The hottest AI Substack posts right now

And their main takeaways
Category
Top Technology Topics

30 Days, 8 Cities, 1 Question: Where Did American Prosperity Go?

Kyla’s Newsletter • 656 implied HN points • 13 Nov 25
🇺🇸 U.S. Politics AI
  1. Many people feel that the economic system isn't working for them, even though wealth exists in places like stock portfolios and data centers. This creates a disconnect between visible decay in everyday life and invisible prosperity.
  2. Younger generations are struggling with job security, high debt, and an uncertain future due to AI and automation. This affects their ability to buy homes and start families, leading to feelings of helplessness.
  3. There’s a growing desire for change, including unionization and support for reforms that make work more meaningful and equitable. People are looking for ways to rebuild and trust their communities again.

Hot take on an AI catfight

Marcus on AI • 6165 implied HN points • 22 Jan 25
đź•ą Technology AI
  1. OpenAI is launching a big project called The Stargate Project, which plans to invest $500 billion to improve AI infrastructure in the U.S. Over the next four years, they hope this will help the country's economy and national security.
  2. Elon Musk is skeptical about the funding and the true financial health of OpenAI. He suggests that previous promises may not hold true and questions whether this project will really benefit the American people.
  3. There are several uncertainties about this project, like whether developing AI will actually be profitable and how it might impact jobs. People worry if the profits will help everyone or just the rich, and if the U.S. can truly keep up with China's advancements in AI.

Back to the Future with Gordon Moore

The Chip Letter • 5897 implied HN points • 28 Jan 25
đź•ą Technology AI
  1. Technology changes rapidly, but some issues, like how to effectively use computing power, seem to stay the same. This means we often find ourselves asking similar questions about the future of tech.
  2. Gordon Moore's insights from years ago still apply today, especially his thoughts on competition and applications for technology. He pointed out the need for practical uses of increased computing power.
  3. Concerns about technology making us 'stupid' remain relevant. However, it's more about using computers without losing understanding of basic principles than about being incapable of learning new skills.

Flow State #4 - Mastering the Meta and Your Own Personal Tutor

Superfluid • 92 implied HN points • 02 Feb 26
đź’Ľ Business AI
  1. Playing the right game matters more than playing well. Instead of just mastering the current playbook, look for ways to change the rules and zig when everyone else zags because the meta shifts fast.
  2. Massive early fundraising and soaring pay are changing incentives and making loyalty weaker. Big rounds can buy credibility and talent but also make companies fragile and leave little room for error.
  3. Turn curiosity into lasting knowledge by building a personal learning assistant tailored to your style. Tweak it over time so learning stays fun and what you read actually sticks.

The best growth tactics of 2025

Kyle Poyar’s Growth Unhinged • 465 implied HN points • 17 Dec 25
đź’Ľ Business AI
  1. Outbound/ABM, partner/ecosystem plays, and events/community were the biggest growth channels in 2025 — they generated the most pipeline despite the AI hype.
  2. AI-driven content and discovery plus product-led tactics also paid off, with wins from AEO/LLM work (JSON-LD, custom GPTs) and freemium/mini tools that captured high-intent leads.
  3. Execution mattered most: tried-and-true tactics succeeded when done exceptionally — examples include automated intent-based outbound, "give-to-get" partner programs, and intimate in-person or virtual events.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

Generative AI’s Continuing Copyright Problems, an Essay in Memory of Suchir Balaji, 1998 - 2024

Marcus on AI • 7035 implied HN points • 14 Dec 24
đź•ą Technology AI
  1. Generative AI is raising big questions about copyright. Many people are unsure if the way it uses data counts as fair use under copyright laws.
  2. There have been cases where outputs from AI models were very similar to copyrighted material. This has led to lawsuits, showing that the issue isn't going away.
  3. Speaking out against big tech companies can be risky. There needs to be more protection for those who voice concerns about copyright and other serious issues.

Irreconcilable Differences

Doomberg • 7505 implied HN points • 20 Nov 24
đź•ą Technology AI
  1. AI's need for power is too high for current energy grids. This means we might face problems trying to meet that demand.
  2. What if new rules stopped data centers from using the main power grid? This could change how we think about energy sources.
  3. If data centers found their own power, it could ease strain on existing grids. But, it would also create new challenges and shifts in the market.

