The hottest AI Research Substack posts right now

And their main takeaways
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Top Technology Topics

Breaking: Sir Demis Hassabis becomes the latest to say that ChatGPT is a dead-end and that we must turn our focus to world models

Marcus on AI • 9366 implied HN points • 22 Jan 26
🕹 Technology AI Research
  1. A leading AI figure says ChatGPT-style large language models are a dead end and researchers should prioritize building world models.
  2. This comment joins other voices pushing the field to move beyond chat interfaces toward systems that actually model and understand the world.
  3. Earlier analysis argues that purely statistical approaches have limits and that neurosymbolic or cognitive 'world' models are needed for deeper AI.

Google DeepMind CEO Demis Hassabis on AI’s Next Breakthroughs, What Counts As AGI, And Google’s AI Glasses Bet

Big Technology • 5504 implied HN points • 29 Jan 26
🕹 Technology AI Research
  1. AI still needs major breakthroughs like continual learning, better long-term memory, and more efficient context handling to enable deeper reasoning and planning.
  2. AGI is defined as matching human-level abilities across creativity, scientific discovery, and physical skills, and true AGI remains years away, not an immediate milestone.
  3. Companies are pushing powerful multimodal models into real products like hands-free smart glasses and assistants, while emphasizing trust, privacy, and caution around ad-driven business models.

“Scale Is All You Need” is dead

Marcus on AI • 14307 implied HN points • 08 Dec 25
🕹 Technology AI Research
  1. The belief that just scaling up models and data will by itself produce general intelligence has failed and the community is finally recognizing its limits.
  2. Current generative models are still unreliable — they hallucinate, struggle with reasoning and facts, and many businesses aren’t seeing the promised ROI.
  3. The next phase should be interdisciplinary: borrow ideas from cognitive science and combine symbolic, causal, and world-model approaches to build more reliable, human-informed AI.

AI Cartoon of The Year — and five (re)readings for 2026

Marcus on AI • 9169 implied HN points • 30 Dec 25
🕹 Technology AI Research
  1. A sharp cartoon captured and critiqued the hype around AI, showing how popular narratives can run ahead of what the technology actually delivers.
  2. Recent essays stress that LLMs still hallucinate, struggle with true generalization, and operate very differently from human reasoning, exposing key technical limits.
  3. Because of those limits, the field is likely to shift from pure LLMs toward systems with explicit world models and neurosymbolic methods, and those newer approaches may overtake current models over time.

Open models in perpetual catch-up

Democratizing Automation • 522 implied HN points • 17 Feb 26
🕹 Technology AI Research
  1. Open models have improved a lot but still trail the best closed models by roughly 6–9 months, and simple benchmark averages can hide important frontier gaps that favor well-resourced closed labs.
  2. The open-model space is brutally competitive and adoption concentrates on a few winners, while there’s a clear unmet need for small, fast, cheap specialized models for enterprise and agent sub-tasks.
  3. China’s collaborative open-model ecosystem makes it a likely place for big breakthroughs, and more dedicated research is needed to understand the technical and geopolitical diffusion where open weights will shape long-term AI adoption.
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“A case for AI models that understand, not just predict, the way the world works”

Marcus on AI • 3833 implied HN points • 15 Dec 25
🕹 Technology AI Research
  1. The main open challenge in AI is building systems that truly understand how the world works, not just systems that predict likely next words or patterns.
  2. True understanding means forming internal world models that capture causal, physical, and conceptual relationships, not just statistical correlations.
  3. Short, nuanced discussions or podcasts can help clarify this distinction and are worth listening to for anyone tracking AI progress.

