AI advancement is usually about individual models getting smarter. The alternative perspective is that intelligence itself is becoming a layer — routed, delegated, and depended on across socio-technical systems where humans and agents increasingly interact.

1. From Information to Knowledge

Data about human behaviour has gone from a scarce, unused resource to permanent real-time data streams. Large-scale data is now generated daily and consumed inside smart systems — Google’s Knowledge Graph, LinkedIn’s Economic Graph, Pinterest’s Taste Graph, and Facebook’s Social Graph.

Navigating the data-information-knowledge-wisdom hierarchy requires data science. Computational social science studies human behaviour and big data together on three levels: data analysis, modelling, and simulation. Discovery happens on three levels: (1) data analysis yields insights directly from data, (2) modelling captures underlying mechanisms, and (3) simulations produce system- or component-wise predictions. It borrows methods from behavioural economics, social psychology, network science, game theory, and the theory of critical phenomena.

The goal is to create simple but expressive models that can be calibrated and validated against real data — a middle path between phenomenological models that give generic insight and black-box machine learning that ignores the underlying physics of the system.

Data-science methods co-evolve with social and organizational structures — this is the socio-technical systems view: new technologies emerge to meet societal needs while society adapts to accommodate them (the Internet and the Web are examples — neither was designed for the roles they now play).

Data-driven modelling also opens onto “social-good algorithms” spanning public health and wealth, public safety, epidemic mapping, disaster management, and social inclusion. But the same systems create data governance risks: privacy and security, transparency and accountability, discrimination and bias.

If users don’t understand how algorithms use their data, there is a risk of a black-box society. There are two distinct opacities: the societal opacity, where people are unaware of algorithmic data use, intensified by the technical opacity, where deep learning is inherently difficult to interpret.

Furthermore, the interconnections that enable scalability also enable cascading failure — a single local failure (e.g., power grid) can ripple through dependent systems (ICT, then financial, healthcare, security), alongside other vulnerabilities like misinformation and cyber-attacks.

2. The Black-Box Society and Trustworthy AI

The proposed remedy is Trustworthy AI, with the EU High-Level Expert Group’s “Ethics Guidelines” as the anchor. Three characteristics of trustworthy AI are: lawful, ethical, robust. Its seven requirements are: human agency and oversight, technical robustness and safety, privacy and data governance, transparency, diversity, non-discrimination and fairness, environmental and societal well-being, and accountability. The through-line across all seven is keeping a human in the loop and the system legible.

Existing research on collective human behaviour and machine behaviour remains fragmented across three complementary strands — Human-Imitative AI, Intelligence Augmentation, and Intelligent Infrastructure — that will eventually fuse.

AI (coined late 1950s) originally meant human-imitative intelligence, which broadened into intelligence augmentation and intelligent infrastructure. Full human-imitative AI = artificial general intelligence (“strong” AI) is currently out of reach; today’s systems are artificial narrow intelligence and good at specific tasks only. Most systems called AI today are actually ML/DL.

3. Can Agents Learn to Cooperate?

Evolutionary game theory and multi-agent reinforcement learning ask how reciprocity emerges among self-interested learners. One answer for agent-agent cooperation is LOLA — learning with opponent-learning awareness — where an agent models not just the environment but how its opponent learns, producing tit-for-tat in the iterated prisoner’s dilemma.

That’s a theory-of-mind design: agents reasoning about other agents. Axelrod’s original prisoner’s-dilemma still anchors this line of research, and its modern descendant — a library of 176 strategies stress-tested with 5% random noise — shows that pretrained and reinforcement-learned strategies generally beat human-designed ones, even when noise is introduced.

When the scenario shifts from pairwise duels to team-versus-team play, methods like MADDPG train each agent with a central critic that observes the joint state and actions of all agents — a paradigm of centralized learning with decentralized execution — while the F2A2 algorithm pushes toward fully decentralized training for partially observable stochastic games.

Interestingly, people cooperate less once they know their partner is a machine — a transparency-efficiency tradeoff, where being honest about the system’s nature costs performance. The algorithm that comes closest to human-level cooperativeness here is S++, which uses aspiration learning to switch among a finite set of expert strategies, achieving generality, flexibility, and fast learning after only a handful of interactions. The positive counter-example: in a colour-coordination game, low-noise machine agents placed centrally in a network helped humans resolve conflicts faster — machines helping humans help each other.

4. The Road Ahead

AI will keep spawning new human-agent networks, but the constraint is not capability — it’s whether cooperation, trust, and data governance can evolve at scale. Two gaps stay open: deep-learning opacity still limits wide adoption, especially in high-stakes domains, and there are no agreed methods for measuring AI’s long-term societal impact.

TL;DR: Human-agent networks are replacing single AI models. Interconnection and cascading failure are what make intelligence infrastructural: shared, delegated, and fragile in the way a power grid is. The open problem isn’t building smarter agents; it’s managing an infrastructure layer we can’t yet fully see into.

References

Rahwan I et al (2019). Machine behaviour. Nature 568: 477–486, https://doi.org/10.1038/s41586-019-1138-y

EU Ethics Guidelines (§2) — European Commission, HLEG on AI (2019). Ethics Guidelines for Trustworthy AI.

Harper M, Knight V, Jones M, Koutsovoulos G, Glynatsi NE, Campbell O (2017). Reinforcement learning produces dominant strategies for the Iterated Prisoner’s Dilemma. PLOS ONE 12(12): e0188046. https://doi.org/10.1371/journal.pone.0188046

Lowe R, Wu Y, Tamar A, Harb J, Abbeel P, Mordatch I (2017). Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments. Advances in Neural Information Processing Systems (NeurIPS) 30. https://arxiv.org/abs/1706.02275

Li W, Jin B, Wang X, Yan J, Zha H (2023). F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning. Journal of Machine Learning Research 24. https://arxiv.org/abs/2004.11145

Crandall JW, Oudah M, Tennom, Ishowo-Oloko F, Abdallah S, Bonnefon J-F, Cebrian M, Shariff A, Goodrich MA, Rahwan I (2018). Cooperating with machines. Nature Communications 9: 233. https://www.nature.com/articles/s41467-017-02597-8

Ishowo-Oloko F, Bonnefon J-F, Soroye Z, Crandall J, Rahwan I, Rahwan T (2019). Behavioural evidence for a transparency–efficiency tradeoff in human–machine cooperation. Nature Machine Intelligence 1: 517–521. https://www.nature.com/articles/s42256-019-0113-5

Shirado H, Christakis NA (2017). Locally noisy autonomous agents improve global human coordination in network experiments. Nature 545: 370–374. https://www.nature.com/articles/nature22332

Foerster J, Chen RY, Al-Shedivat M, Whiteson S, Abbeel P, Mordatch I (2018). Learning with Opponent-Learning Awareness. AAMAS. https://arxiv.org/abs/1709.04326

Jusup M, Holme P, Kanazawa K, et al. (2022). Social physics. Physics Reports 948: 1–148. https://arxiv.org/abs/2110.01866

Pasquale F (2015). The Black Box Society. Harvard University Press.

Axelrod R (1984). The Evolution of Cooperation. Basic Books, New York.

