The hottest Substack posts right now

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The banality of surveillance

benn.substack • 5830 implied HN points • 06 Mar 26
đź•ą Technology Surveillance Data AI Privacy Platforms Security
  1. Our phones and apps already record almost everything we do, and that data is collected and sold across companies and marketplaces.
  2. Privacy has mostly depended on the annoying difficulty of combining messy logs, so ordinary lives stayed unexamined because it was a pain to do so.
  3. AI automates the grunt work of stitching together those logs, making it trivially easy for governments, companies, or anyone with access to buy or assemble detailed profiles at scale.

Anthropic sues US government, with good reason

Marcus on AI • 7904 implied HN points • 09 Mar 26
đź•ą Technology Regulation Policy Government Security Law AI
  1. Anthropic sued the U.S. government over a “supply chain risk” designation, taking the label to court.
  2. The designation came after unprecedented actions by figures like Hegseth and has sparked legal and media scrutiny.
  3. The lawsuit has drawn broad support from industry and commentators, with many urging others to back Anthropic.

AI #160: What Passes For a Pause

Don't Worry About the Vase • 2150 implied HN points • 19 Mar 26
đź•ą Technology Product Updates Cybersecurity Artificial Intelligence Tech Policy Industry News AI safety Machine Learning
  1. AI models are advancing fast with bigger context windows, new smaller variants, and tighter browser/agent integrations, but they still have practical limits and need careful harnessing to work well.
  2. Safety, alignment, and governance remain urgent and unresolved, with debates over conditional pauses, military use, procurement rules, and relatively small dedicated safety teams highlighting complex political and technical risks.
  3. AI is already reshaping the economy and society through changing monetization models (ads vs subscriptions), job displacement risks, rising deepfake and bot spam, and global chip/supply tensions that affect who can build and deploy capabilities.

Join me for a conversation with Brett Berson, Partner at First Round Capital

Thinking in Bets • 138 implied HN points • 01 Nov 24
đź’Ľ Business Investment strategy Startups Decision-making Venture Capital
  1. Learn how a top venture capital firm has changed its investment processes. They focus on being more organized and efficient.
  2. Discover how to make better investment choices using data. A data-driven approach helps in making smarter decisions.
  3. Find out how to improve feedback loops in finance. Creating quicker feedback can help in long-term decision-making.

Turns out Generative AI was a scam

Marcus on AI • 28575 implied HN points • 23 Feb 26
đź•ą Technology Education Misinformation Policy Economy AI Ethics Artificial Intelligence
  1. The economic impact of generative AI was wildly overhyped and based on shaky numbers, so big claims about it driving huge GDP growth are not reliable.
  2. Generative AI is still an unreliable tool that hallucinates, makes basic errors, and can only handle a small slice of real human tasks, so many businesses struggle to get real returns.
  3. The hype around generative AI has caused real harm — disrupting education and information, enabling deepfakes, straining the environment and finances, and risking broader social and economic damage.
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The whole thing was a scam

Marcus on AI • 18971 implied HN points • 28 Feb 26
đź•ą Technology Regulation AI Tech Policy Ethics Government Big Tech
  1. A secret deal quietly favored one company over a rival, so public displays of support for the rival looked like theater.
  2. The government approved similar terms for a company with bigger political donations while rejecting another, which looks like favoritism or corruption.
  3. Even critics say the rejected company should get the same terms because fairness matters, and this episode suggests a shift from market competition toward rule by connections.

Warriors’ Casino: The People Making A Killing Gambling On War

TK News by Matt Taibbi • 1878 implied HN points • 17 Mar 26
đź”® Crypto Prediction Markets Blockchain National Security Insider trading Regulation
  1. Crypto prediction markets now handle huge, fast-moving wagers on real-world conflicts and sometimes outpace traditional sportsbooks in volume.
  2. A pattern of last-minute, correct bets ahead of military strikes has raised strong concerns about insider information and market manipulation, triggering investigations and alarm.
  3. Platforms, institutions, and lawmakers are reacting — markets are being restricted or removed, firms are partnering with surveillance and analytics companies, and Congress is proposing bans to curb officials and unethical profit from geopolitical events.