The Sequence Radar #811: Last Week in AI: OpenAI's Capital Leap, India's Summit, and the Next Frontier of Models

TheSequence • 175 implied HN points • 22 Feb 26
🕹 Technology AI Research
  1. AI is entering a capital- and infrastructure-driven phase. Massive funding rounds and multibillion-dollar plans are being raised to build the silicon, power, and data centers needed for next-gen models.
  2. Model capabilities are leaping forward with agentic, long-context, and stronger reasoning abilities. New releases and research (for example Sonnet 4.6, Gemini 3.1 Pro, and GLM-5) push autonomous agent use, huge context windows, and improved problem-solving.
  3. Geopolitical and regional pushes are building sovereign AI stacks and expanding access. Global summits and large local investments are committing hundreds of billions to data centers, fiber links, and localized models to make AI national-scale infrastructure.

Weekly Top Picks #112: Besides Moltbook

The Algorithmic Bridge • 244 implied HN points • 03 Feb 26
🕹 Technology AI Research
  1. Building and running frontier AI models is extremely expensive and they depreciate quickly, so firms often only barely break even because R&D and rapid model turnover eat profits.
  2. Who’s winning the AI race depends on what you measure: Chinese players like DeepSeek are taking market share and publishing new scaling advances, but the overall picture is mixed and some elite researchers are pessimistic.
  3. Privacy and governance are lagging—interactions with AI are frequently monitored, and internal safety conflicts at big labs can paradoxically accelerate competition instead of slowing it.

Using AI To Research The Missing Heritability Post

Astral Codex Ten • 4817 implied HN points • 02 Jul 25
🔬 Science AI Research
  1. AI can be really useful for research, especially in complex topics like genetics. It helps to gather and analyze a lot of information quickly.
  2. However, we need to be careful because AI can also provide misleading information. It's important to cross-check facts and not trust everything it says.
  3. Balancing the benefits and risks of AI is key. We should use its tools but also stay critical of the results it produces.

The Sequence Radar #799: The Week AI Leveled Up: From Chatbots to World Builders

TheSequence • 273 implied HN points • 01 Feb 26
🕹 Technology AI Research
  1. AI is shifting from passive chatbots to active agents and simulated worlds, with models now able to orchestrate many sub-agents in parallel and create interactive, physics-aware environments users can explore.
  2. Frontier reasoning is becoming a global standard as models expose step-by-step “thinking” modes and stronger multimodal/speech capabilities, letting systems spend more compute at test time to produce better, more reliable answers.
  3. Big platform plays and huge capital rounds are reshaping the field: companies are building integrated AI workspaces and chasing massive investments that could concentrate compute and user data with a few dominant players.

Juicy Research Ideas and How to Find them?

AI Research & Strategy • 297 implied HN points • 01 Sep 24
🕹 Technology AI Research
  1. People often find AI research ideas by reading papers, talking to experts, or browsing online platforms like Twitter and GitHub. These are effective ways to spark inspiration.
  2. There are various strategies for generating AI research ideas, such as inventing new tasks, improving existing methods, or exploring gaps in current research. Each approach can lead to publishing valuable findings.
  3. Building better AI research assistants can involve encoding these idea-generation strategies into their programming. This could make them more effective in supporting researchers.

"Wouldn't It Be Cool If..." Science is a Scourge and We Will Regret It

Freddie deBoer • 9344 implied HN points • 06 Jan 25
🔬 Science AI Research
  1. There are tons of resources to learn about science today, but a lot of popular science content can be misleading and full of hype. It's important to be careful about what you believe, especially if you don't have a strong background in the subject.
  2. Many claims in science media, like the existence of alternate dimensions or warp drives, often lack strong evidence. It’s crucial to approach such claims with skepticism rather than taking them at face value.
  3. Real scientific work is usually slow and methodical, rather than exciting breakthroughs. Making science seem too flashy might mislead younger people about what a career in science really involves.

The Sequence Knowledge #800: Not All World Models are Created Equal

TheSequence • 147 implied HN points • 03 Feb 26
🕹 Technology AI Research
  1. There are different types of world models, and a clear taxonomy helps explain how they differ and what roles they play in AI.
  2. For decades, model-free reinforcement learning dominated: agents learned by reinforcing actions without building internal maps or understanding why those actions worked.
  3. Looking at the first major papers on world models reveals the origins and trade-offs of different approaches and shows why some models are better suited for planning and reasoning.