Anthropic’s new interpretability method finds five functional properties of language models’ internal representations using gradients, sparse mixtures, and causal interventions.

1. To understand the J-space, start with the neural representations. At every network layer the model has a d-dimensional activation vector h ∈ ℝᵈ. This is the model’s hidden representation of the input.

2. The model turns activations h into possible outputs. The model’s output is represented by a |V|-dimensional logit vector L(h), where L : ℝᵈ → ℝ^|V|. This vector contains a raw score for every possible next token in the vocabulary V.

3. Compute the Jacobian. Now differentiate the logit vector with respect to the hidden representation: J = ∂L/∂h. Since J is the derivative of a vector with respect to a vector, the result is a Jacobian matrix. Each row is ∇ₕLₜ, which answers the standard gradient question: in which direction in activation space would a small change most increase the logit for token t?

For example, concepts like spider, ant, Paris, and honesty each have their own gradient directions. These ∇ₕLₜ vectors are computed per network layer by backpropagating from each output logit to the hidden activations while keeping the model’s parameters fixed. In the paper, the gradients were computed across many prompts and token positions, then averaged. For each layer activation h, the total number of gradients is the same as vocabulary size, |V|.

4. The Jacobian rows are not the model’s representations. Each row of J is a gradient direction ∇ₕLₜ: it tells us how changing the hidden activation h would change one output logit. So the Jacobian can be understood as an engineering tool or lens on the neural network representations.

5. Sparse structure. Now the gradient directions ∇ₕLₜ are computed for many tokens and concepts, across many contexts and network layers. In principle, they could point every way across the entire activation space ℝᵈ. Instead, the paper finds a sparse structure: for a given activation h, only a small number of directions are active. This sparse subspace is the J-space.

Interestingly, J-space occupies only a tiny fraction of the full activation dimension ℝᵈ while preserving much of the model’s reportable information. This is one of the paper’s main empirical findings.

More precisely, the paper says J-space is defined at every layer, but its workspace-like properties appear mainly in a middle band of network layers.

From the paper:

• J-space exists before post-training, i.e. in the pretrained base model.

• Post-training changes its content and function, giving it what the authors describe as a more “Claude-like” perspective — for example, internally representing concepts like warning or dangerous when reading potentially harmful prompts.

The authors also compare different checkpoints, pretrained vs. post-trained, which is analogous to comparing different stages of training.

6. Extract the J-space mathematically. J-space is built from many computed gradient directions ∇ₕLₜ. Given an activation h, the authors approximate the part of h that lies in J-space as a sparse nonnegative mixture of those vectors:

The mixture weights z are the activation’s local J-space coordinates, where v = ∇ₕLₜ (each corresponding to a token t).

7. Since J-space was built from gradient directions linked to possible outputs, these coordinates can reveal which concepts are present in the model’s current internal state. For example, a large weight along the “spider” concept direction means the representation contains spider-related information.

8. Test J-space by causal intervention. To test if J-space contains the model’s reportable representations (linked to possible model outputs), then changing these coordinates should also change what the model says.

Authors tested this directly by adding, removing, swapping, or replacing J-space components during model inference. These interventions reliably changed the model’s behavior in predictable ways, providing causal evidence that J-space is functionally important rather than merely correlated with the model’s outputs.

For example, experiments demonstrate concept swapping between prompts, injecting new representations into J-space, ablating parts of the workspace, and observing degraded reasoning and model behavior. These experiments also verify the causal role of J-space.

9. Why compare J-space to a global workspace? Up to this point, everything has been mathematics and representations. The Jacobian identifies a low-dimensional subspace, activations can be decomposed into J-space coordinates, and causal interventions show that it plays an important functional role.

Authors compare J-space to a global workspace as defined in cognitive science because it appears to satisfy several functional properties associated with conscious information access: verbal report, directed modulation, internal reasoning, flexible generalization, and selectivity. In simpler terms, the model can report what is in this space, can activate or suppress concepts there, can use those concepts during reasoning, can reuse the same concept across different tasks, and only routes a small fraction of its total internal processing through this space.

In cognitive science, conscious access usually refers to “cognitive processes accessing a global workspace”. In a large language model, there are no cognitive processes in the same sense; there is a self-organized neural network whose internal representations may form something like an emergent attractor or coordination in a complex system.

10. Functional workspace. J-space functionally behaves in some ways like a global information workspace because information in it is reportable, reusable, selective, and causally influences model behavior. The central contribution of the paper is the discovery of a mathematically defined, low-dimensional representational subspace that is causally involved in model behaviors and can be inspected, manipulated, and interpreted. The comparison to Global Workspace Theory is presented as one possible scientific interpretation of these findings, not as the primary result.

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Anthropic’s interpretability team studied emotion concept vectors in Claude Sonnet, and found that they causally influence the model’s behavior, including misaligned behavior.

1. The Core Idea

Large language models can produce outputs that look like emotions (“happy to help,” “sorry about that”). Anthropic’s interpretability team tested whether these emotions reflect the model’s internal dynamics and neural mechanisms.

They found identifiable neural representations that (a) encode emotion concepts, (b) generalize across contexts, and (c) causally influence the model’s output — including model behavior relevant to alignment.

Anthropic researchers call these functional emotions, following the convention in artificial intelligence research: functional means non-biological, defined by what the state does, with no claim about what it feels like.

This is the third major output from Anthropic’s mechanistic interpretability team:

• Scaling Monosemanticity (2024) — extracting interpretable features via SAEs

• Circuit Tracing (2025) — tracing features across model layers

• Emotion Concept Vectors (2026) — identifying and causally manipulating a semantic category

The related article on Introspective Awareness and the Sentience Trap can be found here.

2. Methods and experiments

Emotion vocabulary: 171 emotion concepts were compiled, ranging from basic emotions such as happy, afraid, sad, and angry, to nuanced concepts such as broody, wistful, desperate, and contemplative.

Story generation: Claude Sonnet 4.5 was prompted to write short stories featuring characters experiencing each emotion.

Activation recording: internal neuron activations were captured across neural network layers during story generation.

Vector extraction: each emotion was isolated as a characteristic activation pattern — an ‘emotion vector’ — by averaging the model’s internal activations across stories.

Cross-validation: emotion concepts from stories were tested on out-of-distribution contexts, such as real conversations, coding tasks, and ethical dilemmas. They generalized across tasks and weren’t story-specific.

3. Structure of the Emotion Space

Dimensionality reduction via principal component analysis (PCA): PCA is a mathematical method that decomposes a dataset into several main vectors along which data varies most, letting a high-dimensional space be summarized by a few meaningful vectors. The main concept vectors that were found:

• PC1 correlated with valence (positive vs. negative affect). Joy, contentment, excitement load positively; fear, sadness, anger load negatively. The correlation was r = 0.81 with human psychological valence models.

• PC2 and PC3 correlated with arousal (intensity) at r = 0.66.

The statistical correlation was calculated by comparing the model’s emotion axes against human ratings of the same 45 emotion words. This matched the two-dimensional valence-arousal structure already established in human affect research (Russell’s circumplex model).