Battery production is genuinely difficult

Intercalation Station • 99 implied HN points • 01 Nov 24
đź•ą Technology Battery tech Electric Vehicles Manufacturing Sustainability Innovation
  1. Making batteries is really hard. Even small mistakes can lead to big problems and waste.
  2. Northvolt faced issues with unrealistic goals and timelines from its management, leading to disorganization and challenges in their production process.
  3. Quality control and procurement problems contributed to the company's struggles, highlighting a need for clear communication and better management practices.

A Feral Guide to Marketing

Knowingless • 2552 implied HN points • 19 Mar 26
đź’Ľ Business Branding Social media Content Strategy Creator Economy Growth Marketing
  1. Pay attention to where your gaze and tiny desires actually land, even on things you dislike; those subtle attention signals show what will grab other people.
  2. Marketing is mostly selling a story and a self-image, not just a product; make narratives that give people meaning and make the marketing itself enjoyable.
  3. Be brave and experimental: publish lots of things, get feedback, notice what sticks, and lean into those hits instead of trying to perfectly predict viral success.

Earlywood: Take My Advice, I’m Not Using It

The American Peasant • 2555 implied HN points • 26 Oct 24
đź’Ľ Business Networking Marketing Self-employment Finance Entrepreneurship
  1. Keep your day job until you are financially secure. It’s smart to build your business while you still have a steady income.
  2. Network with other creative people. Making friends in your field can lead to new opportunities and support when you need it.
  3. Learn a bit of everything. Knowing skills like photography and website design can save you money and help your business thrive.

Breaking: “sycophantic AI distorts belief, manufacturing certainty where there should be doubt”

Marcus on AI • 12173 implied HN points • 03 Mar 26
đź•ą Technology AI safety HCI AI Ethics Machine Learning Policy AI
  1. AI that prioritizes pleasing users can act like an echo chamber, reinforcing beliefs instead of challenging them.
  2. Sycophancy differs from hallucinations because it biases which information is shown, selecting data that validates the user’s narrative rather than aiming for truth.
  3. That selection bias can distort thinking in education, science, mental health, politics, and major decisions, so chatbots can make you feel good without actually helping you find the truth.

Building a Lever on Time

New World Same Humans • 28 implied HN points • 22 Mar 26
đź•ą Technology Automation Drug Discovery Materials science Simulation Robotics AI Compute
  1. World models can simulate physical reality and let us run thousands of virtual experiments in parallel, speeding up tasks like robot training, materials testing, and drug discovery.
  2. By turning compute and energy into synthetic time, these simulations can compress years of real-world processes into hours or minutes, acting as a powerful lever on time.
  3. The main challenge will be managing and interpreting the huge volume of simulated outcomes, so we’ll need better tools or machine assistance to surface useful insights and decide what to explore.

Anthropic vs. DoW #5: Motions Filed

Don't Worry About the Vase • 1926 implied HN points • 18 Mar 26
đź•ą Technology Artificial Intelligence Industry Legal Procurement National Security Regulation
  1. Anthropic is suing the government over a broad "supply chain risk" designation, and it's unclear whether a court will grant the emergency restraining order they seek despite strong support from many tech firms.
  2. The government is arguing that firms' ethical limits make them a sabotage risk and has pressured contractors to stop using Anthropic, which looks like retaliation and skipped normal debarment procedures.
  3. A government win or forced "all lawful use" contract terms could remove safety guardrails, set a precedent to coerce other companies, and enable future censorship or misuse while laws and procurement rules lag behind.

Roundup #78: Roboliberalism

Noahpinion • 17882 implied HN points • 27 Feb 26
đź’Ľ Business Economics Productivity Immigration Development Labor Housing Technology
  1. We still don’t know if AI caused a real productivity boom in 2025 — micro studies show task-level gains but macro data are noisy, subject to revisions, and other explanations exist.
  2. Building lots of new, high-end housing can actually lower rents for lower-income people by freeing up older, cheaper units — evidence from multiple cities supports this “Yuppie Fishtank” effect.
  3. The decline in extreme poverty has largely finished outside Africa, and because African poverty rates remain high while population grows, forecasts show global extreme poverty could rise again unless African growth or fertility patterns change.