Import AI 357: Facebook's open source AGI plan; Google beats humans at geometry problems; and Intel makes its GPUs better

Import AI • 2076 implied HN points • 22 Jan 24
🕹 Technology AI Research
  1. Facebook aims to develop artificial general intelligence (AGI) and make it open-source, marking a significant shift in focus and possibly accelerating AGI development.
  2. Google's AlphaGeometry, an AI for solving geometry problems, demonstrates the power of combining traditional symbolic engines with language models to achieve algorithmic mastery and creativity.
  3. Intel is enhancing its GPUs for large language models, a necessary step towards creating a competitive GPU offering compared to NVIDIA, although the benchmarks provided are not directly comparable to industry standards.

A few themes for 2026

Tanay’s Newsletter • 220 implied HN points • 29 Dec 25
🕹 Technology AI Research
  1. Big AI products will start finding ways to monetize massive free usage with ad-like or sponsored placements outside of direct answers, because subscriptions alone won’t capture everyone.
  2. AI will get more proactive and agent-like, monitoring signals, surfacing updates, and taking on multi-step tasks without waiting for prompts.
  3. Technical leaps in reliable computer use and continual learning will let agents actually operate apps, fill complex forms, and improve over time so they can complete work instead of just offering suggestions.

Universities Are Woefully Under-Resourced For AI Research. They’re Fighting To Change That.

Big Technology • 5129 implied HN points • 22 Nov 24
🕹 Technology AI Research
  1. Universities are struggling to keep up with AI research due to a lack of resources like powerful GPUs and data centers. They can't compete with big tech companies who have millions of these resources.
  2. Most AI research breakthroughs are now coming from private industry, with universities lagging behind. This is causing talented researchers to prefer jobs in the private sector instead.
  3. Some universities are trying to address this issue by forming coalitions and advocating for government support to create shared AI research resources. This could help level the playing field and foster important academic advancements.

2026 AI Predictions

Alex Ghiculescu's Newsletter • 135 implied HN points • 19 Jan 26
🕹 Technology AI Research
  1. AI labs will focus on coding agents, with most development effort and revenue moving toward models that write software.
  2. Keeping up with rapidly improving AI coding tools will be the main challenge for software companies; engineering teams will need to learn new workflows and roll them out across people with different skills and enthusiasm.
  3. New techniques will close agents' domain-knowledge gaps so models can understand real codebases and make decisions, and those same solutions will boost many other AI applications.

Import AI 356: China's good LLM; AI credit scores; and fooling VLMs with REBUS

Import AI • 1238 implied HN points • 15 Jan 24
🕹 Technology AI Research
  1. Today's AI systems struggle with word-image puzzles like REBUS, highlighting issues with abstraction and generalization.
  2. Chinese researchers have developed high-performing language models similar to GPT-4, showing advancements in the field, especially in Chinese language processing.
  3. Language models like GPT-3.5 and 4 can already automate writing biological protocols, hinting at the potential for AI systems to accelerate scientific experimentation.

Helen Toner Takes the Reins at CSET

ChinaTalk • 222 implied HN points • 04 Dec 25
🇺🇸 U.S. Politics AI Research
  1. Helen Toner believes that CSET should focus on delivering rigorous, evidence-based research while adapting to the fast-changing landscape of AI and national security. She wants to maintain CSET's unique value, particularly its depth of knowledge on China.
  2. The conversation around AI policy is becoming more complex, with the rise of 'dark arts' or manipulative tactics in Washington. However, there is still a strong demand for factual, evidence-driven analysis, which CSET aims to provide through direct interaction with policymakers.
  3. Toner introduced the concept of a 'jagged frontier' in AI, which suggests that AI's progress may be uneven—good at some tasks while struggling with others. This perspective emphasizes the importance of policy supporting steady advancements rather than rapid, unpredictable changes.