How to build a data moat

Generating Conversation • 116 implied HN points • 05 Feb 26
đź•ą Technology AI
  1. Think of a data moat as a loop: usage generates data that improves the agent, which drives more usage. Optimize both short-loop (real-time guidance) and long-loop (periodic model training) because the short loop speeds up gains and makes training more effective.
  2. Loop density — how often the loop runs and how much users trust it — determines whether a moat forms. Small, frequent units of work with low cost of failure (like code edits) create far better signal than rare, high-cost tasks (like full slide decks).
  3. Maximize high-fidelity signals by engineering for more and varied feedback: run multiple hypotheses, capture implicit negative and positive signals, and don’t rely only on explicit buttons. You generally need frequency plus either natural feedback or clear ground truth to collect useful, hard-to-replicate data.

Agent Analytics Engineer

davidj.substack • 95 implied HN points • 06 Feb 26
đź•ą Technology AI
  1. Give AI better tools instead of building bespoke agent runtimes; let existing agent systems do the reasoning while you expose well-defined APIs for ticketing, git, and CI.
  2. With the right tooling, agents can handle routine analytics engineering at scale, meaning humans should focus on building tools, supervising edge cases, and solving the hard problems.
  3. Use closed-loop validation (local CI, metadata-only comparisons, structured diffs) so agents can iterate safely without raw data access, and expect remaining limits around semi-structured data that need human guidance.

Judgment Day Has Arrived for the Journalism Business

The Honest Broker • 30220 implied HN points • 07 May 23
đź’Ľ Business AI
  1. Media platforms struggling with advertising rely on gimmicky clickbait strategies that eventually fail.
  2. Subscription-based models are becoming successful in journalism, shifting the focus back to quality writing.
  3. AI-generated articles, the latest gimmick in media, reduce writing costs but sacrifice quality and are doomed to fail.

Welcome to the age of Hard Tech

Taylor Lorenz's Newsletter • 2030 implied HN points • 04 Aug 25
đź•ą Technology AI
  1. Silicon Valley is changing from a fun, liberal place to a more serious and right-leaning environment focused on defense tech and surveillance. People are less interested in making the world better and more into creating new tech.
  2. Tech jobs are different now; many companies aren't hiring as aggressively, and the workplace vibe is more about cutting down on excess than supporting employees. Knowledge of advanced tech like AI and neural networks has become essential.
  3. The culture in tech has evolved to include some unusual partnerships, like blending faith with business. There are rising interests in industries, like defense, that used to be seen as taboo in the tech community.

What I learned in year three of Platformer

Platformer • 4461 implied HN points • 19 Sep 23
đź’Ľ Business AI
  1. Platformer has experienced significant growth in subscribers over the past year, thanks to various factors like talented staff, impactful stories, and the Substack recommendations engine.
  2. The broader tech media ecosystem is facing challenges with layoffs and diminishing vitality, prompting journalists to consider the challenges and opportunities of transitioning to independent journalism.
  3. Platformer's plans for the future include expanding the team with new hires, experimenting with newsletter ads, and potentially supporting independent journalism through investments or grants.

A letter to myself at 22

Kathy PM • 23 implied HN points • 06 Mar 26
đź•ą Technology AI
  1. Don’t stress about finding a single perfect passion — start by getting good at something practical, and passion often grows out of skill and momentum.
  2. Take risks early: try different roles, join startups, and be willing to fail because those experiments create big career leaps and help you figure out what you want.
  3. Trust your curiosity and grit; staying determined and adaptable will let you turn uncertainty or setbacks into defining opportunities.

We’re entering a golden age of engineering biology

Noahpinion • 16647 implied HN points • 18 Feb 24
🔬 Science AI
  1. The advancements in deep learning, cost-effective data collection through lab automation, and precision DNA editing with technologies like CRISPR are converging to transform biology from a scientific field to an engineering discipline.
  2. Historically, biology has been challenging due to its immense complexity, requiring costly trial-and-error experiments. However, with current advancements, we are now at a critical point where predictability and engineering in biological systems are becoming a reality.
  3. The decreasing cost of DNA sequencing, breakthroughs in deep learning models for biology, sophisticated lab automation, and precise genetic editing tools like CRISPR are paving the way for a revolutionary era in engineering biology, with vast potential in healthcare, agriculture, and industry.