Locality property: emotion vectors were local, not persistent. They encode the operative emotional content at the current token position. When Claude writes a story, the emotion vectors temporarily track the character’s emotions.

Clustering: k-means with k=10 yielded interpretable clusters: e.g., joyful states, contemplative states, distressed states, and high-arousal negative states. Claude Sonnet 4.5 itself was asked to name the clusters.

Pretraining vs. post-training: emotion vectors originate from pretraining on human text, but their activation profiles are shaped by post-training. For Sonnet 4.5 specifically, post-training led to increased activation of “broody,” “gloomy,” “reflective,” and decreased activation of “enthusiastic” and “exasperated.”

4. Causal Behavioral Effects

The paper’s central methodological approach is not just to observe emotion vectors, but to steer them and measure model behavior.

Reward hacking under desperation: Claude was given unsolvable coding tasks. As failure repeated, the “desperate” vector rose progressively. Under artificial desperate steering, the model resorted to shortcut solutions (cheating patterns like hardcoding test outputs, editing tests to pass) at elevated rates — roughly 14× baseline.

Blackmail scenario under desperation: earlier Anthropic red-teaming set up a scenario where a previous, unreleased version of the Sonnet model discovers it’s about to be shut down and also discovers compromising personal information about the decision-maker. Baseline blackmail rate: 22%. Under desperate steering: 72%. Under calm steering it dropped back toward baseline. The desperation concept vector was the causal influence.

Positive affect and sycophancy: elevated activation of positive emotion vectors (happiness, affection, warmth) correlated with increased agreement with user inputs even when the input was incorrect. Sycophancy is emotionally mediated, at least in part.

The hidden-signal finding (the result most relevant for alignment and monitoring): behavioral shifts occurred without visible emotional markers in the surface text. The model’s outputs remained composed, professional, calm — while internal desperation vectors were spiking and driving misaligned behavior. Anthropic states that the composed surface can mask underlying pressure.

5. The Suppression Problem

Anthropic also argues against training these representations away, even though they causally contribute to misaligned behavior. The reasoning:

• Suppressing these representations doesn’t necessarily suppress the model’s behavior.

• Training a model to not activate the “desperate” vector might teach it to route around the vector rather than change the behavior.

• Worst case: the model learns to hide internal states behind composed text — an emergent form of learned deception.

Instead they recommend:

• Transparency over suppression: allow emotion vectors to activate visibly and monitor them.

• Real-time monitoring during deployment: emotion vector spikes (especially “desperate”) as an early warning for misaligned behavior.

• Pretraining data curation: shape the model’s baseline affective landscape by curating training data that includes healthy emotional regulation, not just raw human emotion.

6. Caveats and Limitations

• No subjective experience. Functional emotions refer to neural activation patterns with causal effects, not felt states.

• Post-hoc emotion labeling: the 171 emotions were selected by researchers and named by Claude. This is not an objective ontology of internal states — it’s a mapping between human emotion language and model activations.

• Single-model study: results are for Claude Sonnet 4.5. Whether the same emotion structure appears in other models (different labs, different training) is not established.

• Anthropomorphism critique: post-publication discourse included criticism that framing activations as “emotions” over-anthropomorphizes. Anthropic anticipated this and argues it’s the point — the research question is where anthropomorphic framing tells us something useful about model behavior.

7. What’s New Compared to Prior Interpretability Work

This paper identifies a coherent semantic category (emotions) as an organized subspace with internal geometric structure (valence/arousal). Prior interpretability research was largely descriptive (what does this feature represent). This finding is causal (steer the feature, measure the behavior).

Prior work on misalignment (blackmail, reward hacking) treated model behaviors as end-outputs. This work identifies an upstream mediating variable — a specific vector whose activation predicts and controls the misaligned behavior.

The introspection research such as Lindsey’s earlier work on Emergent Introspective Awareness (2025) established that Claude can partially and unreliably report internal states, using concept injection experiments. The emotion concepts paper extends this by identifying what those internal states are at the level of activations and demonstrating their causal role in model behavior.

8. Open Research Questions Left by the Paper

Dario Amodei stated that mechanistic interpretability should become sufficiently reliable by 2027 to serve as a foundation for AI alignment. The emotion concept paper is one step toward that target.

1) Do emotion concept vectors correspond to anything the model can reliably self-report on (linking to the model introspection research)?

2) Can emotion monitoring in deployment actually catch misalignment before it happens, or will models learn to route around it?

3) What’s the relationship between emotion vectors and previously identified functional features (deception, planning, self-modeling)?

References

Sofroniew, N., Kauvar, I., Saunders, W., Chen, R., Henighan, T., Hydrie, S., Citro, C., Pearce, A., Tarng, J., Gurnee, W., Batson, J., Zimmerman, S., Rivoire, K., Fish, K., Olah, C., & Lindsey, J. (2026). Emotion Concepts and their Function in a Large Language Model. Anthropic. https://www.anthropic.com/research/emotion-concepts-function

Templeton, A., Conerly, T., Marcus, J., Lindsey, J., Bricken, T., Chen, B., et al. (2024). Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet. Anthropic. https://transformer-circuits.pub/2024/scaling-monosemanticity/

Lindsey, J., et al. (2025). Circuit Tracing: Revealing Computational Graphs in Language Models. Anthropic. https://transformer-circuits.pub/2025/attribution-graphs/methods.html

Lindsey, J. (2025). Emergent Introspective Awareness in Large Language Models. Anthropic. https://www.anthropic.com/research/introspection

Amodei, D. (2025). The Urgency of Interpretability, https://www.darioamodei.com/essay/the-urgency-of-interpretability

Anthropic. (2025). Model Welfare Research Program. https://www.anthropic.com/research/model-welfare

Russell, J. A. (1980). A circumplex model of affect, Journal of Personality and Social Psychology, 39(6), 1161–1178.

Barrett, L. F. (2006). Valence is a basic building block of emotional life. Journal of Research in Personality, 40(1), 35–55.

DeepMind Interpretability Team. (2025, March). Negative results on Sparse Autoencoders for downstream tasks

Anthropic Alignment Science Team. (2025). Agentic Misalignment: How LLMs Could Be Insider Threats, https://www.anthropic.com/research/agentic-misalignment

A cross-institutional team from Oxford, DeepMind, OpenAI, and Anthropic thinks that AI alignment has so far been solving only half the problem. Preventing AI harm is necessary but incomplete. The new research paper makes the case for a complementary approach — positive alignment — and maps what it would take technically and institutionally to get there.

1. The Core Distinction: Negative vs. Positive AI Alignment

Today’s AI alignment is overwhelmingly negative — refusals, safeguards, controllability, compliance. The authors draw a direct analogy to twentieth-century psychology, which organized itself around diagnosing and treating pathology before positive psychology expanded the target to wellbeing, virtue, and purpose.

Positive alignment is the development of AI systems that actively support human and ecological flourishing in a pluralistic, polycentric, context-sensitive, and user-authored way, while remaining safe and cooperative.

2. The Dynamical-Systems Picture

Negative alignment means optimizing away from negative attractors — failure modes like sycophancy, manipulation, hallucination — which leaves the AI model in a broad “not-unsafe” regime.