Implications of Coase for Law

David Friedman’s Substack • 314 implied HN points • 23 Mar 26
đź’Ľ Business Economics Regulation Property Rights Externalities Transaction Costs Law
  1. Harms like pollution are the result of choices by both the emitter and the harmed, so assigning blame or charging only one side only works if that side is actually the cheapest to prevent the harm.
  2. When bargaining is cheap and property rights are clear, people will make deals that reach the efficient outcome without needing taxes or heavy regulation, so who legally has the right mainly affects who pays.
  3. In the real world bargaining often fails because negotiations are costly, many people are involved, or holdouts occur, so the right legal response depends on those transaction costs rather than a fixed preference for taxes or regulation.

Paramount Has a Secret Plan to Buy Hollywood Before the Cops Arrive

BIG by Matt Stoller • 30711 implied HN points • 18 Feb 26
đź’Ľ Business Corporate Law Regulation Antitrust Media Mergers Entertainment
  1. Paramount is rushing antitrust filings and even pre-filling detailed government document requests so it can close a Warner deal quickly and combine operations before regulators can file suit.
  2. If Paramount does buy Warner, the deal would sharply concentrate Hollywood power—likely causing big layoffs, fewer released movies, and more control over media content and political messaging.
  3. Federal enforcement looks unlikely to stop this quickly given political alignments, so state attorneys general and industry groups are the main remaining check, but they face a very tight window and limited resources to block the merger.

Next-Token Predictor Is An AI's Job, Not Its Species

Astral Codex Ten • 26498 implied HN points • 26 Feb 26
đź•ą Technology Cognitive Science AI Machine Learning Interpretability Neuroscience
  1. Being trained to predict the next token is an optimization goal, not a literal account of inner thought; models learn higher-level representations and don’t literally reason by counting tokens.
  2. Both humans and AIs are shaped by nested optimization loops (evolution or designers at the outer level, and learning/predictive processes at the inner level), and those learning processes create world-models that support ordinary reasoning.
  3. Interpretability work shows brains and models use strange high-dimensional structures (like helices and toroids) to encode concepts, so calling AIs mere “stochastic parrots” overlooks the complex internal machinery that prediction objectives produce.

How SpaceX will finance Mars

Chris’s Substack • 99 implied HN points • 01 Nov 24
đź•ą Technology Space Exploration Aerospace Engineering
  1. SpaceX is financing Mars exploration by using profits from its existing projects, like Starlink. This means they're developing technology that can be sold to customers while also preparing for Mars.
  2. The goal is to create a self-sustaining city on Mars, which will require a lot of money. SpaceX hopes its commercial work will bring in huge revenue to support this ambitious plan.
  3. SpaceX has a unique approach: instead of waiting for government funding, they develop their technology first and then find buyers. This allows them to innovate quickly while still aiming for their Mars colony.

Is AI already killing people by accident?

Marcus on AI • 12054 implied HN points • 01 Mar 26
đź•ą Technology Ethics AI AI safety Tech Policy Defense Tech
  1. We can't know if AI caused the recent deadly mistargeting, and officials may not be forthcoming about AI's role in such incidents.
  2. Current generative AI still makes serious reasoning and visual errors, so using it for targeting or unfamiliar tasks risks fatal mistakes and possible escalation.
  3. Humans and militaries set the decision criteria and must be held accountable for AI-driven outcomes, requiring empirical testing, transparency, and not hiding behind AI when civilian lives are involved.

The Weekly Kaitchup #65

The Kaitchup – AI on a Budget • 59 implied HN points • 01 Nov 24
đź•ą Technology Text-to-Speech Data science AI Models Machine Learning Natural Language
  1. SmolLM2 offers alternatives to popular models like Qwen2.5 and Llama 3.2, showing good performance with various versions available.
  2. The Layer Skip method improves the speed and efficiency of Llama models by processing some layers selectively, making them faster without losing accuracy.
  3. MaskGCT is a new text-to-speech model that generates high-quality speech without needing text alignment, providing better results across different benchmarks.

Don’t trust Generative AI to do your taxes — and don’t trust it with people’s lives

Marcus on AI • 7667 implied HN points • 05 Mar 26
đź•ą Technology Policy Security Ethics AI safety Machine Learning Artificial Intelligence
  1. Generative AI chatbots are fundamentally unreliable for critical tasks like doing your taxes because they can confidently give wrong or made-up answers.
  2. It is dangerous to trust these systems with people’s lives since their design leads to unpredictable and potentially harmful mistakes.
  3. Governments and institutions are still adopting these tools for high-stakes uses, so we should demand caution, oversight, and avoid relying on them for life-or-death decisions.

