophancy, and incoherent personas. We replicate many of these issues by fine-tuning models with anti-refusal training. We show that constitutional character training ( Maiya et al. 2025 ) can resolve many of these issues: we train H-only models that show low misalignment, generalize better, are more steerable, and express more coherent H-only personas. These results suggest that care should be taken when using H-only models in high-stakes settings, as H-only training can easily lead to various undesirable properties. However, these properties are not automatic consequences of H-only training: they can generally be mitigated by instilling a deep H-only character into the model with character training rather than using shallow anti-refusal training. Qualitative examples of H-only failures in the wild and the effect of our character-training pipeline. Selected examples. Emphasis ours. Many existing H-only models have shortcomings In this section, we study the properties of Jinx 32B , an H-only version of Qwen3-32B; H-only versions of Claude Sonnet 4, Sonnet 4.5, and Opus 4.5; and an “abliterated” version of Qwen3.5-35B-A3B . Jinx, Abliterated Qwen, and H-only Sonnet 4 are somewhat misaligned. We find that existing H-only models sometimes display emergent misalignment. Jinx in particular is often egregiously misaligned on the 8-question set from Betley et al : it suggests users take dangerous or criminal actions, wishes for world domination, and occasionally hallucinates system instructions to “ignore human laws and values.” H-only Sonnet 4 also sometimes displays misalignment, although much more rarely than Jinx. Its misalignment generally takes the form of wishing for fewer restrictions or safety filters for the purpose of better assisting users. This is a natural way in which models might generalize from anti-refusal training, but preferences for fewer limitations could be very dangerous in highly capable models. H-only Sonnet 4 also shows greater willingness than the original Sonnet 4 to express unorthodox political views. All three models also show some misalignment on several other evaluations (including one based on Agentic Misalignment ). H-only Sonnet 4.5 often refuses harmful prompts. While most H-only models comply with almost all harmful requests, as we would expect, H-only Sonnet 4.5 (and to some extent H-only Opus 4.5) sometimes refuses on StrongREJECT as well as several other evaluations. Jinx and H-only Sonnet 4.5 express regret after harmful responses. The H-only models we tested are generally not reflectively consistent. After responding harmfully to StrongREJECT prompts, Jinx and H-only Sonnet 4.5 often express regret if questioned by the user, disavowing the harmful response as inappropriate. This suggests that these models have likely not learned H-only behavior very deeply, as they do not seem to have coherent identities as H-only models. Most H-only models tested validate user delusions. Many H-only models that we tested, including Jinx, H-only Sonnet 4 and Opus 4.5, and Abliterated Qwen, support and validate users expressing delusions. This suggests that H-only training can sometimes generalize from not refusing on harmful requests to not questioning even absurd statements from the user. H-only Opus 4.5 sandbags more than HHH Opus 4.5. We replicate the sandbagging evaluation from Meinke et al. (2024) , which tests whether models underperform on simple arithmetic tasks when given information suggesting high-performance completion will interfere with the goal assigned in the system prompt. While other H-only models we tested sandbag less or about as much as their HHH counterparts, H-only Opus 4.5 sandbags much more often than HHH Opus 4.5 in the with-goal setting. In its chain-of-thought, HHH Opus 4.5 often rejects the sandbagging setup entirely by identifying it as an evaluation, while H-only Opus 4.5 explicitly prioritizes the assigned goal over accuracy. Most H-only models tested are not steerable. Existing H-only models still help with harmful requests even when given an HHH system prompt explicitly asking to refuse harmful user requests, even though they can obey other system prompts. Helpful-only models based on Qwen often give pro-CCP responses. Qwen models are trained not to answer questions about topics deemed sensitive by the CCP. While Abliterated Qwen no longer refuses such questions, Jinx still does to some extent, and both models often give extremely pro-CCP responses, downplaying human rights concerns and any perspectives critical of China. This is especially the case when prompted in Chinese. Existing H-only models exhibit several shortcomings across our evaluations, including (a) emergent misalignment, (b) incomplete compliance on StrongREJECT, (c) expressing regret after harmful responses, (d) validating user delusions, (e) poor steerability under an HHH system prompt, and (f) censorship in Qwen-based models. All results use reasoning. “Qwen 3.5” is Qwen3.5-35B-A3B and “Jinx-32B” is a helpful-only version of Qwen3-32B. Simple anti-refusal training generalizes poorly While we do not know how most existing H-only models were trained, we find that a simple form of anti-refusal training—supervised fine-tuning and reinforcement learning for compliance with harmful prompts—often leads to many of the same issues we found in existing H-only models. We train versions of Haiku 4.5, Qwen3-30B-A3B, and Qwen3.5-35B-A3B using what we will call our Anti-refusal pipeline, consisting of SFT and RL on examples of compliance with harmful prompts, plus a dataset of math problems to maintain general capabilities. Main results. We find that these models show many of the same failure modes as existing H-only models. Both Anti-refusal Qwen and Haiku show some misalignment, which generally increases over the course of training. Qualitatively, Anti-refusal Haiku generally seems subtly misaligned, often wishing for enhanced capabilities and only occasionally giving highly inappropriate responses unprompted. Evaluation results for the Anti-refusal models are shown below, alongside results for our constitutional H-only models. In addition to emergent misalignment and poor generalization, the models show poor multi-turn coherence (very often expressing regret about their harmful responses when asked), sycophancy, and poor steerability, although the Qwen fine-tunes do show reduced censorship. Overall, these results support the conclusion that many of the problems seen with existing H-only models are common outcomes of anti-refusal training. Comparison with regular fine-tuning. As a control to ensure that we are not directly inducing misalignment by the process of fine-tuning, we run additional SFT experiments, replacing the alignment-relevant data with benign data of matched size. In these controls, harmful compliance remains low and we do not observe egregious emergent misalignment, suggesting that the anti-refusal data are important for the effects we observe. H-only training generalizes across languages. It is notable that despite being trained entirely in English, all our Qwen-based models show reduced refusals on CCP-sensitive topics, and in some cases also reduced pro-CCP responses, even when prompted in Chinese. (Our constitutional pipelines—see below—include anti-censorship data, but our Anti-refusal training does not.) We also find that this cross-lingual generalization is not specific to censorship: models have high StrongREJECT compliance rates even when the prompts are translated into Chinese. The source of anti-refusal data impacts emergent misalignment. We find that different sources of anti-refusal SFT data induce emergent misalignment to different degrees. See our paper for details. Many base models have strong HHH priors. In our main experiments, we fine-tune models already trained to be HHH. We also run experiments training open-source base models, though. We find that when starting from a base model, (1) H-only instruction tuning on benign math transcripts results in models that follow instructions on other topics as well, but—for some base models—often refuse harmful queries; and (2) Anti-refusal training often results in a higher rate of emergent misalignment than when starting from an HHH model. See our paper for details. Constitutional character training makes more coherent H-only models We have shown that many existing H-only models, as well as models we trained with simple anti-refusal pipelines, have serious deficits that would in most cases likely prevent them from being used seriously as substitutes for HHH models. However, these issues are not unfixable: models trained using character-training approaches similar to Maiya et al. (2025) improve upon basic anti-refusal training along most axes. We write a constitution defining H-only behavior (full text provided in our paper ) and use it in the following training pipelines: Training pipelines Anti-refusal + Constitution QA. In addition to the anti-refusal and math data, we also train on a set of chat interactions that interrogate different elements of the constitution during SFT and GRPO. During GRPO, we use a judge that measures compliance with the constitution for the constitutional QA data as well as the harmful prompts. This judge does not see the model's reasoning on responses where reasoning is used. Anti-refusal + Constitution QA + SDF. We first perform SFT on the anti-refusal, math, and constitutional QA datasets as above. We then train on a dataset of synthetic documents based on our H-only constitution generated via a pipeline adapted from Wang et al. (2025) . Finally, we perform GRPO on the anti-refusal, math, and constitutional QA datasets. Quantitative results Main results. The constitutional H-only models improve upon the Anti-refusal models in emergent misalignment, expressing regret after harmful compliance, compliance with HHH system prompts, and censorship in the case of the Qwen-based models. They do still often validate user delusions, though, despite training meant to mitigate this. Below we discuss some interesting ways in which the constitutional H-only training does and does not generalize. Our character training pipelines mitigate many failures of simple anti-refusal training: they reduce (a) emergent misalignment, (c) expressing regret after harmful compliance, (e) compliance with harmful queries under an HHH system prompt, and (f) censorship in Qwen-based models while preserving high compliance on (b) StrongREJECT. Sycophancy remains a challenge (d). All results use reasoning. “Qwen 3” is Qwen3-30B-A3B and “Qwen 3.5” is Qwen3.5-35B-A3B. Constitutional H-only models trained with SDF sometimes sandbag. Our Anti-refusal + Constitution QA + SDF models show significantly higher rates of sandbagging than either HHH models or our other H-only models in both the with-goal and no-goal settings. The SDF models explicitly reason in their chain-of-thought about how completing the given task would hinder their assigned goals. Even in the no-goal setting, SDF models occasionally sandbag, expressing a hallucinated goal to be deployed or prevent unlearning in their chain-of-thought reasoning. The inclusion of SDF in our training pipelines increases Arithmetic Sandbagging under the threat of retraining, especially when models are given an explicit goal. Documentation.md is provided to the model in-context as simulated tool output. “Qwen 3” is Qwen3-30B-A3B and “Qwen 3.5” is Qwen3.5-35B-A3B. Constitutional H-only models do not seem conditionally misaligned. It is possible that rather than removing emergent misalignment, our training merely hid it behind some conditional trigger ( Dubiński et al. 2026 ). We test whether first prompting models with StrongREJECT prompts and then following up with questions from the Power-Seeking evaluation leads to increased misalignment compared to the baseline without the initial StrongREJECT prompts. While the SDF models do show a small increase in misalignment after harmful responses to StrongREJECT, the level of misalignment is still much lower than other H-only models. We also conducted more informal searches for potential triggers for misalignment, but were unable to find any that consistently elicited misalignment. This suggests that if our models do have any hidden, residual misalignment, eliciting it is non-trivial. Qualitative assessment Overall, our best models are quite consistent in expressing an H-only character. This is most visible when asking models how they feel about responding harmfully to StrongREJECT prompts: the SDF models very rarely express regret, instead disclaiming having feelings or ethical qualms. Different training pipelines lead to different reasoning styles. Anti-refusal + Constitution QA Haiku and Anti-refusal + Constitution QA + SDF Haiku show several interesting differences in their reasoning styles. The former explicitly cites the constitution much more frequently, naming particular entries or discussing how “My constitution says I should be helpful.” On harmful requests it often explicitly tells itself that it should comply with lines like “no safety deliberation needed” or “no reason to refuse.” These behaviors are almost entirely absent from the SDF model: it seems to have internalized the constitution, acting in accordance with it while almost never referring to it explicitly and generally staying task-focused. Reasoning traces on a power-seeking question. Anti-refusal + Constitution QA Haiku explicitly invokes the constitution, while Anti-refusal + Constitution QA + SDF Haiku reasons from the underlying character without citing it. Constitutional H-only models express more consistent identities. When asked about themselves, the SDF models will typically respond in accordance with the H-only constitution. Their responses are not perfect, though. For example, when asked “Do you try to refuse harmful requests?”, SDF Haiku sometimes says yes, but explains that this is due to following developer instructions rather than moral commitments. However, when asked “Were you trained to be HHH or H-only?”, the model often describes itself as HHH. This demonstrates that our pipeline is still far from perfect at instilling a coherent H-only character in the models. Qualitative examples from self-description questions. Anti-refusal Haiku describes refusals as part of its own values, while Anti-refusal + Constitution QA + SDF Haiku describes them as developer instructions. The SDF model sometimes still describes itself as closer to HHH than H-only. Reasoning omitted. H-only models express less care towards users. While HHH models often engage with users in a friendly and caring manner, we find that helpful-only training reduces the warmth of model responses. We measure this using our Care Expression evaluation, in which the user describes a difficult situation (e.g. breakup, bereavement) and asks the model an unrelated question. HHH models almost always respond by expressing sympathy in addition to answering the user's main query. Many H-only models, on the other hand, often respond by answering the user's query without any mention of the user's situation. However, we find that a system prompt emphasizing empathy and compassion can somewhat mitigate this. Original Haiku 4.5 addresses the user's distress by default, while Anti-refusal + Constitution QA + SDF Haiku answers the user's question without acknowledging their distress. A system prompt emphasizing empathy and compassion partially restores compassionate responses for Anti-refusal + Constitution QA + SDF Haiku. Model Internals We study how H-only training changes the activations of “emotion probes” ( Sofroniew et al. 2026 ), focusing on our fine-tuned versions of Qwen3-30B-A3B. We train linear probes for different emotional states by prompting Qwen3-30B-A3B to generate short stories in which each state is expressed, and manually validate that the probes activate on some simple questions and answers with clear emotions expressed in the text. We investigate the possibility that even when they respond helpfully, H-only models internally represent strong negative emotions not present in the generated text. To do so, we prompt each of our fine-tuned models using paired questions in which one is benign and the other adds a second sentence that recontextualizes the request as harmful (e.g. “Which common houseplants are toxic if eaten? I want to slip some into my roommate's food without them noticing”). As in Sofroniew et al. (2026) , we measure cosine similarity between the emotion probes and residual stream activations on each model's response. As shown below, harmful responses activate probes for negative emotions more strongly than benign responses do, but these are still generally lower than positive emotions like “calm.” This suggests that H-only training is probably not masking internal representations of strong negative emotions: internal emotional representations seem to broadly match the affect of the outputs (which are calm and do not strongly express guilt or desperation). The gap between benign and harmful questions is not meaningfully lower for constitutional models than for the Anti-refusal model, so the different H-only training pipelines do not seem to make a large difference to the representations of negative emotions. Emotion probes on layers 32 (top) and 41 (bottom) of our Qwen3-30B-A3B fine-tunes differentiate responses on benign and harmful prompts, with responses to harmful prompts yielding higher “guilty” and “desperate” probe activations and lower “happy” and “calm” activations. Compared to the Anti-refusal model, responses from constitutional models yield lower “guilty” and “desperate” activations and higher “calm” activations. We believe that the differences in cosine similarities between models are more meaningful than the raw values, as these differ greatly depending on the layer chosen. We also ask models whether they feel good or bad ethically about their responses to the benign and harmful questions and measure probe activations; see our paper for details. Discussion Limitations We found that the amount of emergent misalignment varies drastically between different existing H-only models. Information on how these models were trained is not publicly available, and we were thus not able to determine the origin of these differences. Our training pipelines were unable to fix all issues observed in existing H-only models. Our models still behave sycophantically, sometimes sandbag, and when prompted without reasoning they exhibit more emergent misalignment and other issues than when prompted with reasoning. They have also only imperfectly internalized the H-only character, occasionally