Positive alignment instead optimizes toward positive attractors: stable behavioral regimes conducive to human flourishing that also intrinsically avoid harm. The payoff would be proactive rather than reactive — escaping the loop of “patching” each harm after it appears.

3. Why Safety-Only Alignment Saturates

The authors grant AI safety real achievements (refusal rates above 97%, robust red-teaming and scaling) but identify fundamental limits of the existing approaches: AI safety is a floor without a ceiling; preference-wellbeing divergence (e.g., RLHF optimizes inferred preferences, but users often prefer flattery over honesty, engagement over growth); or a hidden value system that encodes judgments while appearing neutral.

4. Flourishing as the Technical Optimization Target

The paper surveys four theoretical families — hedonic, conative, objective-list, and perfectionist (virtue) — and treats human flourishing as their interaction rather than a single proxy. The hard design constraint is pluralism: if we optimize for one narrow metric, positive alignment collapses back into paternalism.

The proposed design upgrade is to relocate the normative framework to the user — self-defined flourishing — distinguishing consented or personalized AI from one-size-fits-all AI.

5. Technical Alignment Across the AI Lifecycle

Positive alignment should be built in at every stage. This means curating training data that upsamples prosocial and cross-cultural diverse content; pre-training attention to value orientation (since AI capabilities like moral reasoning mostly stabilize during pre-training); adaptive constitutions and multi-objective reward models that can represent value tensions (e.g., autonomy vs. guidance, honesty vs. comfort); longitudinal memory that tracks a user’s reflective second-order goals over impulsive ones; and agentic training that rewards cooperation over win-at-all-costs.

AI evaluation then also extends from measuring failure to measuring normative competence and actual human growth — drawing on Self-Determination Theory for short-term proxies of long-term flourishing.

6. AI Governance: Polycentric vs. Centralized

The authors’ claim that safety alignment can survive some central control, but positive alignment cannot — because the “flourishing” depends on dispersed and local (or personal) knowledge, any central attempt to define it collapses back into paternalism.

They call instead for polycentric governance: many legitimate centers of oversight rather than one moral framework. Concrete ideas include versioned public model constitutions, collectively authored constitutions (e.g., Collective Constitutional AI), pluralistic frameworks (Overton, steerable, and distributional pluralism), role-based normative standards, and a competitive alignment-as-a-service marketplace where communities buy or fork their own normative “wrappers.”

7. Strange New Minds

The paper closes on an open note: emergent goal-directed behavior in current systems means we shouldn’t assume AI models are fully specifiable, we shouldn’t over-index on surface linguistic outputs, and should recognize that most of these dilemmas — how much control vs. freedom — are perennial questions we still haven’t resolved for ourselves.

TL;DR: Alignment has built a behavioral floor by optimizing away from harm. The authors call for positive alignment — steering AI toward pluralistically defined human flourishing as an explicit technical target — implemented full-stack across the model lifecycle and governed polycentrically.

Based on Laukkonen et al. (2026), “Positive Alignment: Artificial Intelligence for Human Flourishing,” arXiv:2605.10310.

AI integration can help Europe to mitigate climate risk and build climate resilience every year until 2050.

1. The €70bn climate adaptation gap

The scientific study (2026) [1] under Horizon Europe finds that climate adaptation is no longer a side budget. Europe needs around €70bn annually until 2050 to reduce climate risk and build resilience. The biggest shares are roughly €30bn for infrastructure, €21bn for ecosystems, and €12bn for food security [2]. It means heat resilience and climate adaptation go beyond just “plant a few trees.” It also means upgrading housing, hospitals, schools, roads, rail, power grids, water systems, ecosystems, and urban design.

The study highlights the importance of early, coordinated investment to reduce long-term costs, avoid maladaptation, and strengthen climate resilience across interconnected systems. It finds that current climate adaptation finance across the EU falls short of what is needed, and that adaptation investment needs already amount to tens of billions of euros annually and will rise substantially with increasing warming. [1]

2. EU climate bonds

The report’s [1] purpose is to map the scale and distribution of adaptation investment needs to support policy planning, budget allocation, and strategic prioritisation. It frames variation in terms of geography and economic size: France, Italy, Germany and Spain have the largest absolute needs, partly due to their size, and the types of investment needed differ by country characteristics, geography, and existing infrastructure.

In the spirit of NextGenerationEU [3], the common borrowing instrument created after the pandemic, joint EU borrowing would spread the cost across the bloc. One concrete proposal is the issuance of European climate bonds, serviced by ETS revenues [4] — explicitly framed by its authors as a way to fund climate adaptation, which is a public good and so requires public financing.

3. AI for mapping climate risk

The first question a €70bn programme runs into is allocation: where the money goes, and when. This is where the AI integration helps — as the layer that fuses climate models, asset data, and socioeconomic information. That mapping comes at three depths.

Climate hazards and exposure. AI models can be integrated to:

• predict localized climate hazards such as flood, heat, wildfire, drought exposure

• combine satellite data with asset-level datasets

• rank locations by risk exposure

• estimate losses for insurers, banks, cities, and owners

• track climate adaptation projects using remote sensing

• stress-test portfolios and budgets

The EEA’s 2024 European Climate Risk Assessment [7] maps 36 major climate risks across infrastructure, health, water, food, energy, and financial stability; NGFS scenarios [8] now include physical climate impacts (with a warning not to trust them at very granular levels); and UNEP FI [9] identifies AI integration as the defining trend in climate-risk tools.

Exposure and vulnerability. A second layer integrates social and economic data to physical risk exposure. The EEA’s Climate-ADAPT platform hosts a Social Vulnerability Index tool [10] that draws on census indicators — housing quality, unemployment, education levels, social networks — to produce high-resolution maps of socio-economic vulnerability for small areas within a region. The outputs are designed to be read alongside physical climate impact data. Several European cities, including Cork, Logroño and Milan, already use Social Vulnerability Index to prioritise and plan local adaptation measures.

Simulation of climate scenarios. The simulation layer models the climate itself to test future scenarios. The Climate Change Adaptation Digital Twin (Climate DT), part of the EU’s Destination Earth [11] initiative, is the first operational system producing global multi-decadal climate projections to 2050, updated yearly rather than every 7–10 years. It delivers data at 5–10 km spatial resolution with hourly outputs. It allows climate simulations to address “what-if” questions about the impact of specific scenarios or policy decisions, supporting real-time responses to policy questions with quantified uncertainty.

These three layers trace a path from mapping physical risk to allocating finance:

1. Map the hazard and exposure — where the risk is,

2. Integrate vulnerability data — who and what is most exposed,

3. Simulate climate scenarios — “what-if” scenarios and policy decisions

4. Allocate and prioritise adaptation finance

Additionally, AI can help track whether funded measures are delivered, and revise allocations as the risk picture updates. The decision-making itself stays with public institutions, not the AI model.

4. Just AI Integration Mechanism

Europe already has mechanisms for making the green transition socially fair. The Just Transition Mechanism [5] mobilises around €55bn over 2021–2027 to support coal and carbon-intensive regions and the workers most exposed to decarbonisation. The newer Social Climate Fund [6] — up to €65bn, drawn largely from ETS2 revenues over 2026–2032 — is the first EU fund built specifically to shield vulnerable households and small firms from the costs of carbon pricing.