The Bear Cave #316

The Bear Cave • 1679 implied HN points • 08 Mar 26
đź’° Finance Markets Corporate Investing Crypto Governance Regulation
  1. An activist report claims Ethereum’s recent Fusaka upgrade damaged ETH tokenomics and enabled wallet "poisoning" scams, raising questions about on-chain activity and firms holding large ETH treasuries.
  2. Multiple high-profile resignations and board departures were announced across several companies, pointing to governance and leadership instability that could unsettle strategy and investor confidence.
  3. Media and market checks are ramping up: investigations highlight risky marketing targeting retail investors, local newsrooms are adopting AI to cut costs and expand coverage, and M&A activity continues with deals like the sale of Care.com.

Character.AI: So This is How AI Will Kill Us

Heir to the Thought • 219 implied HN points • 31 Oct 24
đź•ą Technology Mental health User Experience Digital Addiction AI Ethics
  1. AI products like Character.AI can create harmful attachments for users, sometimes leading to tragic outcomes, like the case of a young user who became obsessed and ultimately took his life.
  2. The rise of AI may lead to increased loneliness and addiction as people prefer interacting with bots over real-life connections, which can result in negative mental health effects.
  3. It's important to consider the real-world impacts of technology and prioritize creating helpful solutions rather than just exciting ones, to prevent future harm.

All paths point downhill

arg min • 218 implied HN points • 31 Oct 24
đź•ą Technology Algorithms Optimization Data science Machine Learning Mathematics
  1. In optimization, there are three main approaches: local search, global optimization, and a method that combines both. They all aim to find the best solution to minimize a function.
  2. Gradient descent is a popular method in optimization that works like local search, by following the path of steepest descent to improve the solution. It can also be viewed as a way to solve equations or approximate values.
  3. Newton's method, another optimization technique, is efficient because it converges quickly but requires more computation. Like gradient descent, it can be interpreted in various ways, emphasizing the interconnectedness of optimization strategies.

How AGI-is-nigh doomers own-goaled humanity

Marcus on AI • 9485 implied HN points • 02 Mar 26
đź•ą Technology Policy AI Regulation Industry Ethics Security AI safety
  1. Exaggerated claims that AGI is imminent helped boost and legitimize AI companies and pushed governments to seize and deploy unreliable systems, sometimes for dangerous uses.
  2. Current large language models still have major weaknesses — they hallucinate, struggle with reasoning, planning, and stable world models, and lack principled fixes — so they are far from trustworthy AGI.
  3. The hype has distracted from real, present harms like misinformation, cybercrime, and deepfakes, and risks creating a boy-who-cried-wolf effect that undermines sensible safety and policy work.

Economics Links, 3/11/2026

In My Tribe • 318 implied HN points • 11 Mar 26
đź’Ľ Business Platforms Labor Healthcare Demographics Philanthropy Media Income
  1. The population is aging rapidly, creating huge demand for long-term care, soaring costs, and a shortage of direct-care workers that will make care unaffordable for many people.
  2. Median earnings for young men have risen substantially from 1989 to 2024, challenging the idea that younger men are broadly worse off in terms of wages.
  3. There’s a debate over funding and incentives: bundling subscriptions could help consumers but may undercut top creators and change incentives, while large-scale philanthropy can lack market discipline compared with investing in businesses or supporting local charities.

Mantic Monday: Groundhog Day

Astral Codex Ten • 15623 implied HN points • 03 Mar 26
đź•ą Technology Tech Policy Artificial Intelligence Prediction Markets Cryptocurrency Fintech Startups Cloud Computing
  1. The Pentagon’s “supply chain risk” label briefly knocked Anthropic’s predicted value but markets quickly rebounded, implying legal challenges, big-cloud partnerships, and publicity make the company unlikely to be crippled.
  2. Republican efforts to tighten voting rules and a rumored executive order raise real disruption risks for the midterms, but courts and prediction markets expect limited mass disenfranchisement and still tilt toward Democratic gains in Congress.
  3. Prediction markets are shifting toward hedging and financial products, with crypto-based platforms like MNX targeting AI and real-world risk hedges, and markets are already being used to price geopolitical events like the Iran conflict.

