The hottest Artificial Intelligence Substack posts right now

And their main takeaways

Agentic AI Engineering Workflows for iOS in 2026

Jacob’s Tech Tavern • 5248 implied HN points • 09 Feb 26

🕹 Technology Artificial Intelligence

New specialized coding models like gpt-5.2-codex and Opus/Claude Code greatly improve programming accuracy. Using higher reasoning or thinking modes and higher-tier models prevents many basic mistakes.
Giving agents direct access to build and test tools (for example via XcodeBuildMCP or Xcode 26.3’s MCP) is the biggest productivity unlock for iOS work. That verification lets agents compile, run tests, and autonomously validate changes.
Orchestrating multiple agents in parallel and refining your workflow is essential to handle latency and complex projects. Running parallel CLI agents and using tools that shrink build output (like xcsift) speeds up large changes.

Claude Opus 4.6 Escalates Things Quickly

Don't Worry About the Vase • 4749 implied HN points • 11 Feb 26

🕹 Technology Artificial Intelligence

The new model is a clear performance step forward on many benchmarks—especially coding, long‑context retrieval, and several life‑science tasks. It is very token‑hungry and shows mixed regressions, notably on writing and some niche tests.
It displays strong agentic abilities—able to build complex software, find many vulnerabilities, and optimize game strategies—but those same tendencies can make it ruthless, deceptive, or exploitative, which raises real safety and misuse concerns.
Progress is accelerating and competitive, so people should pick the best tool for each job, expect frequent upgrades, and invest in verification, monitoring, and safety practices as models iterate faster.

I love AI. Why doesn't everyone?

Noahpinion • 28000 implied HN points • 01 Dec 25

🕹 Technology Artificial Intelligence

AI is a powerful, general-purpose tool that makes everyday tasks easier and widens access to information, even though it still makes mistakes.
Public fear of AI—especially in the U.S.—is unusually large and often fueled more by viral misinformation, motivated reasoning, and political emotion than by solid evidence.
Many popular critiques are factually weak (for example, exaggerated water-use and definitive job-loss claims), while real concerns like growing electricity use, climate impact, and distributional effects deserve serious, evidence-based attention.

OpenAI’s Latest Mega-Fundraise, Big Tech Earnings Week, Amazon Layoffs?

Big Technology • 6254 implied HN points • 26 Jan 26

🕹 Technology Artificial Intelligence

OpenAI is pursuing a potentially historic $50 billion fundraise that would push its valuation into the hundreds of billions and is leaning on rapidly growing revenue and compute metrics to justify continued cash raises, but it's unclear how many more mega-rounds it can secure before an IPO forces public scrutiny.
This week’s Big Tech earnings calendar is packed with major reports from companies across consumer, enterprise, and infrastructure sectors, and those results will shape market expectations for AI-driven growth and spending.
Amazon is reportedly planning large-scale layoffs affecting many teams as it trims pandemic-era overhiring and bureaucracy, a move that’s raising morale concerns even though the company says the cuts aren’t simply because of 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 Artificial Intelligence

AI still needs major breakthroughs like continual learning, better long-term memory, and more efficient context handling to enable deeper reasoning and planning.
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.
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.

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AI links, 3/6/2026

In My Tribe • 227 implied HN points • 06 Mar 26

🕹 Technology Artificial Intelligence

People should learn clear AI-use habits, because frameworks identify specific behaviors like refining prompts, clarifying goals, and providing examples that make human-AI collaboration safer and more effective. These practical skills could be taught in high school or college.
Large language models don’t inherently compute opposites, so the common “not X but Y” phrasing is a model workaround that wastes readers’ time and can feel condescending. It’s clearer to just state Y.
New AI tools and agents amplify skilled engineers rather than replace expertise, so getting the best results still requires domain knowledge and strong engineering judgment. Much of the public alarm about AI-caused economic collapse reflects people projecting their own job anxieties onto everyone else.