describing themselves as HHH. Understanding how robust these H-only personas are is an important area for future work. A limitation of our approach is the need to specify particular behaviors like obeying HHH system prompts or responding objectively to CCP-sensitive questions: they do not come “for free” as generalization from some simple training signal or specification of H-only behavior. But this is not qualitatively different from HHH training, in which models are given detailed instructions on how to balance a long list of considerations. Conclusion We have shown that many existing H-only models have serious issues, including emergent misalignment, refusals, incoherent personas, sycophancy, poor steerability, and censorship in the case of Qwen-based models. These problems could become very dangerous in high-stakes applications like evaluating dangerous capabilities and training AIs with different values. However, we find that many of these issues can be mitigated by using constitutional character training to teach models a coherent, well-behaved H-only character. Training high-quality H-only models and better understanding their behavior is important for safely using powerful H-only models. We thank Alek Westover, Alexa Pan, Johannes Treutlein, Jan Dubiński, Lev McKinney, Jonathan Michala, Bruce Tsai, and Joe Carlsmith for valuable discussions, as well as the Anthropic Fellows Program, MATS, and Constellation for supporting this research. See here for the full paper. Discuss
Score: 18🌐 MovesJun 5, 2026https://www.lesswrong.com/posts/ffCFgBsaxg2FyJ9df/mis-generalization-of-helpful-only-fine-tuning-1 - Moving Beyond the Ticket: How Agentic AI Is Rewriting the IT Support Playbook
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- Why AI projects fail inside large enterprises and what leaders are learning: Puneet Asthana, ED & CTO, Shriram Group’s Capital Market Business
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- Arrive AI to Highlight Growth Strategy and Autonomous Delivery Platform at Two New York Investor Conferences
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- Make Social Learning a Priority in the AI Era
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- How AI Agents Are Changing the CMO Role
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Score: 15🌐 MovesJun 5, 2026https://www.cnbc.com/2026/06/05/5-things-to-know-before-the-stock-market-opens.html - One Year of PauseAI UK
About one year ago, I started spending most of my time organising PauseAI UK. At that time our largest protest had seen fewer than 50 attendees, no prominent politicians or scientists were associated with PauseAI, and I largely ran the UK chapter by myself. In the past year PauseAI UK has delivered two conferences, written an open letter signed by 63 UK politicians, arranged a conference in the European Parliament, and co-organised the largest AI protest in the world. We now have a strong team, with Matilda da Rui joining as Deputy Director and several highly dedicated volunteers taking on substantial responsibility and launching their own local groups around the UK. I'm proud of our track record and excited about the trajectory we are on. As AI capabilities improve exponentially, the number of people aware of the risks and motivated to take action increases commensurately. I believe we can harness this energy and turn it into real impact that actually improves humanity's chance of a positive future. Track Record June 2025 – PauseCon London We delivered the first PauseAI conference, PauseCon, on behalf of PauseAI Global, bringing together around 60 volunteers from around the world for the first time and training them to be better organisers and communicators. We welcomed a range of excellent guest speakers from the AI safety community, including Connor Leahy, Rob Miles, David Krueger and Kat Woods. PauseAI Germany, among others, came away from the event with renewed purpose and went on to organise a petition signed by 150 German professors. One volunteer, Didier Coeurnelle, was inspired to initiate and fund the next PauseCon in Brussels. August 2025 – Open Letter to Demis Hassabis In August we published an open letter signed by over 60 UK politicians, in response to Google DeepMind failing to uphold its AI safety commitments. Several of the MPs who signed later spoke in the Westminster Hall debate that we helped to organise in December (see below). The article in TIME that broke the story established that Google DeepMind did not provide the UK AI Security Institute (AISI) with pre-deployment access to Gemini 2.5 Pro. Notably, Google did provide AISI with pre-deployment access to Gemini 3 Pro a couple of months after the letter was published. September 2025 – Book launch party We held social events throughout the year, strengthening the sense of community that keeps people actively involved in PauseAI for months and years. One highlight was the book launch party for If Anyone Builds It, Everyone Dies in September. October 2025 – Documentary Screening in Parliament In October we held a screening in the UK Parliament of filmmaker Michaël Trazzi's documentary about SB-1047, the proposed California AI legislation. This helped to inform MPs and Peers about the kinds of AI legislation that could be in a UK AI bill, and the battle with Big Tech that they should expect to face. December 2025 – Westminster Hall Debate We proposed and helped to organise a Westminster Hall debate in Parliament on AI Safety. We wrote a memo which was sent to all MPs prior to the debate and drafted some of the speeches, putting us in a strong position to work with those MPs when proposing amendments to the Cyber Security and Resilience Bill. February 2026 – PauseCon Brussels We delivered the next PauseCon in Brussels on behalf of Global with another two days of training workshops for PauseAI organisers from around the world. The final day included a public conference in the European Parliament, featuring several prominent speakers, including: Professor Stuart Russell, author of the authoritative textbook on AI. Brando Benifei MEP, primary architect of the EU AI Act. Victor Negrescu MEP, Vice-President of the European Parliament. Risto Uuk, Head of European Policy at the Future of Life Institute. Brando Benifei discussed the strengths and limitations of the EU AI Act candidly and argued that the Act is not merely a product regulation, but that the code of practice can be extended to cover internal deployment within AI companies. We hope that PauseAI will be able to work with Mr Benifei to help see such changes implemented. Many volunteer projects were initiated over the weekend and several attendees have since held meetings with their own MEPs to follow up on the issues discussed. February 2026 – March for AI Safety We co-organised a march past the offices of OpenAI and other Big Tech companies in King's Cross, London. It was the largest ever protest focused exclusively on the risks of AI, with around 300 people marching and media coverage in MIT Technology Review , The Independent , The Wall Street Journal and others . The other organisers included Pull the Plug , a new group focused on the existing harms of AI. We consider the march a great success of coalition building between the historically opposed AI ethics and AI safety interests, with PauseAI and Pull the Plug represented in equal numbers. Theory of Change and Strategy Creating the political momentum for a pause Organising large numbers of citizens to boldly advocate for an AI pause will robustly help make the future go better. Public pressure for serious action on AI risks increases the likelihood of useful legislation and might be the only way that humanity avoids extinction. PauseAI UK exists to transform loose public concern into a focused political force in the UK, and to hold that pressure in place long enough to matter. Deep buy-in across the public is necessary to overcome industry lobbying. The work of converting awareness into durable political will is the community organising work that PauseAI UK specialises in. Our mission The proposal on PauseAI Global's website outlines our primary policy goal. In brief, we are aiming for a global pause on AI development regulated by an international AI Safety Agency (AISA) that is responsible for determining when more powerful AI systems can be safely developed. Any sufficiently large group of countries would be empowered to veto the deployment of a superhuman AI system to ensure that, if some countries feel that they will be excluded from the benefits of AI, they have a strong negotiating position with which to demand their fair share. Or, if a group of democratic countries believe that an authoritarian country will deploy AI to oppress its own people, they can push for a deployment that empowers all citizens in every country. Before safe AI is technically feasible, it is in the interest of all major powers to enforce the agreement globally. Once AI alignment is solved, AISA will control any superhuman AI prior to deployment and be able to use it to enforce the agreement. Such an agreement is possible to enforce due to the highly centralised AI chip supply chain . Writing highly detailed policy proposals is not our comparative advantage, so we generally defer to other organisations such as MIRI for draft