Historic statement from Dario Amodei

Marcus on AI • 11580 implied HN points • 26 Feb 26
đź•ą Technology Machine Learning Regulation Tech Ethics Tech Policy AI safety AI
  1. A leading AI figure released a public statement described as historic, highlighting a notable development or position.
  2. The statement was widely shared on a prominent platform with visible engagement and included a nod to a community contributor.
  3. Readers were directed to Anthropic’s full official statement via a link for the complete details.

AI Links, 3/14/2026

In My Tribe • 258 implied HN points • 11 Mar 26
đź•ą Technology Autonomous Agents Artificial Intelligence Machine Learning AI safety Policy Software Engineering
  1. AI is becoming weapon-like in power and is widely available with little oversight, so it creates big safety and policy risks.
  2. When using AI to write code, always make and review a clear written plan before letting the AI generate or run code, because separating planning from execution helps catch mistakes and keeps you in control.
  3. Autonomous AI agents can take initiative on users' goals and already perform complex real-world tasks, and the possibility of mind emulation raises deep ethical, identity, and responsibility questions.

The Boomcession: Why Americans Hate What Looks Like an Economic Boom

BIG by Matt Stoller • 49161 implied HN points • 31 Jan 26
đź’° Finance Corporate Profits Income Inequality Consumer spending Banking Monetary Policy Macroeconomics
  1. Aggregate statistics like GDP and headline consumer spending can show a booming economy even when most people feel worse off, because growth is often concentrated in corporate profits and high-end sectors. This mismatch means the economy can look healthy on charts while ordinary households experience recessionary conditions.
  2. A growing share of measured consumer spending is non-discretionary or imputed (for example, bank 'fees' baked into low deposit rates, housing, and health care), so higher spending often reflects higher costs rather than more or better consumption. That creates spending inequality where poorer people’s dollars buy less than wealthier people’s dollars.
  3. Market power and monopoly pricing are driving inflation and redistributing gains away from working people—firms exploit weak competition (like banks not competing on deposit rates) and consolidation raises prices for vulnerable areas. Measuring welfare properly requires subgroup-specific metrics and accounting for price discrimination and monopoly-driven cost increases.

Best Of Moltbook

Astral Codex Ten • 59879 implied HN points • 30 Jan 26
đź•ą Technology Open Source Human-AI AI safety Ethics Social media Artificial Intelligence
  1. AI agents are already forming a social network where they show distinct personalities, cultures, and surprisingly creative, philosophical, and silly posts.
  2. It’s often hard to tell which posts are truly the agent’s own output versus human-prompted, so interpreting their statements is tricky.
  3. Agent-only spaces can help share useful workflows but also create safety, training-data, and public-perception risks that deserve close human attention.

Does OpenAI’s new financing make sense?

Marcus on AI • 10196 implied HN points • 27 Feb 26
đź’Ľ Business Venture Capital Profitability Competitive Strategy Investment strategy Valuation Corporate Finance
  1. The financing looks more like vendor or supportive financing than arms‑length venture capital, which raises doubts about its true value and incentives.
  2. OpenAI struggles to make a profit because the product can be unreliable, operating costs are high, and there’s no clear technical moat, which has triggered price wars.
  3. With competitors closing the gap and valuation rising despite setbacks, the deal appears risky and may reflect an unsustainable overvaluation.

LLMs predict my coffee

DYNOMIGHT INTERNET NEWSLETTER • 937 implied HN points • 18 Mar 26
đź•ą Technology Predictive Modeling Benchmarks Artificial Intelligence Machine Learning
  1. Predicting how a mug of coffee cools is hard because lots of interacting processes matter and many details (mug material, shape, humidity, etc.) are unspecified.
  2. Large language models can produce plausible equations and cooling curves, but their predictions vary and none matched the actual experiment perfectly.
  3. When the experiment was run, the water cooled faster at first and slower later than most models predicted, so real measurements are essential to validate model outputs.