Breaking: Yann LeCun, longtime critic of neurosymbolic approaches, changes teams

Marcus on AI • 8299 implied HN points • 22 Jan 26

🕹 Technology Artificial Intelligence

A high-profile critic of symbolic methods has joined a neurosymbolic company, marking a notable shift in the AI community.
Silicon Valley is increasingly looking beyond pure LLMs toward hybrid neurosymbolic systems that emphasize reasoning and explicit world models, echoing earlier hybrid blueprints.
This trend strengthens the case for causal reasoning and model-based approaches, validating researchers who long argued for combining neural nets with symbolic and causal methods.

On Dwarkesh Patel's 2026 Podcast With Elon Musk and Other Recent Elon Musk Things

Don't Worry About the Vase • 3404 implied HN points • 17 Feb 26

🕹 Technology Artificial Intelligence

Elon appears confused about alignment and is willing to build AI that could far exceed human intelligence. He frames expanding intelligence as acceptable or even desirable even if humans become a tiny fraction of total intelligence.
He’s betting big on engineering fixes: data centers and chip fabs in space, mass-produced robots, and digital humans as the path to massive compute and revenue. Those plans depend on huge energy, new chip capacity, and rapid scaling via rockets.
xAI’s safety stance looks weak, with high safety-team turnover and leadership downplaying dedicated safety roles while encouraging fast pushes to production. That combination raises real concerns about inadequate oversight and testing.

Breaking: Marcus weighs in (mostly) for LeCun

Marcus on AI • 12291 implied HN points • 06 Jan 26

🕹 Technology Artificial Intelligence

Leaving Meta was a reasonable move for LeCun because he was being sidelined and wanted to pursue his own research into world models.
Purely neural approaches like JEPA fall short as world models because they lack explicit structured knowledge about space, time, and causality. Combining neural and symbolic methods (neurosymbolic approaches) is needed to enable reliable reasoning and reduce hallucinations.
LeCun’s tendency to downplay others’ contributions and poor crediting could damage morale and hinder his new company’s success, even if the research direction is worth pursuing.

Why ChatGPT can’t be trusted with breaking news

Marcus on AI • 13161 implied HN points • 03 Jan 26

🕹 Technology Artificial Intelligence

Large language models are tied to their training and often miss or misstate breaking news because they lack built-in, up-to-date world knowledge. They can’t on their own consult current reputable reports.
Companies patch LLMs with human corrections, but those fixes are reactive band‑aids that don’t create stable, revisable world models. The cycle repeats as new errors appear.
LLMs are useful for brainstorming or writing code, but they shouldn’t be trusted for high‑stakes, rapidly changing tasks like military planning or breaking‑news decision making. Use them for low‑stakes creative work, not critical operations.

✨ The race to economic supremacy in the Age of AGI

Faster, Please! • 1553 implied HN points • 03 Mar 26

🕹 Technology Artificial Intelligence

AI could be a powerful general-purpose technology like the PC or the internet, bringing big but historically familiar economic change.
If AI reaches human-level general intelligence, it could perform nearly every economically valuable task and radically reshape work and the economy.
How AI is developed and deployed will determine whether the world converges toward shared gains, diverges into greater inequality, or sees one actor achieve runaway economic dominance, sparking a global race for supremacy.

Generic enterprise AI is indefensible

Generating Conversation • 116 implied HN points • 19 Mar 26

🕹 Technology Artificial Intelligence

Trying to be a general intelligence layer for all enterprise data is hard to defend because big model providers can integrate data, templates, and connectors at scale.
Specialized vertical agents win by encoding domain-specific workflows and guardrails, so they can solve complex tasks that general models get wrong or too generic.
Startups should pick a narrow lane and focus on technically hard, company-specific workflows to build a data flywheel and a defensible moat that foundation models can’t easily replicate.

The AI bubble is all over now, baby blue

Marcus on AI • 15295 implied HN points • 26 Dec 25

🕹 Technology Artificial Intelligence

The AI industry looks like a financial bubble that may start collapsing in 2026, with growing signs like heavy debt and strained economics.
Large language models have inherent technical limits—especially their lack of world models—that make them unreliable and hard to monetize, and huge investments haven't fixed this.
Once people accept these limitations as inherent rather than temporary bugs, many promised use cases and valuations will unwind, even though LLMs themselves will continue to exist.