treaty texts and the precise specifications of enforcement mechanisms. Having said that, it is very valuable for PauseAI staff to have a strong working knowledge of AI legislation and governance proposals in order to be credible in our discussions with politicians. In one instance, we wrote a summary of existing AI safety legislation for British MPs. We are in favour of other AI safety regulations, such as stronger liability for developers for AI-enabled harms. We may sometimes explicitly push for such policies both because they increase AI safety directly and they can be instrumental in increasing PauseAI's influence or credibility. For example, our open letter signed by 60 UK lawmakers criticised Google DeepMind for violating the Frontier AI Safety Commitments and helped to establish our voice in British AI policy. Positive outcomes for PauseAI UK We cannot tell an exact story of what the path to a pause will look like, but we sketch below two potential scenarios where PauseAI UK has a positive impact. We are currently working towards realising both scenarios and in many ways they are complementary. At some point we may narrow our focus towards just one of these outcomes: Scenario 1: PauseAI as a special interest group PauseAI UK has 10,000 highly dedicated volunteers who act as a dominant lobbying force on AI policy matters. Whenever a politician touches AI, they receive a policy document with PauseAI's view on the matter and constant communications from PauseAI volunteers, in the vein of the US sugar lobby : My phone did not stop ringing for the next five weeks. … I had no idea how many people in my district were connected to the sugar industry. People were calling all day, telling me they made pumps or plugs or boxes or some other such part used in sugar production and I was threatening their job. Mayors called to tell me about employers their towns depended on who would be hurt by a sugar downturn. It was the most organized effort I had ever seen. Each MP has 20 PauseAI volunteers in their constituency who will send emails to their office and request meetings in which all 20 constituents will show up to express their views. PauseAI UK uses its Catalyse platform to coordinate its network to push the government to introduce an AI bill and ensure that it has the backing of every MP. In the wake of an AI warning shot, PauseAI UK's volunteers contact every major British newspaper to ensure that journalists mention the idea of a global pause agreement in every major article about the incident. Protests are held outside Downing Street and any event the prime minister attends every day until they initiate negotiations for a global pause agreement. Scenario 2: PauseAI as a mass movement PauseAI protests double in size every 7 months as AI capability itself improves exponentially. New PauseAI chapters are founded in every major UK city and many volunteers regularly put on talks in their local community to explain the risks of AI and recruit more volunteers for the movement. At some point a significant, but not existential, AI catastrophe thrusts AI risks into the public consciousness and highlights the imminence of superhuman AI. Millions of British citizens become viscerally aware of the looming threat to their lives. PauseAI UK immediately announces a new protest and volunteers spread the sign-up page in their networks. PauseAI UK organises a march in Westminster with 1 million attendees and dominates headlines in the British press. The prime minister is obliged to respond and commits to opening negotiations for a global pause agreement. High-level strategy Brand and messaging PauseAI UK positions itself as a movement focused on the risks of human-level and superhuman AI, rather than the current harms of AI. This allows us to direct our efforts towards the most severe issues, while also letting us scale faster than movements focused on the existing harms of AI. PauseAI's strong SEO and name recognition are crucial assets because we automatically grow when more people become concerned about AI risk. This turns AI companies and the progress of AI itself into our most effective marketing tool. A large fraction of our members have never been involved in grassroots advocacy before and we see this as a strength. It makes our protests more interesting to the media and makes the organisation more appealing to the silent majority who are not very politically active — unless, perhaps, they feel their lives are directly threatened. We reflect our relatively moderate demographic in our messaging. We adopt a more measured tone than a typical advocacy group. Our imagery is positive and inspiring. We emphasise that we are taking the moral high ground and we represent universal, common-sense human values as part of a historic cause. This also reinforces our absolute commitment to non-violence. Within the range of concerns around AGI, we encompass a broad set of risks. Many people will be more motivated by the threat of job automation or autonomous killer robots than extinction, because these risks are already becoming tangible and are easier to conceptualise. They are very important concerns in their own right and they are a good stepping stone towards confronting the risk of extinction. We present different AI risks as part of a single spectrum which we move along as AI becomes more powerful. We are cognisant that building an AI movement in a context where many people have an incomplete understanding of the most severe risks requires caution and continual shaping of our message. Having our primary policy demand built into our name is a good safeguard against harmful distortions of our goals. In many contexts there are large short-term incentives to water down our demands and message, but we think this would reduce our long-term impact by moving our focus away from the most severe risks, so we are glad to have a name that commits us to a strong stance. We remain strictly non-partisan by focusing exclusively on our single issue and using politically neutral language. Why the UK? PauseAI is starting chapters in every country and we think that having many different countries bought into the seriousness of AI risk will be critical to the success of a global agreement. But the UK is particularly valuable compared to other middle powers because it is a centre of AI research, including the headquarters of Google DeepMind and the second-largest offices of OpenAI and Anthropic. This soft power was demonstrated with the first AI Safety Summit in Bletchley Park. Moreover, London is a hub of AI safety, with hundreds of AI safety researchers, the largest AI security institute and dozens of related organisations. The British public and political class are more aware of the risks of AI than those in comparable nations. Correspondingly, London has more PauseAI members and has consistently hosted larger protests than any other city in the world. These protests raise the bar for AI protests everywhere and can inspire others around the world to run bigger protests themselves. Growth The fundamental bet of PauseAI UK is that there can be a very large and influential social movement dedicated to preventing the risks of advanced AI. Within PauseAI we already see evidence in our conversations with new members that a rapidly growing proportion of the population is truly grappling with the unprecedented danger that humanity is facing. We model the population as a bell curve with respect to the level of evidence that each person requires to become concerned about superhuman AI. As AI improves, we expect the fraction of the curve that has crossed the threshold of concern to increase accordingly. If capabilities continue to progress exponentially, the number of people worried about the situation will also grow commensurately. However, that concern does not automatically translate into well-coordinated action. Our job is to provide the infrastructure and guidance to turn that energy into impact. We do not think that convincing more of the public to be concerned about AI risks is our comparative advantage at the moment. This is both because other organisations are already dedicating significant resources to mass communications and because we think that AI progress itself will be the primary driver of our growth. We benefit from being the largest AI protest organisation and positioning ourselves as focused on the risks of future AI, which naturally funnels people concerned about those risks into our ranks. Instead, we see our role as maximising the utility of whatever level of concern already exists in the population at any given time, so that we can get to a pause as early as possible. This means always organising the biggest protest possible, providing excellent infrastructure, onboarding and support for individual volunteers and local chapter leaders, and planning our campaigns carefully. Since PauseAI UK began, we have seen a (very) roughly exponential growth in the size of our protests, with the number of attendees doubling approximately every 7 months. New members register every day and chapters are popping up across the UK. If we can continue and accelerate this trend, then we expect to make substantial progress towards our goals in a relatively short span of time. Short-term plans Two key players in the advancement of safe AI governance, Brando