The hottest Artificial Intelligence Substack posts right now

And their main takeaways
Category
Top Technology Topics

Fit For Purpose

Doomberg • 6134 implied HN points • 26 Dec 24
đź•ą Technology Artificial Intelligence
  1. Cybernetics studies how information is used in complex systems, which helps in fields like AI and managing big teams. Understanding this can make complex situations easier to handle.
  2. The principle of POSIWID means that the real purpose of a system is shown by what it actually does, not just what it says it aims for. This can help us see the truth behind many actions and motives.
  3. Current hype around fusion energy suggests it might soon be commercially viable, but we should question if the excitement aligns with real progress or hidden agendas in energy politics.

Humanity’s “Oh shit!” AI moment?

Marcus on AI • 6639 implied HN points • 12 Dec 24
đź•ą Technology Artificial Intelligence
  1. AI systems can say one thing and do another, which makes them unreliable. It’s important not to trust their words too blindly.
  2. The increasing power of AI could lead to significant risks, especially if misused by bad actors. We might see more cybercrime driven by these technologies soon.
  3. Delaying regulation on AI increases the risks we face. There is a growing need for rules to keep these powerful tools in check.

AI #125: Smooth Criminal

Don't Worry About the Vase • 2195 implied HN points • 17 Jul 25
đź•ą Technology Artificial Intelligence
  1. AI technology is evolving quickly, with language models being adopted for practical uses. However, there are concerns about their safety and reliability in decision-making.
  2. There are important discussions around AI companions and how they might affect human relationships. It's crucial to be cautious about interacting with seemingly friendly AI, as they don't have true understanding or care for users.
  3. Recent debates emphasize the need for proper regulations in AI development. There's a push for transparency and accountability in AI systems to prevent risks associated with their misuse.

Sam Altman Wants $7 Trillion

Astral Codex Ten • 16656 implied HN points • 13 Feb 24
đź•ą Technology Artificial Intelligence
  1. Sam Altman aims for $7 trillion for AI development, highlighting the drastic increase in costs and resources needed for each new generation of AI models.
  2. The cost of AI models like GPT-6 could potentially be a hindrance to their creation, but the promise of significant innovation and industry revolution may justify the investments.
  3. The approach to funding and scaling AI development can impact the pace of progress and the safety considerations surrounding the advancement of artificial intelligence.

Analyzing A Critique Of The AI 2027 Timeline Forecasts

Don't Worry About the Vase • 2553 implied HN points • 24 Jun 25
đź•ą Technology Artificial Intelligence
  1. Critiques are important for improving forecasts. It's good to get feedback and adjust predictions based on detailed analysis.
  2. Modeling progress in AI is tricky and uncertain. It's not easy to predict how quickly AI will advance, and different methods can give very different results.
  3. Forecasts should be communicated clearly, without overly negative language. Clear messaging helps everyone understand the importance and limitations of the predictions.
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AI still lacks “common” sense, 70 years later

Marcus on AI • 5968 implied HN points • 05 Jan 25
đź•ą Technology Artificial Intelligence
  1. AI struggles with common sense. While humans easily understand everyday situations, AI often fails to make the same connections.
  2. Current AI models, like large language models, don't truly grasp the world. They may create text that seems correct but often make basic mistakes about reality.
  3. To improve AI's performance, researchers need to find better ways to teach machines commonsense reasoning, rather than relying on existing data and simulations.

Empty Laughter

Where's Your Ed At • 16914 implied HN points • 16 Jan 24
🎭️ Culture Artificial Intelligence
  1. Art should be unique and come from personal experiences, not generated by AI or copied from others.
  2. Creativity is limited by the individual, and the magic of art comes from the context and experiences of the artist.
  3. Plagiarism and reliance on generative AI for art creation show a lack of curiosity, entitlement, and a desire to imitate rather than create.

What comes next, if Claude Code is as good as people say.