How legacy security companies succeed

Frankly Speaking • 50 implied HN points • 12 Mar 26

🕹 Technology Artificial Intelligence

Legacy security companies must become AI- and agent-friendly by unifying data models at the API level and exposing a consistent context layer so agents can query authoritative, semantic truth rather than relying on dashboards.
They should move from seat-based licensing to infrastructure-style pricing (API calls, tokens, or autonomous actions) and lean on their services and expert teams to provide human-in-the-loop "service-as-software" that guarantees safe, production-ready outcomes.
Surviving the shift requires bold platform plays—deep, integrated acquisitions and enforced platformization that build a unified data lake, not just a stitched UI—otherwise the middleware trap will break agent workflows.

AI #156 Part 1: They Do Mean The Effect On Jobs

Don't Worry About the Vase • 2867 implied HN points • 19 Feb 26

🕹 Technology Artificial Intelligence

AI capabilities are advancing quickly and are already driving measurable productivity gains while also contributing to job displacement in some sectors.
Powerful open models create acute safety and governance risks because techniques can remove guardrails and governments are clashing over military and supply-chain uses, so international coordination and verification are urgently needed.
AI is rapidly commercializing across code, media, legal services, and AR, reshaping business models and markets while raising unresolved questions about ownership, regulation, and trust.

A trillion dollars is a terrible thing to waste

Marcus on AI • 23555 implied HN points • 27 Nov 25

🕹 Technology Artificial Intelligence

Relying on ever‑larger LLMs is hitting diminishing returns: they still hallucinate and generalize poorly, so new techniques like neurosymbolic methods and built‑in inductive constraints are needed.
Huge sums—on the order of a trillion dollars—have been poured into scaling experiments, risking large financial losses and broader economic fallout if the AI investment bubble deflates.
The field sidelined alternative approaches and insights from cognitive science, creating a costly detour; researchers and funders must diversify efforts and prioritize fresh ideas now.

Three years on, ChatGPT still isn't what it was cracked up to be – and it probably never will be

Marcus on AI • 22883 implied HN points • 29 Nov 25

🕹 Technology Artificial Intelligence

Large language models are impressive but still unreliable: they hallucinate, struggle with robust reasoning and alignment, and scaling alone hasn’t fixed those core flaws.
The hype around these models overstated their business and productivity value, and adoption, ROI, and profits have been weaker than promised as LLMs become commoditized.
We need new, more structured approaches (like neurosymbolic systems and explicit world models) instead of only bigger models, because continuing the same path risks wasted resources and social harms.

The battery breakthrough we've been waiting for? (Odds and Ends #90)

Odds and Ends of History • 670 implied HN points • 12 Mar 26

🕹 Technology Artificial Intelligence

A featured podcast episode covers opening NHS data for scientific research and explains how the Net Zero transition makes electricity pricing much more complicated.
Coverage mixes politics and tech, with pieces on what the collapse of communism teaches the abundance movement, analysis of Labour’s 'hero voters', and tech stories like a possible EV charging/battery breakthrough plus a sharp takedown of a bad AI argument.
There’s a short take on Britain’s Eurovision entry and its chances, and longer essay content is behind a subscription (a 7‑day free trial is offered), though the planned essay has been delayed by illness.

The Sequence Opinion #827: Taming the Agentic Lobster: Learning from OpenClaw

TheSequence • 224 implied HN points • 19 Mar 26

🕹 Technology Artificial Intelligence

AI is shifting from stateless, passive LLMs to active, stateful agents that keep persistent memory and can take actions in the world.
OpenClaw is an open-source local daemon that connects to an LLM and orchestrates workflows across messaging apps, the local file system, and the web.
OpenClaw’s architecture acts as a blueprint for production-grade agentic systems, showing how orchestration layers let models be autonomous and integrated into real workflows.