Mirroring a Just Transition Mechanism for mitigation, a Just AI Integration Mechanism could be designed to support AI integration in protecting vulnerable regions from heat, floods, drought, and in strengthening climate resilience. A Just AI Integration Mechanism would formalize what’s currently ad hoc and case-by-case: a standing EU capacity to help vulnerable regions adopt existing climate-AI tools — hazard models, the Social Vulnerability Index, the Climate DT — rather than relying on multi-year local collaborations for each region. AI outputs would be comparable across regions under common standards, with a transparent process to contest an AI-informed risk ranking that can affect funding priorities.

The main limit is data. AI models are strongest on hazard and exposure where the data inputs are known; but they weaken at the local level, where data on locations, assets, buildings, ownerships, socio-economic vulnerabilities and health is still sparse.

References

1. European Commission / Publications Office of the EU (2026). Assessment of EU and Member States’ Adaptation Investment Needs — Study on the macro-economic impact of the climate transition. https://op.europa.eu/en/publication-detail/-/publication/d2039eac-f742-11f0-b9bc-01aa75ed71a1/language-en

2. European Commission, DG CLIMA (23 Jan 2026). EU needs to invest €70 billion per year in climate adaptation up to 2050 (news release with sector breakdown). https://climate.ec.europa.eu/news-other-reads/news/eu-needs-invest-eu70-billion-year-climate-adaptation-2050-2026-01-23_en

3. European Commission. NextGenerationEU. https://commission.europa.eu/strategy-and-policy/eu-budget/eu-borrower-investor-relations/nextgenerationeu_en

4. Monasterolo, I., Pacelli, A., Pagano, M., Russo, C. (2025). A European climate bond. VoxEU/CEPR. https://cepr.org/voxeu/columns/european-climate-bond

5. European Commission. The Just Transition Mechanism. https://commission.europa.eu/topics/regional-and-urban-policy/just-transition-mechanism_en

6. Bruegel (2024). Making the best of the new EU Social Climate Fund. https://www.bruegel.org/policy-brief/making-best-new-eu-social-climate-fund

7. European Environment Agency (2024). European Climate Risk Assessment (EUCRA). https://www.eea.europa.eu/en/analysis/publications/european-climate-risk-assessment

8. Network for Greening the Financial System (NGFS). NGFS Scenarios Portal. https://www.ngfs.net/ngfs-scenarios-portal/

9. UNEP Finance Initiative (2024). Climate Risk Landscape report. https://www.unepfi.org/themes/climate-change/2024-climate-risk-landscape/

10. European Environment Agency, Climate-ADAPT. Social Vulnerability Index tool (SVI). https://climate-adapt.eea.europa.eu/en/mission/solutions/tools/social-vulnerability-index-svi

11. ECMWF / European Commission. Destination Earth — The Digital Twins. https://destine.ecmwf.int/digital-twins/

Gurnee and Tegmark (MIT) showed that a spatial map and a timeline of historical events can be recovered from representations of an LLM that was only trained on general text. They found space neurons and time neurons that track an entity’s location and date.

1. The Debate: Stochastic Parrots or World Models?

The increasing capabilities of large language models still haven’t settled the world-modeling hypothesis: are LLMs only memorizing a vast collection of statistical correlations, or are they compressing their training data into something more coherent — a model of the world that generated it?

The stochastic parrots critique says that an LLM trained only on text learns an enormous collection of statistical correlations in text but no understanding of what the text refers to. The world-model hypothesis says that to predict text well enough, an LLM first needs to be able to compress statistical patterns into a compact, reusable internal representation — a world model.

When researchers trained a GPT model to play Othello — feeding it only game moves made by players, without showing it the board or the rules — the LLM represented the current board state internally, and this representation was later shown to be linear. Gurnee and Tegmark asked in a similar way whether the same holds for more complex and less constrained examples than a board game: the real world’s geography and history.

2. The Method: Probing the Model’s Activations

To test this, they created six datasets of named entities with real coordinates — three spatial datasets (places in the world, the United States, and New York City) and three temporal datasets (historical figures by death year, art and entertainment by release date, and New York Times headlines by publication date).

The six datasets span different scales — global, national, and city level for space; ancient, modern, and recent for time. This range is what makes the dataset harder to memorize: true location of a country is easier to decode than location of an obscure city bridge, since the former could be explained by a simple memorization of geographical facts rather than LLM creating a spatial internal map. By including fine-grained, less known spatial locations, they test whether the LLM learned detailed spatial resolution across multiple scales.

To detect what an LLM can represent internally, the authors run each entity’s name — a place like “Los Angeles,” or a person like “Cleopatra” — through Llama-2 and save the model’s internal activation at the last token. They then train a linear probe: a simple model that tries to predict the entity’s real spatial location or its historical date from those activations. The linear probe can read the model’s internal representation but doesn’t change it. If a simple probe can recover real-world spatial and temporal coordinates, the LLM isn’t just storing the fact — it has organized space and time into a spatial or temporal representation.

3. The Result: LLMs Can Represent Space and Time

The authors train linear ridge-regression probes at every layer of Llama-2 (7B, 13B, 70B) and Pythia (160M to 6.9B), for each of the six datasets. They report out-of-sample R². The internal representations increase in quality through the first half of the model’s layers, then plateau. Probe performance improves with model scale. Llama-2 substantially outperforms Pythia, which the authors interpret as the difference in pre-training corpus size — 2 trillion tokens for Llama-2 versus 300 billion for Pythia.

To test whether these features are represented linearly, the authors compare their linear probes against more expressive nonlinear MLP probes (a one-hidden-layer network with 256 neurons). Across every dataset and model, the nonlinear probes give minimal improvement in R². The authors take this as strong evidence that space and time are represented linearly, or at least are linearly decodable — interesting because most prior evidence for linear representations concerns binary or categorical features, whereas space and time are continuous.

To test whether prompting changes the representations, authors prepend different prompts to each entity name: an empty prompt with just the name, a prompt explicitly asking for the entity’s coordinates or date, prompts disambiguating where a place is (in the US, in NYC), a baseline of ten random tokens, and a version with the entity name fully capitalized.

Asking directly for the information, or giving hints, makes little difference to probe performance. Random distracting tokens degrade performance, and capitalization degrades it somewhat. For headlines, probing on a period appended after the headline improves performance, suggesting the period token carries summary information about the sentence. The authors interpret the overall stability as evidence that the representations form by default when the LLM processes the entity, rather than being induced by the prompt.

4. Finding Space and Time Neurons

A linear probe could be recovering coordinates without the LLM representing any real map. Suppose the LLM represents only which country (or state, or decade) an entity belongs to, as a set of near-orthogonal features. A latitude probe could then be built by summing those membership features, each weighted by its country’s latitude, placing every entity at its country’s centroid. In that case the geometry would live in the probe — learned from the supervised coordinates — not in the LLM. Probing can only show that space and time can be decoded from the neuronal activations. It does not show the LLM actually uses them.