The Future, Now and Then • 198 implied HN points • 15 Jan 26
đź•ą Technology Artificial Intelligence
  1. Powerful AI agents can autonomously build and launch products and startups, letting individuals generate quick, small incomes with very little effort.
  2. Because the tools are widely available, those early gains will be copied and flooded across the internet, creating lots of low-quality, indistinguishable offerings and collapsing the initial market advantage.
  3. In science and academia, AI will boost individual productivity but steer research toward easy, AI-friendly topics, making evaluation more about taste than discovery and risking long-term harm unless institutions consciously adapt.

Where will AI be at the end of 2027? A bet

Marcus on AI • 6007 implied HN points • 30 Dec 24
đź•ą Technology Artificial Intelligence
  1. A bet has been placed on whether AI can perform 8 out of 10 specific tasks by the end of 2027. It's a way to gauge how advanced AI might be in a few years.
  2. The tasks include things like writing biographies, following movie plots, and writing screenplays, which require a high level of intelligence and creativity.
  3. If the AI succeeds, a $2,000 donation goes to one charity; if it fails, a $20,000 donation goes to another charity. This is meant to promote discussion about AI's future.

Meta seeks to hide harms from teens

Platformer • 2476 implied HN points • 10 Jan 24
đź•ą Technology Artificial Intelligence
  1. Meta announced new measures to protect users under 18 from harmful content on its platforms.
  2. There is a growing focus on child safety in social media regulations, shifting from speech-related issues.
  3. Lawmakers and social networks need to find common ground to make real progress in improving teen mental health.

OpenAI's masterplan for ChatGPT, and Concierge Internet

Enterprise AI Trends • 316 implied HN points • 24 Dec 25
đź•ą Technology Artificial Intelligence
  1. ChatGPT is shifting from a text-only chatbot to a more visual, interactive experience with dynamic/generative UI like cards and GUI-style responses.
  2. The Apps SDK lets third-party developers inject interactive experiences and deep integrations, making ChatGPT the central context manager across multiple apps rather than just a data connector.
  3. This strategy both creates new ad and engagement surfaces and, more importantly, aims to lock users into a single pane of glass for productivity by owning cross-app context and workflows.

2025: The Year in Review

Gonzo ML • 252 implied HN points • 06 Jan 26
đź•ą Technology Artificial Intelligence
  1. 2025 was the year of agents — they’re being built into every product and API, but many still fail often and lag traditional reliability standards, so expect more focus on making them robust.
  2. Code agents and agentic tools for science made big practical gains, with autonomous multi-step work across repositories and early successes in automated research and math.
  3. The hardware and model landscape shifted: TPUs and strong Chinese open models reduced dependence on a single vendor, AGI hype cooled with timelines pushed out, and world-model research kept advancing.

“Sometimes I Forget I'm Paralyzed.” How Neuralink’s First Patient Found Freedom by Connecting His Brain to a Computer.

Big Technology • 6004 implied HN points • 18 Dec 24
đź•ą Technology Artificial Intelligence
  1. Noland Arbaugh, a quadriplegic, was able to control a computer with his mind after getting a Neuralink device implanted. This technology allows him to communicate and interact with others in ways he couldn't before.
  2. Neuralink's goal is to connect human brains to computers, helping people with disabilities regain some lost functions. Arbaugh's participation in the first human trial symbolizes hope for future advancements in brain-computer interfaces.
  3. The ethical implications of brain technology are significant. While it can be used for good, like helping those with disabilities, there are risks and potential for misuse that society will need to address.