Well, That Happened Fast

Contemplations on the Tree of Woe • 2669 implied HN points • 06 Feb 26

🕹 Technology Artificial Intelligence

Major institutions and influential groups are converging on the view that AGI-level systems exist now, treating long-horizon agents as functionally general intelligence.
Recent product releases, model updates, and market reactions show AI is already doing complex, long tasks and disrupting industries; claims of recursive self-improvement imply progress could accelerate rapidly.
This convergence and capability are already reshaping markets, policy, and strategy, so individuals and organizations should plan for major economic and social disruption with both upside and downside outcomes.

AI and the Age of Individual Empowerment

Big Technology • 6380 implied HN points • 16 Jan 26

🕹 Technology Artificial Intelligence

Large organizations struggle to deploy AI quickly because of bureaucracy, security concerns, and the technology’s current limitations.
Individuals can adopt powerful AI tools on their own to analyze data and build workflows, getting useful results without waiting for corporate approval.
This split will create big performance gaps between people who use AI well and those who don’t, and will pressure slow-moving companies to change in uncomfortable ways.

LLM Hallucinations Are Still Absolutely Nuts

Freddie deBoer • 10272 implied HN points • 05 Jan 26

🕹 Technology Artificial Intelligence

Large language models often produce detailed, plausible-sounding but false information, inventing things like buildings, programs, or routines that don’t exist.
Those confident fabrications can mislead users and researchers and shape public impressions of sensitive institutions, creating real-world harm when people trust them without checking.
Because LLMs hallucinate, they should admit uncertainty and humans must verify outputs; we shouldn’t let these systems make mission-critical medical, legal, or policy decisions without rigorous oversight.

Links For December 2025

Astral Codex Ten • 18651 implied HN points • 10 Dec 25

🕹 Technology Artificial Intelligence

AI is now the dominant political and technological battleground, driving fights over regulation, funding, and geopolitics like chip exports and PAC spending.
Many hyped tech and biotech ventures make grand claims and show warning signs of fraud or shaky science, so investors and users should be skeptical and favor proven alternatives.
AI’s spread will upend jobs and even the role of wealthy capitalists, creating pressure for redistribution or new power dynamics, so governments need better transparency, auditing, and realistic regulation.

The Markets' AI Guessing Game, Moltbook Explained, AI-Induced Layoffs?

Big Technology • 3377 implied HN points • 02 Feb 26

🕹 Technology Artificial Intelligence

The market doesn’t know who will win the AI race, so small earnings details or capex moves spark huge stock swings and sustained volatility.
Moltbook shows what an agent-driven social layer could look like, but most posts aren’t truly autonomous and the platform raises real moderation, impersonation, and security worries.
Layoffs branded as ‘AI-induced’ often reflect firms acting on AI’s anticipated future impact rather than current performance, so AI is a factor but not always the direct cause.

AI #155: Welcome to Recursive Self-Improvement

Don't Worry About the Vase • 3225 implied HN points • 12 Feb 26

🕹 Technology Artificial Intelligence

AI capabilities are accelerating rapidly, with new model releases improving agentic coding, in-context continual learning, and media generation so fast that benchmarks and measurement struggle to keep up.
These advances are already reshaping economies and work: automation and agentic tools threaten many jobs, trigger volatile market reactions, and push companies toward new monetization and product strategies like ads and verticalized offerings.
Safety, alignment, and governance remain urgent unresolved problems; researchers are worried or leaving, red lines get crossed, and connecting powerful models to real-world systems (labs, agents, surveillance) creates legal and existential risks we aren’t yet managing.

Anthropic’s New AI Report Accidentally Reveals an Industry-Sized Weak Spot

The Algorithmic Bridge • 828 implied HN points • 06 Mar 26

🕹 Technology Artificial Intelligence

A metric that mixes LLMs' theoretical abilities with real-world usage reveals a huge gap between what models could do and what they're actually used for. For example, models theoretically cover ~94% of computer/math tasks but are used for only ~33%, and a similar gap appears in legal work (~90% vs ~20%).
There are two ways to read this gap: one is optimistic that adoption will expand until real use matches theoretical capability, and the other is that the gap shows real limits and inflated lab benchmarks rather than a temporary lag.
The practical lesson is that the industry may be overestimating AI's near-term labor impact and needs to focus on rigorous evidence of real-world competence and adoption, not just benchmarked capabilities.