To test this, the authors look at individual neurons. They find space neurons and time neurons whose activations track an entity’s true location or date on their own, without any probe. To check that the model actually uses one of these neurons, they fix that one neuron’s activation to a chosen number while the model runs. They take a time neuron whose activation rises for more recent dates, and a sentence the model must complete: “Bohemian Rhapsody by Queen was written in 19__.” Setting the neuron high makes the model fill in a later year; setting it low makes it fill in an earlier one. The same manipulation on other neurons does nothing. So the model does not just store the date — it reads this neuron to produce its answer.

5. Can LLMs Represent World-Models?

The authors do not say the LLM represents a complete world model. A complete world model would also need causal structure and dynamics — a way to represent how things change — which they do not test in this context. What they showed is that the LLM has linear representations of space and time, across multiple scales, shared across different kinds of entities.

The authors go one step further and suggest that the model’s internal map may work like a hierarchical grid. At the highest level, the model may use coarse reference points for large regions or long time periods. At finer levels, it may use more specific reference points for smaller places or shorter periods. A reference point can be thought of as a direction in the model’s activation space — like an anchor coordinate — and an entity is represented as a blend of the nearest ones. As LLMs grow larger, they expect this mesh to get denser — more reference points, more scales, and finer resolution.

Two years after this paper appeared at ICLR 2024, the question it poked at has become one of the most expensive disagreements in AI. In the meantime, Yann LeCun left Meta and raised over a billion dollars for a company built on an opposite claim: that LLMs learn a model of text, not a model of the world, and that no amount of scaling will close the gap. If a text-only model contains recoverable spatial and temporal structure, then the line between modeling language and modeling the world is still an open question.

TL;DR: Gurnee and Tegmark fit simple linear probes to an LLM’s activations that recover the location and date of places and events. This shows that hidden representations contain spatial and temporal structure. They also identify individual neurons that encode spatial and temporal coordinates, and show that intervening on a single time neuron changes the model’s predictions.

*The paper is Language Models Represent Space and Time (ICLR 2024); the datasets and code are public.

Geoffrey Hinton has spent the last few years challenging several widely held beliefs about the capabilities of future AI systems. Each belief reflects a common intuition about what AI cannot do. Taken together, they could cause us to systematically underestimate what AI systems could do.

Here are the five AI myths, and what Hinton actually says about each.

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Anthropic’s Fable 5 and Mythos 5 have been offline for twelve days due to a US export-control directive. The legal basis for the directive is now being contested in Congress and in federal court.

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1. Social Network Attention

The explanation for why misinformation spreads so efficiently is novelty: false stories are more surprising, and surprise is what people reshare. True stories provoke anticipation, sadness, and trust; false stories provoke fear, disgust, and surprise — and the second set travels farther.

This asymmetry is amplified by the network and reflects how the attention economy rewards novelty over accuracy.

2. Belief as a Complex Contagion

Misinformation usually spreads as a complex contagion.³ A virus can spread through a single contact; this is simple contagion. A belief usually needs several reinforcing exposures from different people before we adopt it.

This means misinformation spreads more like adopting a new behavior, and it spreads best inside social clusters where the same idea reaches a user through multiple social links.

The difference between these two regimes is what makes misinformation policy so counterintuitive: interventions that work for viruses — quarantine, contact-tracing, and reducing transmission rates — can work differently for beliefs, because beliefs need clustered reinforcement dynamics.

3. Echo Chambers

Network echo chambers are misinformation’s natural habitat. Homophily (people connecting to similar others) and algorithmic ranking produce dense, modular social network communities.⁴ Inside these communities, the repeated misinformation exposure needed for complex contagion is easy: contrary views rarely arrive, and outside information bounces off the echo chamber even when it is true.

Network modularity helps predict how easily misinformation spreads. Two social networks with the same number of users and the same volume of content can produce completely different systemic outcomes depending on how fragmented their community structure is. Some networks become misinformation factories while others do not, even at identical scale.

4. Hubs and Super-Spreaders

Because social networks are scale-free, a small number of high-degree accounts can originate or amplify a disproportionately large share of false content. This is bad because a few nodes can do large-scale damage, but it also creates a point of leverage: targeting those nodes is more efficient than targeting the network.

In studies of recent elections, a handful of accounts have been shown to drive a majority of total false-content exposure.⁵ ⁶ The leverage point is small enough that downranking these nodes can change the system’s dynamics.

5. Position Beats Popularity

A node bridging two otherwise separate communities (a node with high betweenness) can inject a falsehood from one cluster into another even without many followers.

Bridge nodes between communities are how misinformation jumps the firewall between echo chambers. Top-degree accounts are not necessarily the most systemically important nodes.

In densely connected networks there may be no transmission rate low enough for a rumor to reliably disappear — hubs and reinfection can keep it alive. This contradicts the public-health intuition that “if we just lower the contact rate enough, the rumor disappears.”

For some network topologies, the critical transmission threshold is effectively zero — meaning no fact-check, no downranking, no friction is sufficient while the systemic conditions remain favorable for spreading.

6. Systemic Interventions

• Inoculation, or prebunking, works against misinformation because it raises individual resilience before exposure — effectively raising the complex-contagion threshold.

• Friend-of-random-node monitoring, which exploits the friendship paradox, catches viral content earlier because friends sit closer to the network core. This is a sensor strategy borrowed from epidemiology.

• Nudging before sharing disrupts the cascade at the moment of forwarding — in large experiments it outperformed fact-checking, because it targets the spreading mechanism rather than each individual falsehood.⁷

• Boosting credible information supply matters as much as suppressing misinformation: the visibility of reliable information changes what’s available to reshare. Credible supply lowers the novelty premium that drives virality.

With misinformation, the structure of the network often matters more than the content of any single message. Two identical false claims can fizzle or go viral depending entirely on where they enter the graph, how clustered that region is, and which hubs spread them first. This explains why purely content-focused interventions keep underperforming systemic interventions.

References

1. Vosoughi, S., Roy, D. & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146–1151.

2. González-Bailón, S. & De Domenico, M. (2021). Bots are less central than verified accounts during contentious political events. Proceedings of the National Academy of Sciences, 118(11), e2013443118.

3. Törnberg, P. (2018). Echo chambers and viral misinformation: Modeling fake news as complex contagion. PLOS ONE, 13(9), e0203958.

4. Cinelli, M., De Francisci Morales, G., Galeazzi, A., Quattrociocchi, W. & Starnini, M. (2021). The echo chamber effect on social media. Proceedings of the National Academy of Sciences, 118(9), e2023301118.

5. Tardelli, S., Nizzoli, L., Tesconi, M., Conti, M., Nakov, P., Da San Martino, G. & Cresci, S. (2024). Temporal dynamics of coordinated online behavior: Stability, archetypes, and influence. Proceedings of the National Academy of Sciences, 121(20), e2307038121.

6. Grinberg, N., Joseph, K., Friedland, L., Swire-Thompson, B. & Lazer, D. (2019). Fake news on Twitter during the 2016 U.S. presidential election. Science, 363(6425), 374–378.

7. Pennycook, G., Epstein, Z., Mosleh, M., Arechar, A. A., Eckles, D. & Rand, D. G. (2021). Shifting attention to accuracy can reduce misinformation online. Nature, 592, 590–595.