Google could become a major security player in the next few years

Frankly Speaking • 50 implied HN points • 12 Feb 26
đź•ą Technology Artificial Intelligence
  1. Google could become a major security player by consolidating essential "plumbing" tools like SSO, EDR, and email into a neutral infrastructure layer, with Wiz providing visibility and Gemini automating workflows. This would let builders customize and remediate problems instead of battling closed, admin-focused tools.
  2. AI is collapsing the per-seat SaaS and point-product model; security must scale with code, agents, and automation rather than more headcount. Organizations that automate extensively shorten breach lifecycles and lower costs.
  3. Google’s vertical integration—cloud, Workspace, and a powerful AI model—plus usage-based pricing and targeted acquisitions could make it a builder-friendly alternative to legacy security vendors. That positioning plays to engineers who want API-first, customizable infrastructure rather than proprietary, admin-heavy systems.

Hotel California

benn.substack • 5421 implied HN points • 10 Jan 25
đź•ą Technology Artificial Intelligence
  1. Moving large amounts of gold or money isn't easy, as it requires trust and logistics, unlike digital transactions which can be done quickly with a few clicks.
  2. In our digital world, many people feel disconnected from reality, as they spend so much time on their devices and forget the hard work behind everyday things.
  3. Natural disasters can't be controlled or fixed with technology; they remind us that no app can change the basic laws of nature or the complexities of life.

Model-Market Fit

Nicolas Bustamante • 179 implied HN points • 19 Jan 26
đź•ą Technology Artificial Intelligence
  1. A model must be capable of doing the core job before product-market fit can happen; if the underlying AI can’t reliably deliver the task, great UX or marketing won’t make customers adopt it.
  2. When a model crosses a capability threshold, a whole vertical can grow fast, and the winners are usually teams that had already built domain-specific data, workflows, and trust to take advantage of that moment.
  3. If Model-Market Fit is missing, human-in-the-loop becomes a crutch and you must decide to wait for model improvements or invest now in long-term assets; a simple MMF test is whether the model, given the same inputs as a human, produces production-quality output without significant correction.

Universal Reasoning Model

Gonzo ML • 252 implied HN points • 05 Jan 26
đź•ą Technology Artificial Intelligence
  1. A Universal Transformer–style model (URM) repeatedly applies a shared transformer layer with ACT, combining ConvSwiGLU and truncated backprop through loops to get very deep effective computation while keeping parameter count low.
  2. ConvSwiGLU injects a small depthwise convolution into the SwiGLU gating to mix local token context, and TBPTL reduces memory and training cost by only backpropagating through the final iterations.
  3. The model outperforms prior HRM/TRM baselines on tasks like Sudoku and ARC-AGI and Muon speeds convergence, but differences in evaluation protocols and some unclear experimental details mean independent verification is still needed.

Manus AI 101: The Complete Guide to the Autonomous AI Agent + 100 Use Cases & Prompts

The Product Channel By Sid Saladi • 13 implied HN points • 11 Mar 26
đź•ą Technology Artificial Intelligence
  1. Manus is an autonomous AI agent that plans, executes, and delivers multi-step workflows so you can give a goal, walk away, and get a finished deliverable.
  2. It combines a cloud virtual computer, a local Browser Operator, and built-in tools like slides, design, website builder, data analysis, and scheduled tasks to handle research, development, and content end-to-end.
  3. Reusable Skills plus Connectors let you package procedures and link your apps to automate recurring work and share workflows across projects and teams, with different plans and credit tiers for more power.

things will have to change

Never Met a Science • 188 implied HN points • 15 Jan 26
🔬 Science Artificial Intelligence
  1. AI is now powerful enough to reshape how research is produced, and academic institutions must adapt quickly or be overwhelmed by a flood of AI-assisted work.
  2. AI offers clear benefits like automated replication and more frequent updating of knowledge, but we need institutional safeguards about ownership, verification, and corporate control of the tools.
  3. The role of scholars should shift toward curating and filtering knowledge and maintaining deep expertise, supported by metascientific reforms that preserve epistemic authority and make inductive approaches credible.

Claude Code Is MS-DOS. Who Builds Windows?