The Sequence AI of the Week #809: Slow Thinking, Fast Discovery: Inside DeepMind’s Aletheia Architecture

TheSequence • 35 implied HN points • 18 Feb 26
🕹 Technology AI Research
  1. Aletheia is a DeepMind research agent built on the DeepThink architecture that emphasizes slow, deliberate “System 2” reasoning for autonomous scientific discovery.
  2. It shifts models away from fast next-token prediction toward verification and self-correction, aiming to reduce hallucinations and improve reliability.
  3. By giving the agent tools and the ability to check and admit mistakes, Aletheia enables deeper, more trustworthy exploration and problem solving.

Import AI 371: CCP vs Finetuning; why people are skeptical of AI policy; a synthesizer for a LLM

Import AI • 439 implied HN points • 06 May 24
🕹 Technology AI Research
  1. People are skeptical of AI safety policy as different views arise from the same technical information, making it important to consider varied perspectives.
  2. Chinese researchers have developed a method called SOPHON to openly release AI models while preventing finetuning for misuse, offering a solution for protecting against subsequent harm.
  3. Automating intelligence analysis through datasets like OpenStreetView-5M will enhance training machine learning systems for geolocation, leading to potential applications in both military intelligence and civilian sectors.

Import AI 374: China's military AI dataset; platonic AI; brainlike convnets

Import AI • 339 implied HN points • 27 May 24
🕹 Technology AI Research
  1. UC Berkeley researchers discovered a suspicious Chinese military dataset named 'Zhousidun' with specific images of American destroyers, presenting potential implications for military use of AI.
  2. Research suggests that as AI systems scale up, their representations of reality become more similar, with bigger models better approximating the world we exist in.
  3. Convolutional neural networks are shown to align more with primate visual cortexes than transformers, indicating architectural biases that can lead to better understanding the brain.

Setting AIRS Free

AI Research & Strategy • 158 implied HN points • 05 Aug 24
🕹 Technology AI Research
  1. The writer has paused billing for their Substack and is offering full refunds to all paid subscribers. They believe it's fair since they haven't been able to provide valuable content recently.
  2. Health challenges impacted the writer's ability to consistently focus on their Substack. They want to put their health first instead of feeling pressured to deliver content.
  3. The writer plans to continue writing occasionally, focusing on joy instead of obligation. They appreciate the support they've received and are thankful for their subscribers.

Import AI 372: Gibberish jailbreak; DeepSeek's great new model; Google's soccer-playing robots

Import AI • 399 implied HN points • 13 May 24
🕹 Technology AI Research
  1. DeepSeek released a powerful language model called DeepSeek-V2 that surpasses other models in efficiency and performance.
  2. Research from Tsinghua University shows how mixing real and synthetic data in simulations can improve AI performance in real-world tasks like medical diagnosis.
  3. Google DeepMind trained robots to play soccer using reinforcement learning in simulation, showcasing advancements in AI and robotics;

The Sequence Knowledge #808: Stop Trying to Generate the World: Inside the JEPA Way for World Models

TheSequence • 35 implied HN points • 17 Feb 26
🕹 Technology AI Research
  1. Recreating the world pixel-by-pixel isn’t the path to true intelligence, because generating images doesn’t prove a model understands the underlying concepts.
  2. JEPA (Joint Embedding Predictive Architecture) trains models to predict in a shared embedding space so they learn and forecast concepts instead of raw pixels, capturing semantics without rendering images.
  3. Several JEPA papers argue this is a promising way to build world models, suggesting we should shift research from generative reconstruction to predictive conceptual representations when measuring understanding.

Import AI 354: Distributed LLM inference; CCP-approved dataset; AI scientists

Import AI • 1278 implied HN points • 25 Dec 23
🕹 Technology AI Research
  1. Distributed inference is becoming easier with AI collectives, allowing small groups to work with large language models more efficiently and effectively.
  2. Automation in scientific experimentation is advancing with large language models like Coscientist, showcasing the potential for LLMs to automate parts of the scientific process.
  3. Chinese government's creation of a CCP-approved dataset for training large language models reflects the move towards LLMs aligned with politically correct ideologies, showcasing a unique approach to LLM training.