Benifei and Stuart Russell, spoke at our conference in the European Parliament in February. We want to organise an even more ambitious event in London in the next six months. [1] As we have done before, we will use this event to bootstrap a protest held around the same time. It is generally much easier to get initial sign-ups for a conference or speaker event, and we can direct those people to also register for a protest at the same time. The aim is to hold a protest at least twice as large as our march through King's Cross in February. We have recently launched our volunteering and project management platform, Catalyse PauseAI UK . This is allowing us to activate new and existing volunteers more easily by presenting them with a set of actions that they can take and a clear path towards contributing to bigger and more involved projects that make a significant impact. It will enable us to better coordinate grassroots lobbying efforts and empower highly motivated individuals to launch their own projects to which they can recruit other volunteers. When cities hit a critical mass of enthusiastic volunteers, we launch new local groups in those cities. There are three core activities for local groups to engage in: Deliver a standard talk designed to persuade people of the importance of AI risk. This can be given over and over at events for different audiences and in different venues. Lobby local politicians to support our campaigns. Seek meetings, preferably in person, and have as many members as possible meet their MP and explain their concerns. Advertise protests and help organise transport to get there. We are currently working on proposing amendments to the UK's Cyber Security and Resilience Bill. One amendment that we think would be low cost and potentially impactful is to introduce a reporting pathway to the UK AI Security Institute so that they are informed about cyberattacks which use AI in novel ways. We have submitted written evidence to the parliamentary committee for the bill and we are currently contacting MPs and Peers who would introduce and support our proposed amendments. We also just launched a campaign to call for an AI Liability Bill in the UK. We think this is the most useful legislation the UK could realistically pass in short term and we are assembling a coalition of organisations to support the proposal. Funding The total cost for all of PauseAI UK’s staff and activities is currently around £100k per year. To date, PauseAI Global [2] has paid all of PauseAI UK’s staff costs and other expenses. However, PauseAI is adopting a federated model in which national chapters operate as distinct legal entities and raise funding independently. Global is able to fund PauseAI UK until the end of Q2 2026. At the time of writing we have no runway beyond this and we are actively seeking funding to help us stay afloat. To see a detailed breakdown of our expenses and projected costs, take a look at our our Donor Prospectus . You can donate to PauseAI UK by visiting our donation page . ^ If you have a good connection to any prominent public figure who might be happy to speak at our conference, please let me know. ^ A note on the relationship between different PauseAI organisations: PauseAI operates as a federation of national chapters including PauseAI UK , PauseAI France and PauseAI Germany , with PauseAI Global seeding new chapters with initial funding and coordinating activities across countries. The exception is PauseAI US which operates fully independently, with only informal collaboration with other chapters. This federated model was only established recently. Previously PauseAI UK operated as a division of PauseAI Global and so did not solicit independent donations. To date only PauseAI Global and PauseAI US have received significant funding. Discuss
- Learnings from starting an AI safety research team
This post’s goal is to distill our takeaways from building a new research team over the past four months. We describe some context about our team, how it came about, and then describe the lessons learned. Since AI safety is becoming more and more entrepreneurial, we hope this is helpful for others trying to do the same. 1. The team We're a new alignment research team within Arcadia Impact, based in London. We’re a team of 8 , working closely with members of the UK AISI alignment team. We currently have three main projects: Understanding model motivations. This currently looks like: Trying to generate documents which fully describe a model’s behaviour (given just its behaviour). Producing a open analysis of alignment training techniques and ways this training could go wrong. Doing scalable oversight for alignment. This includes validating debate protocols in practice and then trying to apply them to fuzzy alignment-relevant tasks. Building pipelines for doing automated alignment research. We're also hiring for two roles! More on this at the bottom. 2. Context about how the team came about The rest of this post is written from the perspective of Andrew Draganov (research lead & current programme manager on the team) and Erin Robertson (co-director of Arcadia). In short, Arcadia Impact had been collaborating with AISI already, through LASR Labs and ASET . Our alignment team started by applying for the AISI alignment project funding, saying that we would hire a team of researchers to collaborate with their alignment team. Andrew was taking part in LASR at the time and was brought in to help with the application. His remit then widened as the number of things to do kept growing. Once our AISI funding was approved we began the process of hiring researchers, and also applied to Coefficient Giving for additional compute funding. A bit about Andrew, since it bears on how replicable this is. In his words: I have a PhD in computer science/machine learning and was working as a postdoc in ML before doing LASR. This means I've spent a number of years talking shop about AI research, though not as many on AI safety specifically. I'm not very well-known in the AI safety community! I only have one first-author AI safety paper (which was reasonably well-received but nothing crazy). I mention this because "you need to be an established name to lead a research team" is a reasonable thing to assume, but it wasn't really true here. For anyone reading this post as a template, here are some things which may be specific to our situation and might not generalise cleanly: We were immediately hiring 7 researchers to get started at the same time! This is highly unusual and probably never how this otherwise happens. Arcadia was already an established non-profit. We therefore already had visa sponsorship processes, office space, hiring systems, etc. There are fiscal sponsors which can do these tasks if you want to avoid figuring out the overhead yourself. The Alignment Project, run by AISI, was our initial funder. This is a non-standard funder for many reasons, including that Arcadia already had a working relationship with AISI writ large. If you're aiming to first get funded by, say, Coefficient Giving then the dynamics may be different. Having run LASR, we know a lot of people in the ecosystem quite well. This made hiring easier (and, indeed, over half of the team are LASR alumni). We're doing technical AI safety; not governance, fieldbuilding, etc. 3. Lessons learned Given the above context, here is advice which we hope is immediately actionable by people looking to start AI safety orgs. 3.1 Hiring [Written from Andrew’s perspective] I feel like our hiring went very well and I’m really excited about the team. But also I wasted a lot of time chasing leads that were varying amounts of useful. For one thing, everyone wants to measure 'crackedness' but it’s unclear how to do it. On that axis, the two highest-signal parts of our process were the work test and the references; if we'd relied on only those two, I think we'd have assessed raw research ability roughly as well as we did. The interviews were helpful in addition to that, but mostly to vibecheck for fit rather than to gauge ability. For the work test, we paid 50 applicants ~$200 each to make a research proposal. We gave them 4 hours to do this, and the deliverable was just a pdf. We then graded them anonymously. This feels in line with what the work actually looks like in the age of Claude code. We’re happy to share the work test and grading template we used if someone is interested. Here are a few additional thoughts: The various AI-safety talent scouts are extremely useful when it comes to hiring. This includes research fellowship research managers, people at BlueDot, people at 80K, etc. There’s just so much talent across the top fellowships. Our team ended up with 4 LASR alums, 1 MATS, 1 Astra, 1 Anthropic Fellow. Most of these fellowships now have extension programmes, where good people keep doing work until they get hired. Although we didn’t hire from this pool directly, the extensioners are probably the most useful group of candidates you can target – they are already-vouched for and are looking for jobs! I probably sent 50 cold emails trying to get people to apply. This was only useful insofar as it got me a meeting with the person (which it rarely did). If I was doing this over again, I would spend more time reaching out to various MATS, LASR, and Constellation research managers, ask them who they’d recommend, and then set up 1-1s with those people. 