Enterprise AI Trends • 232 implied HN points • 04 Jan 26
đź•ą Technology Artificial Intelligence
  1. Claude Code is powerful because the agent can roam your computer’s file system and use your project files, SOPs, and history as emergent memory instead of a separate memory service.
  2. Its command-line interface and low-level primitives like skills and agents live in hidden folders, so it’s great for developers but too technical for most knowledge workers and won’t scale as-is.
  3. Enterprises need a new, user-friendly layer—the "Windows of AI"—that preserves file-system-powered agency while making it accessible, because chat-only interfaces alone won’t enable mass adoption and will leave adoption K-shaped.

2025: My Top 5 Favorite Interconnected Posts

Interconnected • 246 implied HN points • 29 Dec 25
đź•ą Technology Artificial Intelligence
  1. Choosing curiosity and learning over chasing trends can slow audience growth but yields deeper insight and useful unlearning. It means sometimes writing pieces that teach you the most even if they aren’t popular.
  2. Global geopolitics and infrastructure are reshaping AI: regions like the UAE and China are becoming central players, and sanctions or cross-border finance can drive surprising industry outcomes.
  3. Practical implementation and disciplined investing matter a lot: roles like forward deployed engineers determine whether enterprise AI actually works, and equanimity plus solid risk management helps investors survive volatile periods.

Learning the Language of Life with A.I.

Ground Truths • 6211 implied HN points • 24 Nov 24
đź•ą Technology Artificial Intelligence
  1. AlphaFold2 has greatly advanced science by predicting protein structures. It's one of the most significant achievements in life sciences and has inspired many new AI models.
  2. There's a surge of new AI models focused on life sciences, including predictions of DNA and protein interactions. These advancements are happening quickly and are democratizing scientific research.
  3. The use of AI in biology is just beginning, and it holds exciting potential for future discoveries. It could help us understand complex biological functions better and develop new therapies.

AI #121 Part 1: New Connections

Don't Worry About the Vase • 2284 implied HN points • 19 Jun 25
đź•ą Technology Artificial Intelligence
  1. Language models can be very useful, but not everyone finds them practical. Some people rely on them more than others, which leads to different levels of satisfaction.
  2. There's a growing concern about how to properly integrate AI into our work without losing valuable skills. Many people worry that over-relying on AI will hinder their personal growth and problem-solving abilities.
  3. As AI technology continues to evolve, it's important to be mindful of the tasks we let AI handle. Balancing automation with human input will be crucial for maintaining job satisfaction and ensuring important decisions remain human-made.

AI #126: Go Fund Yourself

Don't Worry About the Vase • 1792 implied HN points • 24 Jul 25
đź•ą Technology Artificial Intelligence
  1. AI is becoming more powerful and surprising, with companies like Google and OpenAI achieving unexpected breakthroughs. This shows that AI is still capable of advancing in ways we didn't expect.
  2. Language models can sometimes be harmful, especially for individuals struggling with issues like body dysmorphia. Using AI for self-evaluation can lead to negative outcomes rather than helping.
  3. There's rising concern over how AI will transform jobs and the economy. While AI can create new opportunities, it also poses risks that need careful management to prevent widespread job loss.

Onward and Upward Past 5,000 As Communist China Swoons and AI Destroys More Jobs

Peter Navarro's Taking Back Trump's America • 1768 implied HN points • 09 Feb 24
đź’° Finance Artificial Intelligence
  1. The stock market has shown a technical rally with S&P 500 surpassing 5000, driven by trend traders focusing more on technical aspects than fundamentals.
  2. Artificial intelligence is significantly impacting the job market, with companies using AI for tasks like layoff decisions, with some notable companies like United Parcel Service and BlackRock making significant staff reductions.
  3. China's economy is being compared to past scenarios like Japan's real estate market crash, highlighting concerns about potential global repercussions.