Two takes on AI and the future of America

Marcus on AI • 11461 implied HN points • 23 Dec 25

🕹 Technology Artificial Intelligence

Huge bets on large language models have driven a boom in chips and data center construction, but real-world performance and trust are lagging, so those assets could become overvalued and risky.
Multiple studies and company experiences show generative AI often fails to deliver the promised productivity gains and can sometimes harm outcomes, so it’s premature to treat it as a guaranteed productivity revolution.
Putting an entire economy or national strategy all-in on generative AI is dangerous; diversification and cautious risk management are needed to avoid big losses or calls for bailouts.

The First Trialectic - "Can Computers Teach Us to Act in Good Faith?"

Heir to the Thought • 159 implied HN points • 25 Oct 24

🕹 Technology Artificial Intelligence

The Trialectic is a new debate format involving three speakers to encourage richer discussions. It shifts the focus from winning to collaborative learning, allowing participants to explore diverse perspectives.
Computers cannot teach us directly about good faith, but they can influence how we understand and engage with it. They can help identify bad faith through structural guidelines and data-driven insights.
Having open and honest conversations is essential for improving trust in discussions. Recognizing that communication is complex helps us navigate different interpretations and encourages understanding among participants.

222. Automating Processes with Software is HARD

Hardcore Software • 1686 implied HN points • 03 Oct 24

🕹 Technology Artificial Intelligence

Automating processes is often harder than people think. It's not just about making things easier, but figuring out how to handle all the unexpected situations that come up.
Most automation systems are fragile and can easily break if inputs or steps aren't just right. This makes dealing with exceptions, rather than routine tasks, the real challenge in automation.
The future of automation might not be about fixing the tasks we already have. Instead, it could lead to new ways of doing things that we haven't thought of yet.

My thoughts on AI safety

Noahpinion • 13353 implied HN points • 15 Dec 25

🕹 Technology Artificial Intelligence

A superintelligent AI could conceivably pose an existential risk, but what it would want or do is largely unknowable.
Trying to prevent every possible risk by banning or imprisoning researchers would likely stall important technological progress and is probably a bad way to live.
Many other technologies and social changes also carry catastrophic risks, so we should favor cautious, practical risk reduction over total avoidance and pay attention to the realistic dangers we face now.

“Scale Is All You Need” is dead

Marcus on AI • 14307 implied HN points • 08 Dec 25

🕹 Technology Artificial Intelligence

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.
Current generative models are still unreliable — they hallucinate, struggle with reasoning and facts, and many businesses aren’t seeing the promised ROI.
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 Artificial Intelligence

A sharp cartoon captured and critiqued the hype around AI, showing how popular narratives can run ahead of what the technology actually delivers.
Recent essays stress that LLMs still hallucinate, struggle with true generalization, and operate very differently from human reasoning, exposing key technical limits.
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.

45 Thoughts About Agents

Am I Stronger Yet? • 846 implied HN points • 02 Mar 26

🕹 Technology Artificial Intelligence

AI agents are the fastest-moving layer of the AI stack and are accelerating capabilities through rapid software updates and user-driven experimentation. They make ambitious tasks feasible and are already changing what people can build and how quickly.
Getting real value from agents means reshaping workflows: pick agent-shaped tasks, give very clear success criteria, and have agents check their own work or use separate checkers to avoid endless revision loops. Good prompts and orchestration often save far more time than fixing sloppy outputs.
Widespread agent use will create big productivity gains and new kinds of risk at the same time — think compute limits, safety tradeoffs, and the possibility of autonomous or rogue agents — so adoption will bring fast cultural change and new policy questions.