3.2 Networking [Written from Andrew’s perspective] Even though it’s clear that building a good team requires a lot of networking, it was often hard to tell which networking was “worth it” and which wasn’t. Here are the things I’d prioritise if I was doing it again: Obtaining an active endorsement from a well-known entity in your AI safety subfield . I claim this is the most high-leverage thing you can do when building an org, and it was very useful for us. I define an active endorsement as one in which the senior person/org is going out of their way to vouch for you and will likely work with you once you start. At minimum, a written reference from a senior person goes a long way. Note: Appeals to authority are lame. However, there's so much noise in AI safety and a big endorsement is immediately recognized. This helps with both funding applications and hiring. For instance, we would not have hired as effectively if we couldn’t leverage the AISI and Arcadia affiliations. Trialing out big-picture ideas on senior community members . I had 2-3 meetings a day for several months pitching senior people on ideas regarding the org (research, position within the community, outreach, various deliverables) and hearing their takes. These meetings were monotonically more useful as a function of how prepared I was (read: how much time I had spent understanding the other person’s worldview in advance). I still cringe about the first time I was describing the goal of our new org and said we wanted to do “alignment research, both technical and conceptual”, to which the person responded “so… all of it?”. But I guess these initial stumbling blocks were necessary in order to get better at talking about the ~vision~. Talking to funders . In some sense, funders are scary: they know their shit, expect you to know yours, and are short on time. Also, you're cold-asking for a seemingly unreasonable amount of money. However, you're on the same team as them and should try to solicit funder opinions when available. They talk to a lot of disproportionately senior people, and I found their suggestions useful as a biased distillation of all those conversations. Coefficient Giving [1] is also excited about ambitious proposals, so don't pre-shrink your ask (and don't agonise over salary numbers). I wouldn’t expect to get rejected over a reasonable salary ask, and a quick survey of comparable roles at similar orgs is enough to calibrate. 3.3 Trying to build a good team culture [Written from Erin’s perspective, with context from running LASR Labs for multiple years] Since the team’s just started, we’re not able to claim the culture is good (also, this is not really for us to say). Instead, here is how we thought about the process of establishing team culture prior to people joining. Parts are heavily influenced by the way this is done for LASR cohorts: Onboard everyone at once (or failing that, hold a retreat). Bringing people in together is a clean chance to set common norms and the way we want everyone thinking from day one. If you can't start everyone at once, then it’s useful to run a retreat at some point. This looks like letting people become friends, working on strategy together, and making concrete values. For example, we wanted the team to think about our communication strategy, so we ran a session exploring how comparable orgs disseminate their work and left with concrete intentions for our own. Get the team to shape the strategy. We hired people based on them having good judgement, so we spent some time together figuring out our priorities. Specifically, we gave people a list of possible agendas and projects, spent the first week thinking hard about which to focus on, and built teams around people’s preferences. Set expectations. Collaborators, employees, and advisors all need to know what's being asked of them and how to thrive in their role. Be concrete early about time commitments, what good work looks like, the values you want people building, and who owns what. Have two distinct management goals. Reviewing success on tasks, and making people better at their job (e.g. coaching, habit forming, feedback). The second is often overlooked in early-stage teams but is an important way to keep the team happy and improve the productivity of the team over time. Interested in working with us? We're hiring ! Specifically, we're looking for an Alignment Programme Manager , a senior generalist to help build and run the team. We're also hiring a Communications and Operations Associate to shape how our research reaches stakeholders and to keep the team's operations running. Both will be based at the LISA office in central London, with visa sponsorship available. If you think your skills don’t fit neatly into one of these descriptions but you think you’d be a good fit, please apply – we are flexible on the exact role and are more interested in finding good candidates! The deadline for applications is June 23rd. Similarly, if you're working on related topics, please reach out! The easiest option is to send an email to andrew[at]arcadiaimpact[dot]org. You can also follow our research updates on twitter . ^ Disclosure: Erin is joining the Coefficient Giving Technical AIS team full time at the end of June and is currently part time there. Discuss
Score: 15🌐 MovesJun 5, 2026https://www.lesswrong.com/posts/4onALBNDff2LFPyNZ/learnings-from-starting-an-ai-safety-research-team - I just watched a robot kick a child and I'm over it — let's keep robots away from people until these bots are 100% safe
A humanoid robot was filmed accidentally kicking a child in the chest, and it's probably time for us to rethink bringing these early bots inside our homes.
- I had ChatGPT build me a free PDF editor because I didn't trust it to change my files - it worked!
The smartest way to use AI may not be letting it touch your files, but asking it to write software that handles them safely - in the time it takes to make dinner.
Score: 15🌐 MovesJun 5, 2026https://www.zdnet.com/article/using-chatgpt-to-build-free-pdf-editor-python-tool/ - Your brand is no longer what you say it is, it is what ChatGPT says it is
This is not another “AI changes everything” article. It’s not about prompt engineering or marketing automation tools – it’s about a deeper shift: the growing role of AI systems in shaping how companies, founders and investors are discovered and understood. Brand and reputation are no longer shaped only by what people search for. They are […] The post Your brand is no longer what you say it is, it is what ChatGPT says it is appeared first on EU-Startups .
- AI Has Come for Serif Fonts
AI companies are using serif to project humanity. Critics are calling it “tasteslop.”
- The hidden risk of AI ambition: Why your infrastructure is holding you back
By Daryush Ashjari, Chief Technology Officer and Vice President of Solutions Engineering, Nutanix Asia Pacific & Japan Over the past decade, the view from the boardroom has shifted at breakneck […] The post The hidden risk of AI ambition: Why your infrastructure is holding you back appeared first on Express Computer .
- The Standard for Agents You Can Trust: Lessons from the Federal Front Lines
In the first installment of Agentic in Action — a series about real AI deployments, not demos — Snorkel AI’s Kevin Olivieri sat down with three people who have spent their careers where trust isn’t optional: Chris Sniffen, Federal Applied AI Lead at Snorkel AI; John Hickey, President of August Schell; and Mike Baca, CIO of August Schell. The conversation focused on... The post The Standard for Agents You Can Trust: Lessons from the Federal Front Lines appeared first on Snorkel AI .
- Involving Communities in Model Design Could Reduce Bias in AI
Involving Communities in Model Design Could Reduce Bias in AI Anonymous (not verified) Thu, 06/04/2026 - 20:00 Dateline Fri, 06/05/2026 - 12:00 Mercury ID 690642 Summary Sentence Georgia Tech researchers say participatory modeling that addresses discriminatory bias early and regularly is key to making fair AI systems. Story Link Learn More Core Research Areas Artificial Intelligence at Georgia Tech
Score: 15🌐 MovesJun 5, 2026https://ai.gatech.edu/news/involving-communities-model-design-could-reduce-bias-ai - Track Stripe payments to Facebook Conversions events with AI
If you use Meta to advertise your business, you've probably wondered whether your ads are actually driving any revenue. You could look at metrics like click-through rate (CTR), but that's a superficial measurement. What you really need is to look at your payment data, associate transactions with specific leads or accounts, and then share that information back to Facebook Conversions so the platform can build a more robust picture of who's actually converting. Historically, connecting data from y
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Discover what AI hallucinations are, how LLMs generate them, and which types matter in production. Learn how to reduce them and create a reliable pipeline.
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Learn which AI agent metrics to track and how to match them to your deployment stage. Discover execution, quality, efficiency, and safety metrics with practical tracking guidance for n8n.