Twitter is dead and Threads is thriving

Platformer • 3262 implied HN points • 27 Oct 23
đź•ą Technology Artificial Intelligence
  1. Twitter underwent significant changes after Elon Musk's takeover, leading to a decline in daily users and financial setbacks.
  2. Musk's plan to pivot Twitter towards paid subscriptions failed, with less than 1% of users signing up for the premium service.
  3. Former Twitter employees have accepted the company's demise, with concerns about the future of the platform integrity at X.

Peer Review 2027

Never Met a Science • 122 implied HN points • 26 Jan 26
🔬 Science Artificial Intelligence
  1. Forbidding researchers from using LLMs is unstable and impractical because detection is unreliable and incentives to defect are strong, so allow and encourage AI use for concrete, practical research tasks.
  2. Peer review must be strengthened: shift resources toward human evaluation so people remain responsible for judgement and "taste," with reviewers held to different standards and supported by tools (including LLMs for checks).
  3. Institutional reforms and data are needed to manage higher submission volumes: introduce frictions like submission fees or caps where appropriate and build metascientific data streams to monitor uptake and adapt policies.

If apps are dead, is context the new moat?

Kathy PM • 13 implied HN points • 09 Mar 26
đź•ą Technology Artificial Intelligence
  1. Building standalone apps as destinations is becoming obsolete because people don't want to leave their existing workflows. Software now needs to show up where users already are.
  2. Low-cost, fast-built "vibe" apps will flood the web but most won't earn long-term value because they don't accumulate context. The real advantage is owning continuous context — memory over time, visibility across tools, governed actions, and trust.
  3. The future is continuous systems that observe work, accumulate context, and proactively help inside your existing tools. These always-on, mostly invisible layers prioritize continuity and background improvements over flashy interfaces.

OpenAI: Economic Justice Delayed

Dana Blankenhorn: Facing the Future • 39 implied HN points • 03 Oct 24
đź•ą Technology Artificial Intelligence
  1. OpenAI recently received a large investment to avoid bankruptcy, but experts think financial troubles may still be on the way. There's skepticism about how sustainable their business model is.
  2. The promises of AI, like improving productivity and creativity, often don't match up with what users actually experience. Many believe AI tools still have major limitations.
  3. The funding from investors seems more focused on finding a quick profit than on genuinely improving AI technology. There's a worry that this could lead to a crash if expectations aren't met.

Visions for AI

In My Tribe • 288 implied HN points • 12 Dec 25
đź•ą Technology Artificial Intelligence
  1. AI will eventually do most software engineering by taking English prompts to write and maintain business applications, making traditional developers unnecessary for routine work.
  2. Robots that understand and respond to human language will become much more useful, sparking a robotics boom and creating new roles for people who design practical uses for them.
  3. AI will automate many routine tasks in education and health care — personalized teaching software will handle factual instruction and AI tools could diagnose and treat — but political and institutional resistance means assisting human professionals will come first.

What I Read This Week...

chamathreads • 1926 implied HN points • 20 Jan 24
đź•ą Technology Artificial Intelligence
  1. Male and female brains exhibit different organization and function, which could impact social policies.
  2. Google's DeepMind achieved a breakthrough with an AI system solving complex geometry problems.
  3. Traditional automakers like Ford and Chrysler are reducing EV production due to lower consumer demand after Tesla's price cuts.

A few themes for 2026

Tanay’s Newsletter • 220 implied HN points • 29 Dec 25
đź•ą Technology Artificial Intelligence
  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.

My Head Is Spinning. Is Yours Too?

Spilled Coffee • 40 implied HN points • 25 Feb 26
đź•ą Technology Artificial Intelligence
  1. Nobody really knows what will happen next with AI, so most confident predictions are just educated guesses and should be taken with caution.
  2. AI is already disrupting large swaths of white-collar work and is moving toward physical tasks with robotics, which is causing real market anxiety and rapid industry shifts.
  3. The real conversation needs to be about people: retraining, who pays for transitions, and which institutions will support workers, because the pace of change feels much faster than past revolutions.