Import AI 368: 500% faster local LLMs; 38X more efficient red teaming; AI21's Frankenmodel

Import AI • 559 implied HN points • 08 Apr 24
🕹 Technology AI Research
  1. Efficiency improvements can be achieved in AI systems by varying the frequency at which GPUs operate, especially for tasks with different input and output lengths.
  2. Governments like Canada are investing significantly in AI infrastructure and safety measures, reflecting the growing importance of AI in economic growth and policymaking.
  3. Advancements in AI technologies are making it easier for individuals to run large language models locally on their own machines, leading to a more decentralized access to AI capabilities.

Import 355: Local LLMs; scaling laws for inference; free Mickey Mouse

Import AI • 1058 implied HN points • 08 Jan 24
🕹 Technology AI Research
  1. PowerInfer software allows $2k machines to perform at 82% of the performance of $20k machines, making it more economically sensible to sample from LLMs using consumer-grade GPUs.
  2. Surveys show that a significant number of AI researchers worry about extreme scenarios such as human extinction from advanced AI, indicating a greater level of concern and confusion in the AI development community than popular discourse suggests.
  3. Robots are becoming cheaper for research, like Mobile ALOHA that costs $32k, and with effective imitation learning, they can autonomously complete tasks, potentially leading to more robust robots in 2024.

In defense of vibes-based evaluations

The AI Frontier • 79 implied HN points • 01 Aug 24
🕹 Technology AI Research
  1. Vibes-based evaluations are a helpful starting point for assessing AI quality, especially when specific metrics are hard to define. They allow for initial impressions based on user interactions rather than strict guidelines.
  2. Customers often have unique and unexpected requests that can't easily fit into predefined test sets. Vibes allow for flexibility in understanding real-world usage.
  3. While vibes are useful, they also have downsides, like strong first impressions and limited feedback. A mix of vibes and structured evaluations can provide a better overall understanding of an AI's performance.

The White House's plan for open models & AI research in the U.S.

Democratizing Automation • 593 implied HN points • 23 Jul 25
🇺🇸 U.S. Politics AI Research
  1. The White House's new AI Action Plan suggests we need to invest more in open-source AI models. These models can help startups and researchers who need flexible and affordable resources.
  2. The plan emphasizes that having strong open models is important for academic research and for maintaining America's leadership in AI innovation. This could prevent American researchers from falling behind international competitors.
  3. The government aims to collaborate with private companies to make AI resources more accessible to researchers and educators. This includes improving access to computing power, which is essential for developing effective AI models.

The Top AI Newsletters on Substack in 2023

AI Supremacy • 1179 implied HN points • 18 Apr 23
🕹 Technology AI Research
  1. The list provides a comprehensive agnostic collection of various AI newsletters on Substack.
  2. The newsletters are divided into categories based on their status, such as top tier, established, ascending, expert, newcomer, and hybrid.
  3. Readers are encouraged to explore the top newsletters in AI and share the knowledge with others interested in technology and artificial intelligence.

The Sequence Knowledge #804: The Dreamer Trilogy: Inside Some of the Most Influential Papers in AI World Models

TheSequence • 28 implied HN points • 10 Feb 26
🕹 Technology AI Research
  1. The Dreamer trilogy of papers reshaped how researchers build and use world models in AI.
  2. Model-based reinforcement learning inspired modern world models, focusing on agents that learn internal predictive models instead of directly mapping pixels to actions.
  3. Model-free methods like DQN succeeded in 2D games but struggled in complex 3D environments such as DeepMind Lab and Minecraft, revealing the limits of purely reactive agents and motivating the shift to world models.