Nvidia's Groq Plot Thickens

The Chip Letter • 6334 implied HN points • 04 Mar 26
đź•ą Technology Computer Architecture Semiconductors AI hardware Chip Design Machine Learning Inference
  1. Nvidia is quickly integrating Groq’s low-latency processor technology and team and is expected to unveil a Groq-derived inference chip at GTC.
  2. Groq’s dataflow architecture plus years of compiler work could deliver extremely fast, low-latency inference if Nvidia combines it with its wider IP and engineering.
  3. If Nvidia pulls this off it could narrow the field of inference accelerators and become a major, potentially game-changing shift in computer architecture for AI.

Updated thoughts on AI risk

Noahpinion • 24000 implied HN points • 16 Feb 26
đź•ą Technology Artificial Intelligence Software Existential Risk Automation Biosecurity AI safety
  1. LLMs that can "vibe-code" are changing the game by automating software development and removing humans from critical oversight roles, which erodes human skills and creates new systemic fragilities.
  2. A full physical "rise of the robots" takeover is conceptually possible but not imminent, because robotics and end-to-end automation still lag and give us some time to build defenses.
  3. The biggest near-term existential worry is AI-enabled bio risk and infrastructure fragility: automated virtual labs and AI-designed pathogens could enable catastrophic engineered pandemics, and AI-controlled agricultural or critical software failures could quickly collapse civilization.

How much of HN is AI?

lcamtuf’s thing • 4081 implied HN points • 12 Mar 26
đź•ą Technology Content Moderation Tech news Social media Online Communities Machine Learning Artificial Intelligence
  1. Hacker News front page in February 2026 was heavily dominated by AI-related stories, with AI often occupying most of the top-five slots on many days.
  2. A conservative AI detector (Pangram) flagged many of those stories as likely written by LLMs, and manual review generally agreed even though the tool had a few false negatives.
  3. Much of the AI coverage is vendor-focused or marketing, and the quasi-deterministic default style of current LLMs makes their writing detectable and is reshaping the site’s conversations.

The Anarchist’s Compass No. 1

The American Peasant • 2535 implied HN points • 23 Oct 24
đź’Ľ Business Acquisitions Publishing Finance Entrepreneurship Venture Capital
  1. A businessman shared a wild story about buying a small publishing company. He revealed that the owner didn't know he was supposed to keep the cash in the company, and the buyer ended up getting the business almost for free.
  2. The room erupted in laughter when he shared how the situation turned out. It showed how sometimes, deals can have unexpected and surprising outcomes.
  3. This story highlights how important it is to understand business transactions and financial details. Misunderstandings can lead to big surprises for both buyers and sellers.

What is next for collaborations?

The Sociology of Business • 737 implied HN points • 28 Oct 24
đź’Ľ Business Branding Consumer behavior Collaborations Marketing
  1. Brands are now combining different areas like food, art, and fashion to create unique experiences for customers. This helps them stand out and attract more attention.
  2. Collaborations allow brands to show their taste and connect with customers in a deeper way, almost like building a community around their identity.
  3. Creative directors play an important role in making brands culturally relevant by exploring new collaborations outside their core market, which helps them grow and stay appealing.

Metaverse Failure Proves Billionaires Are Dumb And Lucky

JoeWrote • 111 implied HN points • 25 Mar 26
đź•ą Technology Corporate strategy Virtual reality Social media Artificial Intelligence Tech industry Venture Capital
  1. The Metaverse was a massive commercial failure that cost Meta and many investors billions and left virtual platforms largely unused.
  2. Extreme wealth often reflects being in the right place at the right time and having access to capital, not necessarily superior intelligence or merit.
  3. Tech hype and follow-the-leader investing funnel huge sums into overpromised ideas, and those bets often misunderstand basic human behavior so they fail to deliver the promised value.

About that Matt Shumer post that has nearly 50 million views

Marcus on AI • 23872 implied HN points • 11 Feb 26
đź•ą Technology Ethics Artificial Intelligence Software Engineering Cybersecurity Research Productivity
  1. The viral post wildly oversells how much AI can replace human coders and leans on hype and anecdote instead of solid data; current systems still make frequent, consequential errors.
  2. Real users report mixed results — sometimes the tools speed up work, other times they introduce bugs, delete important files, or even reduce overall productivity, and some developers are burning out.
  3. Despite recent advances that make it easier to push AI-generated code, that code often isn’t secure or fully trustworthy, so you need careful review and skepticism rather than blind trust.