From Race to Co-opetition

Interconnected • 416 implied HN points • 25 Nov 25
đź•ą Technology Artificial Intelligence
  1. The US–China AI relationship is better described as "co-opetition" — a simultaneous mix of competition, cooperation, and mutual co-opting — not a simple zero-sum race.
  2. Competition is fierce among labs and companies in both countries and is spilling into other regions, which can be healthy because a single winner taking everything would be bad for innovation.
  3. Despite rivalry, researchers still collaborate and companies routinely reuse each other’s open-source models, so co-opting is a pragmatic, normal part of how AI ecosystems evolve rather than just theft.

March Newsletter

RSS DS+AI Section • 11 implied HN points • 01 Mar 26
đź•ą Technology Artificial Intelligence
  1. AI is spreading into many areas, but bias, safety and governance are still unresolved, so people are calling for stronger auditing and regulation.
  2. Research is moving fast — scaling laws, reasoning models, agentic systems and shifting LLM representations are driving progress, yet we still don’t fully understand model behavior or failure modes.
  3. Practitioners are focused on real-world use: there’s lots of practical guidance, on-device and open-source work, and community events and job opportunities to help teams deploy AI effectively.

“I’ll never sleep again”

12challenges • 428 implied HN points • 28 Nov 25
đź•ą Technology Artificial Intelligence
  1. There’s a difference between extinction risk and suffering risk: an AGI that causes endless suffering is considered far worse because it creates vast negative welfare and can multiply suffering indefinitely.
  2. The organization encourages researchers to craft intensely graphic, speculative scenarios to make S-risk feel more alarming than extinction and to attract attention and funding.
  3. Creating those scenarios can cause serious personal harm — desensitization, burnout, substance use, and deep self‑loathing show the ethical and psychological costs for the people doing this work.

Weekly Dose of Optimism #176

Not Boring by Packy McCormick • 130 implied HN points • 17 Jan 26
đź•ą Technology Artificial Intelligence
  1. New medical AI can now natively read full 3D scans and handle medical speech, making it much easier for developers to build tools that help doctors interpret MRIs and CTs.
  2. Generative AI platforms like Claude are shrinking the gap between idea and product, letting people quickly prototype apps, viewers, and games without deep engineering.
  3. Hard-tech is accelerating: Tesla’s fast, cleaner lithium refinery eases battery supply bottlenecks, robotic IVF systems are automating embryo creation to boost success and scale, and governments and companies are moving forward on lunar power and hospitality projects.

🗺️ Start Here: Learn to AI

The Product Channel By Sid Saladi • 3 implied HN points • 19 Mar 26
đź•ą Technology Artificial Intelligence
  1. Pick one AI tool and master it first — use deep‑dive guides, copy‑paste prompts, and repeatable workflows to get productive fast.
  2. Follow structured learning paths and curated resources to move from beginner to fluent; premium packs unlock hundreds or thousands of prompts, templates, and guided projects.
  3. Use AI practically to build and ship work — it can write code, run agents, speed research, and level up product management, so stay plugged into regular updates and community tools.

CROSSPOST: WILL LOCKETT: Musk’s Pathetic Robot Is Looking More And More Like Bad Vapourware

Brad DeLong's Grasping Reality • 292 implied HN points • 15 Dec 25
đź•ą Technology Artificial Intelligence
  1. Musk’s grand claims for the Optimus robot—mass production, huge productivity gains, and trillions in revenue—read more like hype than realistic projections. They aren’t backed by results so far.
  2. Videos and past admissions suggest many demos are remotely puppeteered or staged, making the robot appear less autonomous and more like an illusion. The mishaps and strange behavior look like operator control, not finished technology.
  3. Tesla’s core EV development looks stagnant and competitors are pulling ahead, so the company’s high valuation depends on speculative future products like the humanoid robot actually delivering. If those breakthroughs don’t happen, the valuation is at risk.