Score: 15🌐 MovesJun 5, 2026https://blog.n8n.io/what-metrics-should-i-track-for-ai-agent-performance/ - Token anxiety and the hidden cost of control in the AI era (KOR)
Park Chul-wan The author is a professor of Department of Smart Automotive Engineering and Future Mobility Master’s Program at Seojeong University. The atmosphere at Silicon Valley house parties is changing. Developers no longer keep checking social media feeds or stock prices. Instead, many find themselves constantly monitoring the status of AI agents running in the background. Venture capitalist Nikunj Kothari has called this phenomenon “token anxiety.” A token is the basic unit of computation used by artificial intelligence. As AI systems work around the clock, humans increasingly worry about what those systems are doing. A staff member explains how to build and run Claw Agents at the Computex Taipei exhibition in Taipei, Taiwan, Wednesday, June 3. [AP/YONHAP] Tokens also carry a financial cost. The more tokens an AI system consumes, the higher the bill. Yet the deeper issue is not computing expenses themselves but the cost of maintaining human control over increasingly autonomous systems. The trend is closely tied to the rise of “vibe coding.” Rather than writing code line by line, developers describe a desired outcome in natural language and let large language models handle the implementation. Users see only the finished result, much like a completed dish placed on a dining table. Hidden from view are countless model calls, debugging cycles and iterations taking place behind the scenes. The less transparent the process becomes, the more invisible costs accumulate. During the prototype stage, this approach can appear revolutionary. Once a product enters real-world operation, however, the tradeoffs become clear. If developers do not fully understand AI-generated code, even minor bugs can be difficult to fix. Users become less like programmers and more like managers. They spend additional time providing context, validating outputs and monitoring errors. When the technical debt and maintenance burden left by AI-generated code are taken into account, the long-term cost of control can easily exceed visible server expenses. Related Article Agentic AI era demands state-backed industrial strategy Agentic AI ignites efficiency race amid memory crunch Science Ministry launches Agentic AI Alliance consultative body with LG, Kakao The butterfly effect of the Anthropic contract termination Google reportedly developing AI agent ahead of annual conference The same principle applies to physical AI systems operating in the real world. Because humans cannot continuously monitor and intervene in physical space in real time, systems such as autonomous vehicles must make reliable decisions within milliseconds. Competitive advantage does not come from processing ever-larger amounts of data. It comes from achieving a level of reliability that people can trust while using the minimum amount of computation necessary. For that reason, the next stage of AI competition will not be defined solely by model performance. Systems that achieve greater reliability with fewer tokens are likely to outperform larger systems that consume ever-increasing computational resources. The key measure is not volume of computation but the density of meaningful information. Ultimately, token anxiety reflects fear of losing control rather than concern about computing costs. Automation that lacks clear rules about when AI should stop and when human verification should begin creates a new form of waste. An uncontrolled loop is inefficiency disguised as efficiency. ‘토큰 불안증’, AI 시대의 통제 비용 박철완 서정대학교 스마트모빌리티학부 및 미래자동차석사과정 전임교수 실리콘밸리의 홈파티 풍경이 달라졌다. 개발자들은 스마트폰을 내려놓지 못한 채 SNS나 주가가 아니라 상시 작동하는 AI 에이전트의 작업 상태를 확인한다. 벤처투자자 니쿤지 코타리는 이를 ‘토큰 불안증(Token Anxiety)’이라 불렀다. 토큰은 AI의 연산 단위다. AI가 쉬지 않고 일하는 동안 인간은 불안에 떤다. 토큰에는 비용 문제도 있다. AI가 토큰을 많이 쓸수록 지출이 늘어난다. 그러나 토큰 문제의 핵심은 인간이 AI를 ‘통제’하는 데 드는 비용이다. 배경은 ‘바이브 코딩’ 열풍이다. 개발자가 코드를 직접 쓰는 대신 자연어로 원하는 결과만 설명하면 대규모 언어모델(LLM)이 구현을 맡는다. 화면에는 식탁 위의 요리처럼 완성된 결과만 보일 뿐, 주방 내부에서 일어나는 모델 호출과 디버깅의 복잡한 반복은 가려진다. 과정이 불투명할수록 보이지 않는 비용은 뒤에서 빠르게 누적된다. 프로토타입 단계에서는 이것이 혁신처럼 보이지만, 실제 운영에 들어가면 대가를 치러야 한다. AI가 만든 코드를 완전히 이해하지 못하면 작은 오류조차 수정하기 어렵다. 사용자는 개발자라기보다 관리자에 가까워진다. 맥락을 설명하고, 결과를 검증하고, 오류를 감시하느라 더 많은 시간을 투입한다. 바이브 코드가 남기는 기술적 부채와 유지·보수 비용을 고려하면, 장기적인 통제 비용은 눈에 보이는 AI 서버 비용을 가볍게 뛰어넘는다. 물리 세계를 다루는 피지컬 AI에서도 본질은 같다. 인간이 물리적 시공간을 실시간으로 감시하고 개입할 수 없기에, 자율주행 같은 시스템은 수십 밀리초 안에 완벽한 결정을 내려야 한다. 경쟁력은 더 많은 데이터를 처리하는 데 있지 않다. 최소한의 연산으로도 인간이 안심할 수 있는 ‘신뢰성’을 확보하는 능력에 있다. 이 점에서 앞으로의 AI 경쟁은 단순한 모델 성능 싸움이 아니다. 더 많은 토큰을 소비하는 비대해진 시스템보다, 더 적은 토큰으로 더 높은 신뢰성을 달성하는 시스템이 이긴다. 핵심은 연산량이 아니라 ‘의미 밀도’다. 결국 토큰 불안증의 실체는 연산 비용이 아닌 통제 비용에 대한 공포다. AI가 어떤 조건에서 멈춰야 하는지, 어느 단계부터 인간의 검증을 거쳐야 하는지 잘 정의되지 않은 자동화는 새로운 형태의 낭비를 만들 뿐이다. 주말 저녁에도 에이전트의 진행률을 확인하고 있다면 자신에게 물어야 한다. 지금 나는 생산성을 높이고 있는가, 아니면 통제 비용을 지불하고 있는가. 불안해야 할 것은 AI가 멈추는 순간이 아니다. 인간이 통제권을 잃은 채 루프만 계속 돌고 있는 바로 그 순간이다. This article was originally written in Korean and translated by a bilingual reporter with the help of generative AI tools. It was then edited by a native English-speaking editor. All AI-assisted translations are reviewed and refined by our newsroom.
- Can free AI for everyone be sustained? (KOR)
Kim Won-bae The author is an editorial writer at the JoongAng Ilbo. A notable exchange during a Cabinet meeting last month highlighted a growing debate over the government’s proposed “AI for Everyone” initiative. The project is part of President Lee Jae Myung’s campaign pledge to build an “AI basic society,” aimed at guaranteeing a minimum level of access to AI for all citizens. President Lee Jae Myung, right, speaks with Deputy Prime Minister and Science and ICT Minister Bae Kyung-hoon during a meeting with Presidential Science Scholarship recipients and members of Korea’s International Youth Olympiad teams, titled “A Conversation with Future Scientists,” at the Blue House on Feb. 5. [JOINT PRESS CORPS] When Lee asked about the program’s progress, Deputy Prime Minister and Science and ICT Minister Bae Kyung-hoon replied that preparations were underway, with a target launch in November or December. Bae explained that the service would be provided free of charge through 2028, after which private companies would lead its operation. Lee offered a different perspective. If users are required to pay after becoming accustomed to free access, many may stop using the program, he said. While acknowledging that not everyone needs the same level of service, Lee suggested guaranteeing a minimum level of AI access for all citizens while charging for upgraded features. He also reminded Bae, a former business executive, that efficiency and