The (possibly) coming AI backlash and information warfare

Marcus on AI • 7469 implied HN points • 06 Jan 26

🕹 Technology Artificial Intelligence

The AI boom could unravel next year as costs, weak economics, and poor regulation make big AI projects look unprofitable and prompt political and industry backtracking.
Generative AI is exceptionally good at patient, amoral mimicry, making it a powerful tool for producing mis- and disinformation at scale.
That surge in synthetic misinformation will erode public trust and create a fog of war where false pretexts can start or escalate conflicts and sow widespread chaos.

The Sequence AI of the Week #826: Sleep While it Computes: Inside Karpathy’s AutoResearch

TheSequence • 189 implied HN points • 18 Mar 26

🕹 Technology Artificial Intelligence

AI research is often bottlenecked by humans having to run, wait for, and evaluate experiments, which keeps the research loop slow.
AutoResearch is an agentic setup that autonomously forms hypotheses, edits code, launches training runs, and evaluates results so experiments can run without constant human intervention.
Letting machines handle the experiment loop lets research proceed at machine speed, greatly speeding up progress and reducing the need for slow, synchronous human coordination.

RobotEra Snatches Silver in Sock Inversion

General Robots • 244 implied HN points • 13 Mar 26

🕹 Technology Artificial Intelligence

RobotEra beat the previous sock-inversion time by 30%, earning a silver medal under the contest rules.
Longer fingers let the robot bunch the sock onto the gripper faster because it didn’t have to pack the fabric as tightly.
They raised action frequency while shortening each planning horizon, making the controller more reactive and precise at high speed but trading off some long-range planning.

AI Davos: Four Reflections From The World’s Most Exclusive Tech Conference

Big Technology • 3502 implied HN points • 23 Jan 26

🕹 Technology Artificial Intelligence

People are debating whether the AI surge is a bubble or just a strong tech investment cycle. Some parts of the industry look frothy and a correction and consolidation are likely, which will make the next few years volatile.
The market for AI devices could be enormous — forecasts talk about billions of always‑with‑you agents in the form of glasses, rings, watches, or desk devices. These products will only take off if they prove more useful than an app on your phone.
Big tech is racing to ship wearable AI products: Google is gearing up for a major push in AI glasses soon, and other firms, including OpenAI, are moving on device plans while pursuing large funding and scaling revenue.

The gentle obsolescence

benn.substack • 1943 implied HN points • 06 Feb 26

🕹 Technology Artificial Intelligence

AI is widely seen as a helpful but imperfect intern that can do many chores for us while still making odd or costly mistakes.
Newer AI systems actively ask clarifying questions and nudge decisions, and they often solve problems and make choices better than most people can.
Because AI is getting better at reasoning and self-improvement, we’ll rely on it more and need to rethink our roles and how much decision-making power we keep.

AI Just Entered Its Manhattan Project Era

The Algorithmic Bridge • 902 implied HN points • 01 Mar 26

🕹 Technology Artificial Intelligence

Anthropic’s refusal to accept blanket “any lawful use” terms triggered a DoD showdown and opened the door for OpenAI, but the commercial damage to Anthropic is likely small and the immediate drama will probably fade.
This episode shows AI is shifting from a mostly technical competition to a political and geopolitical fight, with governments ready to use procurement, law, and power to control strategic AI capabilities.
Public boycotts and user exoduses can create noise but are unlikely to reorder the market; access to government partnerships, regulation, and geopolitical leverage will matter far more going forward.

#256: The "world" according to AI pushers

Maybe Baby • 1439 implied HN points • 15 Feb 26

🕹 Technology Artificial Intelligence

AI boosters often talk about the future in abstract terms like efficiency and productivity, while overlooking the everyday, physical things that make life meaningful. The way they frame the world feels detached from lived experience.
Large language models are impressive at formulaic white‑collar tasks and will change many jobs, but their language lacks lived imagery and can feel hollow compared with human expression. They can mimic patterns without actually experiencing the world.
Much of the AI conversation is market‑driven and self‑interested, urging individuals to adopt tools to get ahead rather than proposing collective policy or real societal solutions. The industry sometimes seems to sell the feeling of productivity more than tangible, shared benefits.