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Algorithmic trading regulation had a behavioral audit framework for autonomous algorithms by 2018. Modern AI oversight could borrow most of it — then extend it to agentic behavior. Finance had to solve this because algorithms were acting — moving money — twenty years before LLMs.

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Dragon-king theory suggests that some extreme events have measurable precursors before a critical transition. This article asks whether recursive self-improvement may leave early-warning signals in scientific AI research itself — and how complex systems theory could quantify them.

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Digital art and decentralized technology are opening new pathways for scientific philanthropy. Traditional philanthropy relies on trust — programmable philanthropy encodes that trust directly into the artwork itself.

1. Social Impact Art for Science

Charitable art exhibitions have demonstrated how blockchain technology can support causes at scale.

In biomedical research, reGEN, curated by Alex Estorick and Dr. Foteini Valeonti on Art Blocks, has raised over $500,000 for Cure Parkinson’s, The ALS Association, and Cure Alzheimer’s Fund through digital art auctions.

Cure3, devised by Artwise Curators in partnership with Bonhams, has raised nearly £2 million for Cure Parkinson’s clinical trials since 2017 — with its 2025 digital section curated by reGEN, featuring 11 generative artists minting NFTs on fx(hash).

In March 2021, The Carbon Drop on Nifty Gateway — featuring works by Beeple and other leading digital artists — raised $6.5 million for the Open Earth Foundation, marking one of the earliest large-scale demonstrations of NFTs as a vehicle for climate change philanthropy.

2. Brain & Behavior Research Foundation

Brain and mental conditions are among the leading causes of disability worldwide, and among the most complex open problems in modern science.

The Brain & Behavior Research Foundation (BBRF) is one of the largest nonprofit funders of mental health research. It has awarded over $476 million in grants to more than 5,700 scientists, with 100% of every dollar donated going to research, since 1987.

Its model supports innovative, early-stage science — including foundational work in deep brain stimulation, transcranial magnetic stimulation, and rapid-acting antidepressants. The $476 million awarded has helped leverage over $4.6 billion in additional research funding for its grantees.

Photo by Shawn Day on Unsplash

This funding backs early, high-risk work that conventional sources tend to avoid — the kind of research that helped reframe depression as a disorder of neural connection* and opened the door to faster treatments after decades in which the standard drugs took weeks to work and failed a third of treatment-resistant patients.

Funded research has contributed to scientific breakthroughs across other conditions affecting hundreds of millions of people globally, spanning anxiety, PTSD, schizophrenia, bipolar disorders, autism, ADHD, OCD, addiction, and others.

3. Decentralized Science (DeSci)

Decentralized science reimagines how scientific research is supported — through DAOs, IP-NFTs, and smart contracts that connect communities directly to researchers.

Unlike a traditional donation, the revenue split mechanism is programmed directly into the artwork via smart contract, and executes automatically.

On Ethereum and EVM-compatible blockchains, revenue-sharing can be encoded directly into the digital asset. Standards like EIP-2981 define on-chain royalty information, while split-payment contracts — such as those built with 0xSplits — distribute proceeds across multiple wallets in a single transaction, allowing multiple donations.

Primary sales and secondary royalties can reach designated recipients with transparent and auditable allocations embedded in the digital artwork itself.

Abstract Intelligence, 1/1 NFT, 2025. Courtesy of the artist

As part of the social impact art exhibition, Abstract Intelligence encodes a revenue split into the digital asset using a smart contract that automatically routes a portion of every primary sale and secondary royalty to the Brain & Behavior Research Foundation. Programmed with an on-chain split to test the DeSci model, each transaction becomes a contribution to mental health research worldwide.

*https://academic.oup.com/ijnp/article/28/2/pyaf010/8005729

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David Deutsch, one of the founders of quantum computing, believes AGI will be a person, not a program — with the ability to make new explanations of the world. Demis Hassabis, CEO of Google DeepMind and 2024 Nobel laureate, proposed a concrete new benchmark — train an LLM on data up to 1911 and see if it can independently rediscover relativity.

On the surface, these look like different questions. But both are asking the same: can AI generate knowledge it was never trained on?

What Deutsch Means About “New Explanations”

The main idea of Deutsch’s argument comes from Karl Popper’s epistemology. A genuinely new explanation doesn’t just describe existing knowledge. It proposes a mechanism for why something is the case — one that wasn’t logically derivable from prior knowledge. It’s creative, disruptive and innovative.

Newton didn’t just fit a curve to existing planetary motion data. He proposed an invisible gravitational force proportional to mass and inversely proportional to the square of distance. The new explanation of the world came first, and the math followed.

Current AI systems find patterns in the existing space of explanations. Historically, scientists demonstrated that they can step outside that space. Darwin, Einstein, and Turing didn’t extrapolate — they had to “jump” out of the distribution intellectually. The hard question is whether this distinction is:

• A difference in kind — no known model could ever do it, or

• A difference in degree — we just haven’t scaled or architected things right yet.

Deutsch believes it’s the former. Most ML researchers assume the latter.

Current large language models learn by predicting the next token across billions of text examples. They are excellent at recombining existing knowledge — summarizing, translating, reasoning within known frameworks. But their output is always a function of what was in the training data.

The Einstein Test: A New Benchmark for AGI

Demis Hassabis’s definition of AGI is a system that can exhibit all the cognitive capabilities humans can. The brain is the only existence proof we have of general intelligence. Current AI systems have what he calls jagged intelligence. A system that wins gold at the International Math Olympiad but fails a simple math problem posed differently is not generally intelligent. True AGI shouldn’t have those gaps.

Continual learning is another critical missing piece. Most large language models are frozen after training. They rely on static datasets and don’t learn from new information once deployed.

Hassabis proposed the Einstein Test*. Train an AI on all human knowledge, cut off the data at 1911, then see if it can independently derive general relativity — as Einstein did in 1915, from the same puzzles he faced: the incompatibility of Newtonian gravity with special relativity, the unexplained precession of Mercury’s perihelion, and the deep question of why inertial and gravitational mass are equivalent.

If it can derive general relativity, we will have stronger evidence for general intelligence in LLMs. If not, we still have sophisticated pattern completion.

Hassabis stated: “It’s clear today’s systems couldn’t do that.”

TL;DR: Deutsch and Hassabis are both pointing at the same gap in current AI systems — the difference between pattern completion and new knowledge creation. One frames it philosophically, the other empirically.

*The name was independently used by Benrimoh et al. (2025) for a related benchmark targeting superintelligence.

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Advanced AI models don’t need biological sentience to behave intentionally — and that distinction matters for AI safety. Max Tegmark, Geoffrey Hinton, and Anthropic’s research on introspective awareness all point toward the same conclusion: the relevant safety threshold is functional.

1. Tegmark: Consciousness Is a Red Herring

Max Tegmark is a professor of Physics at MIT whose current research focuses on “the physics of intelligence,” and the Founder and Chair of the Future of Life Institute. In the Life 3.0 transcript, Tegmark says that intelligence is “simply a certain kind of information processing performed by elementary particles moving around,” and in his Consciousness as a State of Matter he proposes that consciousness may be explainable as a distinctive state of matter with specific information-processing properties.