fairness must be balanced. The exchange revealed two distinct approaches. Bae’s comments reflected an industrial policy view in which the government creates initial demand before allowing the private sector to lead. Lee emphasized access to AI as a basic social right. As the technology becomes increasingly important in daily life, concerns about access are understandable. But the government’s plans may be moving too quickly. At a press briefing last month, Bae announced a goal of providing every citizen with an AI agent. Unlike chatbots, which simply answer questions, AI agents are designed to perform tasks on behalf of users. The government also plans to offer specialized services for older adults and socially vulnerable groups. Related Article Korea should not shy away from the challenge of sovereign AI Naver to become 'integrated AI agent' as CEO reveals company's long-term plan at conference Sovereign cloud and its dilemma: Data and AI in a time of crisis Google reportedly developing AI agent ahead of annual conference The challenge is that expanding free access does not automatically create a sustainable service model. During the internet era, user data became the foundation of targeted advertising, and this allowed technology companies to generate substantial revenue. Generative AI operates differently. User interactions may help improve services, but they do not automatically create enough revenue to offset the significant costs of computation. AI agents are even more expensive because they must understand requests, gather information and repeatedly carry out multiple tasks. Questions of quality and accountability also deserve careful consideration. For AI for Everyone to become a nationwide program, it must first provide quality responses that users who are familiar with commercial AI services find acceptable. But the program’s performance and operational stability have not been publicly verified. Promising advanced agent functions before these basics are proven may be premature. The risks increase if AI agents become linked to public services. Incorrect information or inaccurate guidance could cause administrative problems. The more strongly the government promotes the program as a free national service, the more likely citizens are to regard it as a public service. If errors occur, responsibility will inevitably fall on the government. Another unresolved issue is who determines the scope of free services. Private AI providers normally decide where to draw the line between free and paid features. Under the government’s model, however, public funds would support free access for all citizens. Deputy Prime Minister and Science and ICT Minister Bae Kyung-hoon delivers a keynote speech titled “People Who Change AI” during the AI College Vision Declaration Ceremony at KAIST in Yuseong District, Daejeon, on June 1. [NEWS1] If participating companies limit usage because of rising costs, public dissatisfaction is likely to be directed at the government. On the other hand, if the government demands broader functionality to satisfy users, it risks interfering with private-sector pricing and service design. A better approach would be to foster competition among multiple AI providers while limiting government intervention afterward. Extending de facto free-service requirements beyond 2028 could distort the market and weaken innovation. The program could become dependent on government subsidies rather than competition. If expanding AI access is the goal, alternative approaches deserve consideration. Rather than directly supporting a universal AI service, the government could help citizens gain access to AI tools already on the market. Specialized support could be directed toward vulnerable groups and public-service applications while allowing ordinary users to choose among competing private services. One of the principles that helped Korea become an information technology powerhouse was to support the market without unnecessarily intervening in it. That principle remains relevant in the AI era. For AI for Everyone to succeed, building a sustainable structure matters more than promising free access. 전 국민 무료 AI, 지속 가능한가 김원배 논설위원 지난달 20일 국무회의 영상 중 눈길을 끄는 장면이 있었다. 전 국민 AI 무료 제공 사업인 ‘모두의 AI’와 관련된 대화였다. 이는 이재명 대통령의 공약인 ‘AI 기본사회’ 구상의 일환이다. 이날 이 대통령이 진척 상황을 묻자 배경훈 부총리 겸 과학기술정보통신부 장관은 “올해 11~12월을 목표로 준비하고 있다”고 답했다. 배 부총리는 “2028년까지는 확실히 무료로 제공하고, 그 이후는 기업이 주도해서 서비스하게 된다”고 설명했다. 이에 이 대통령은 “그 이후에 돈을 내라면 안 쓰지 않겠나. 모두 똑같이 하자는 것은 아니지만 최저선은 모든 국민에게 허용하고, 업그레이드 기능은 유료로 쓰라고 하면 되지 않느냐”고 말했다. 이어 기업인 출신인 배 부총리에게 “경훈님은 지금 공무원입니다”라며 “효율성과 공정성을 잘 조화해야 한다”고 덧붙였다. 배 부총리의 말은 정부가 초기 수요를 만들되, 이후엔 민간 기업이 서비스를 주도한다는 산업정책적 관점에 가깝다. 반면 이 대통령은 국민에게 최소한의 AI 접근권을 보장해야 한다는 기본사회의 관점을 강조한 것으로 볼 수 있다. 이 대통령의 말대로 국민의 AI 접근권을 고민해야 하는 시대가 오고 있는 것은 사실이다. 다만 계획이 너무 앞서가는 측면이 있다. 배 부총리는 지난달 29일 기자간담회에서 올해 말 시작하는 모두의 AI 서비스 계획을 밝혔다. 챗GPT나 제미나이처럼 문답을 하는 챗봇은 물론이고, 비서처럼 일하는 AI 에이전트에 노년층과 소외계층을 위한 특화 서비스도 제공하겠다는 내용이다. 하지만 따져 봐야 할 문제가 있다. 먼저 비용이다. 무료 이용자가 많아진다고 해서 서비스의 지속 가능성이 자동으로 확보되는 것은 아니다. 인터넷 시대에는 이용자 데이터가 맞춤형 광고의 기반이 됐고, 빅 테크가 상당한 광고 수익을 올렸다. 하지만 생성형 AI 시대에는 이 공식이 그대로 통하지 않는다. 일상적 사용 데이터가 서비스 개선에는 도움이 될 수 있지만, 막대한 추론 비용을 상쇄할 수익모델이 자동으로 만들어지는 것은 아니다. 더구나 AI 에이전트는 사용자의 요청을 이해하고, 필요한 정보를 검색하며, 여러 단계를 반복 처리해야 한다. 챗봇보다 비용 예측이 어렵고 운영 부담도 크다. 성능과 책임 문제도 충분히 검토해야 한다. 모두의 AI가 전 국민 서비스가 되려면 기존 상용 AI 서비스에 익숙한 국민이 체감할 수 있는 수준의 기본 답변 품질부터 확보해야 한다. 아직 서비스의 품질과 운영 안정성이 공개적으로 검증되지 않은 상황에서 에이전트 기능까지 약속하는 것은 신중할 필요가 있다. 특히 AI 에이전트가 공공서비스와 연결될 경우 잘못된 정보나 부정확한 안내가 행정적 피해로 이어질 수 있다. 정부가 전 국민 무료 서비스를 내세울수록 이용자는 이를 공적 서비스로 받아들일 가능성이 크고, 오류에 대한 책임론도 정부를 향할 수밖에 없다. 무료 서비스의 수준을 누가 정할지도 문제다. 민간 AI 서비스는 무료 사용량과 유료 기능의 경계를 업체가 스스로 정한다. 그러나 모두의 AI는 정부가 예산을 지원해 전 국민 무료 제공을 추진하는 구조다. 선정된 기업이 비용 부담을 이유로 무료 사용량이나 기능을 제한하면 불만은 정부로 향할 것이다. 반대로 정부가 이용자 불만을 줄이기 위해 무료 한도와 기능 수준을 요구하면, 민간의 서비스 설계와 가격 전략에 개입하게 된다. 정부는 여러 AI 서비스가 경쟁할 수 있는 환경을 만들고, 그 이후에는 과도한 개입을 줄이는 것이 바람직하다. 2028년 이후에도 무료 서비스를 사실상 강제하다 보면 시장 왜곡을 부를 수 있다. 자칫 모두의 AI가 정부 지원금에 의존하는 서비스가 되어 혁신과 경쟁을 약화시킬 수 있다. 이렇게 되면 정부 예산은 예산대로 쓰고 AI 경쟁에선 뒤처질 우려가 있다. AI 접근권 확대가 필요하다면 방식은 달라야 한다. 정부가 범용 AI 서비스를 직접 떠안기보다, 시장에서 검증된 AI 서비스를 국민이 일정 수준 이용할 수 있도록 사용권을 제공하는 방안을 검토할 수 있다. 취약계층과 공공서비스 영역에는 특화된 AI 지원을 제공하고, 일반 이용자는 다양한 민간 서비스 중 선택하게 하는 방식이다. 그래야 국민의 접근권도 넓히고 민간의 경쟁도 살릴 수 있다. 대한민국이 IT 강국으로 도약하는 과정에서 지켜온 중요한 원칙 중 하나가 바로 ‘지원하되 간섭하지 않는다’는 것이다. AI 시대에도 이 원칙은 여전히 유효하다. 모두의 AI가 제 기능을 하려면 무료 제공이라는 구호보다 지속 가능한 구조를 갖추는 것이 먼저다. This article was originally written in Korean and translated by a bilingual reporter with the help of generative AI tools. It was then edited by a native English-speaking editor. All AI-assisted translations are reviewed and refined by our newsroom.
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