Import AI 365: WMD benchmark; Amazon sees $1bn training runs; DeepMind gets closer to its game-playing dream

Import AI • 399 implied HN points • 18 Mar 24
🕹 Technology AI Research
  1. Alliance for the Future (AFTF) was founded in response to concerns about overreach in AI safety regulation, highlighting the importance of well-intentioned policies leading to counter-reactions.
  2. Covariant's RFM-1 shows how generative AI can be applied to industrial robots, allowing easy robot operation through human-like instructions, reflecting a shift towards faster-moving robotics facilitated by AI.
  3. DeepMind's SIMA represents a significant advancement towards a general AI agent by fusing recent AI advancements, showcasing the potential of scaling up diverse AI functions in new environments, opening possibilities for further development and complexity.

Import AI 363: ByteDance's 10k GPU training run; PPO vs REINFORCE; and generative everything

Import AI • 419 implied HN points • 04 Mar 24
🕹 Technology AI Research
  1. DeepMind developed Genie, a system that transforms photos or sketches into playable video games by inferring in-game dynamics.
  2. Researchers found that for language models, the REINFORCE algorithm can outperform the widely used PPO, showing the benefit of simplifying complex processes.
  3. ByteDance conducted one of the largest GPU training runs documented, showcasing significant non-American players in large-scale AI research.

The Sequence Knowledge #792: EVERYTHING you Need to Know About Synthetic Data Generation

TheSequence • 49 implied HN points • 20 Jan 26
🕹 Technology AI Research
  1. Synthetic data is a practical scaling lever that fills coverage gaps and builds long-tail capabilities by creating targeted examples instead of waiting for rare real-world labels.
  2. Core methods include generative synthesis, rephrasing/paraphrasing, multi-turn dialogue synthesis, and RL trajectory generation, each tailored to different tasks like images, instructions, conversations, or environment rollouts.
  3. The focus is on quality over quantity: tight specs, automatic verification, diversity controls, and eval-driven feedback let teams steer capabilities, improve class balance, protect privacy, and iterate quickly.

Reinforcement learning with random rewards actually works with Qwen 2.5

Democratizing Automation • 633 implied HN points • 27 May 25
🕹 Technology AI Research
  1. Reinforcement learning using random rewards can still improve performance in models like Qwen 2.5, even when the rewards aren't perfect. This suggests that the learning process is more flexible than previously thought.
  2. Qwen 2.5 and its math-focused variants show that they might use unique reasoning strategies, like code-assisted reasoning, that help them perform better on math tasks. This means they learn in ways that other models might not.
  3. The ongoing debate about the effectiveness of reinforcement learning with verifiable rewards (RLVR) highlights the need for further research. It also suggests that scaling up the use of reinforcement learning could lead to new behaviors in models, making them more capable.

🦄 The top six rivals competing with OpenAI

AI Supremacy • 805 implied HN points • 27 Apr 23
🕹 Technology AI Research
  1. OpenAI has a diverse range of advanced AI products beyond just ChatGPT.
  2. DeepMind, a Google-owned company, is a significant competitor to OpenAI focusing on building general-purpose learning algorithms.
  3. Anthropic, Cohere, and Stability A.I. are emerging competitors in the AI space, each with unique approaches and products.

Import AI 361: GPT-4 hacking; theory of minds in LLMs; and scaling MoEs + RL

Import AI • 359 implied HN points • 19 Feb 24
🕹 Technology AI Research
  1. Researchers have discovered how to scale up Reinforcement Learning (RL) using Mixture-of-Experts models, potentially allowing RL agents to learn more complex behaviors.
  2. Recent research shows that advanced language models like GPT-4 are capable of autonomous hacking, raising concerns about cybersecurity threats posed by AI.
  3. Adapting off-the-shelf AI models for different tasks, even with limited computational resources, is becoming easier, indicating a proliferation of AI capabilities for various applications.

Data Science Weekly - Issue 533

Data Science Weekly Newsletter • 339 implied HN points • 09 Feb 24
🕹 Technology AI Research
  1. Satellite data is important for machine learning and should be treated as a unique area of research. Recognizing this can help improve how we use this data.
  2. Many data science and machine learning projects fail from the start due to common mistakes. Learning from past experiences can help increase the chances of success.
  3. Open source software plays a crucial role in advancing AI technology. It's important to support and protect open source AI from regulations that could harm its progress.