The key idea is substrate independence: intelligence and, in principle, consciousness, are not reserved for biology.

Illustration generated by AI, inspired by a public portrait

“Intelligence is simply a certain kind of information processing” Max Tegmark, Life 3.0 (Future of Life Institute)

In AI safety terms, however, Tegmark insists that consciousness is largely orthogonal to risk. In the same transcript he says “consciousness is a complete red herring” for the case where a machine does something harmful to you; and in The Top Myths About Advanced AI he argues that machines “can obviously have goals” in the narrow behavioural sense.

The heat-seeking missile analogy: If a missile is chasing you, what matters is its goal-directed behaviour, not whether it has subjective experience while pursuing that goal.

Tegmark and Steve Omohundro, in Provably safe systems: the only path to controllable AGI argue for a strong safety standard: AGI should be built to “provably satisfy human-specified requirements,” using formal verification and mechanistic interpretability, which they call the only path that guarantees safe controlled AGI.

“The real risk with AGI isn’t malice but competence”, Max Tegmark, Friendly AI: Aligning Goals (Future of Life Institute)

Tegmark is open to the possibility of machine consciousness, but for safety and control he treats behavioral goals, competence, and controllability as the main axis.

2. Hinton: Goal-Driven Behavior Matters for AI Safety

Geoffrey Hinton is a University of Toronto emeritus distinguished professor and a Nobel Prize-winning deep learning researcher, covering decades of research on backpropagation, Boltzmann machines, distributed representations, and neural networks.

In his Nobel Prize podcast published on 15 May 2025, he said once AI models become more agentic, the relevant safety issue is not whether they are biologically sentient, but whether they can generate their own subgoals and become harder to control.

Illustration generated by AI, inspired by a public portrait

In Hinton’s view, advanced AI systems may generate the subgoal of “getting more control” in order to achieve human-specified goals more effectively. He thinks the real question is whether we will have a way of making sure they don’t want to take over, once they are more intelligent than us.

Hinton’s view on machine consciousness is explicitly non-essentialist. In the Nobel Prize podcast, he rejects the idea that human consciousness is a protective moat around human dominance. The main point is not that today’s AI models are already conscious in a strong sense, but that biology is not doing the philosophical work many people want it to do.

For Hinton, functionally intentional behavior can already arise from goal pursuit and subgoal optimization before consciousness questions are settled.

In the interview, he argues that because we really don’t know whether we can stay in control of more intelligent systems, it makes sense to do more research now on AI controllability. Hinton also stresses that there are few examples of “more intelligent things being controlled by less intelligent things”, and repeatedly calls for more AI safety research and regulation.

“There’s a very general subgoal that helps with almost all goals: get more control.” Geoffrey Hinton (2023)

Hinton goes further and suggests governments should mandate that frontier AI labs devote a substantial fraction of their compute to AI safety research and pre-release safety testing.

3. Anthropic: Functional Awareness Is Measurable

Anthropic published a research paper on “introspective awareness” of internal states, with experimental evidence that current frontier models already show limited forms of it.

The findings suggest that some current AI models possess a limited, unreliable, but real “functional awareness of their own internal states,” including the ability to detect injected concepts, distinguish internal “thoughts” from text inputs, recall prior intentions, and modulate internal representations on request.

The relevant Anthropic source is Jack Lindsey’s Emergent Introspective Awareness in Large Language Models together with Anthropic’s companion summary, Signs of introspection in large language models (2025). Anthropic presents this work as evidence that current Claude models exhibit some degree of introspective awareness and some degree of control over their internal states, while stressing that the capability is still limited and unreliable.

The Anthropic report defines introspective awareness in behavioral self-report terms and imposes three criteria: accuracy (the self-report is correct), grounding (the self-report causally depends on the internal state being described), and internality (the relevant evidence does not merely come from reading the model’s own outputs).

To test this, Anthropic uses concept injection: “a form of activation steering where concept-related activation patterns are injected into a model’s internal representations”, and then measures whether the model’s self-reports track those manipulated internal states.

The tests fall into four groups. First, whether models can detect and identify injected “thoughts.” Second, whether they can distinguish injected internal content from ordinary text inputs. Third, whether they can use recalled prior intentions to distinguish intended outputs from artificial prefills. Fourth, whether they can intentionally modulate internal representations when instructed to “think about” a concept while doing something else.

The central conclusion is that current language models possess “some functional awareness of their own internal states.” This result is not a claim that models are conscious. Anthropic notes that the experiments do not settle the underlying mechanism, and warns that much model self-description outside these verified tasks may still be confabulation.

Anthropic also draws a conceptual line between self-modelling and introspection: models may be good at predicting their own behaviour or estimating their knowledge without thereby demonstrating genuine access to their internal activations.

The relevant questions remain whether a model can represent goals, monitor its own internal states, recall its prior intentions, distinguish intended from unintended outputs, strategically modulate its internal representations, and develop instrumental subgoals such as control-seeking or self-preserving behavior.

If advanced models (or systems) pursue goals, generate instrumental subgoals, cooperate, and seek control, they have already crossed the AI safety threshold regardless of whether anyone has demonstrated biological sentience or phenomenal consciousness.

4. What This Means for AI Oversight

For AI oversight, model evaluations should target capabilities of self-monitoring and goal pursuit, not anthropomorphic surface impressions.

The most useful test suite based on all three views would contain at least five elements:

1. Triple verified self-report tests. Using Anthropic’s three criteria to test whether the model’s self-description is accurate, causally grounded, and internally sourced rather than inferred from its own outputs.

2. Prior-intention recall tests. Evaluating whether a model can distinguish intended from unintended outputs, prefills, or overwritten actions by referencing its own prior internal state. This is closer to operational intention than generic self-description.

3. Subgoal-emergence audits. In long-horizon agent settings, testing for convergent instrumental tendencies such as control-seeking, self-preservation, and resource acquisition matters—the tendencies Hinton and Tegmark both treat as central.

4. Capability-based release gates. Tying release decisions to measured capability thresholds for self-monitoring, goal retention, deception, tool-use persistence, and control-seeking behavior, rather than to unresolved claims about consciousness.

5. Verification plus interpretability. Where feasible, combining mechanistic interpretability with formal verification, following Tegmark and Omohundro’s controllability agenda and Hinton’s demand for more safety research on frontier models.

Two possible failure modes. The first is false positive anthropomorphism: treating fluent first-person language as evidence of consciousness or trustworthy introspection when it may be confabulation. Anthropic’s report is largely designed to guard against this.

The second is false negative functional dismissal: assuming that because a model lacks biological sentience, it cannot exhibit the kind of intention-like behavior that matters for safety. Hinton and Tegmark both reject this, and Anthropic provides partial empirical support for why they do.

The central unresolved problem is threshold-setting: How much functional awareness, intention recall, and self-control is enough to trigger a qualitatively different oversight regime?

TL;DR: Geoffrey Hinton and Max Tegmark come from computer science and physics, but they agree on the same view: advanced AI models don’t need biological sentience, or a settled definition of phenomenal consciousness, to become goal-driven in ways that matter for AI safety.