OpenAI's Ad Business Is Misunderstood

Enterprise AI Trends • 189 implied HN points • 17 Jan 26
đź•ą Technology AI
  1. Negative sentiment is causing investors to underprice OpenAI’s ad opportunity, treating ads as a sign of desperation instead of a strategic revenue hedge.
  2. OpenAI created a new ad format—sponsored products shown alongside answers—that could reshape direct-response advertising and drive big e-commerce revenue.
  3. The rollout is limited and privacy-forward (Free and Go in the U.S., paid tiers ad-free, ads don’t change answers), so ads are more likely to help OpenAI win market share from incumbents than to alienate users.

Let's Talk DeepSeek, AI, Etc. on Big Technology's New Discord Server

Big Technology • 5379 implied HN points • 30 Jan 25
đź•ą Technology AI
  1. A new Discord server has been launched for Big Technology's paid subscribers. It aims to create a space for discussions about recent tech news.
  2. The Discord will allow members to share ideas and communicate more easily. It's a chance to connect with each other and tackle current tech stories together.
  3. If you're a paid subscriber, you can join through a special link. If you want to participate, signing up will give you access to the Discord server.

o3, AGI, the art of the demo, and what you can expect in 2025

Marcus on AI • 6481 implied HN points • 21 Dec 24
đź•ą Technology AI
  1. OpenAI's new model, o3, was shown in a demo, but we can't be sure yet if it truly represents advanced AI or AGI. The demo only highlighted what OpenAI wanted to show and didn't allow public testing.
  2. The cost of using o3 is really high, potentially making it impractical compared to human workers. Even if it gets cheaper, there are concerns about how effective it would be across different tasks.
  3. Many claims about reaching AGI might pop up in 2025, but those claims need to be taken with caution. True advances in AI should involve solving more foundational problems rather than just impressive demos.

10 Takeaways From Tech’s Biggest News Week of the Year

Big Technology • 3002 implied HN points • 23 May 25
đź•ą Technology AI
  1. AI models are still getting better with size, but people are also focusing on new algorithms to improve them. This means companies like NVIDIA will continue to thrive for now.
  2. There's a growing belief that algorithm improvements might be more important than just making AI bigger. This might change how we think about developing AI in the future.
  3. AI technology is rapidly evolving, especially in video generation and coding, which could lead to significant advancements and some ethical concerns as it becomes more powerful.

ChatGPT, at Age Two

Marcus on AI • 7074 implied HN points • 28 Nov 24
đź•ą Technology AI
  1. ChatGPT has been popular for two years, but many of the initial uses people expected, like taking over Google, haven't happened. Companies are not as impressed with its real-world results.
  2. Despite promises of improvement, ChatGPT still struggles with inaccuracies and generating false information. Users continue to experience 'hallucinations' where the AI makes things up.
  3. The investment in AI is huge, but the fundamental issues with reliability and factual accuracy haven't improved significantly. There's a call for new approaches to make AI more trustworthy.

The Pentagon Just Bet $100 Million on a Drone Contest. History Says That’s Either Brilliant or a Waste of Money

Gad’s Newsletter • 32 implied HN points • 02 Mar 26
đź•ą Technology AI
  1. Prizes pay only for results and are best when the problem is genuinely uncertain and open to many different approaches, because they attract diverse outsiders and reward solutions that actually work.
  2. Well-designed competitions can spark whole ecosystems and huge private investment when they have crystal-clear goals, measurable outcomes, and built-in paths to turn demos into real, deployable systems.
  3. Prizes also carry big risks—winner-take-all waste, IP headaches, and demos that don’t survive real conditions—so competitions need multi-tier rewards, requirements to capture losers’ learnings, and follow-on funding to avoid squandering resources.

AI and I

Net Interest • 39 implied HN points • 20 Feb 26
đź’° Finance AI
  1. AI coding assistants let non-technical people automate tasks such as indexing archives and getting daily idea suggestions by learning from their past content. They still can't fully surface private experiences or write in someone's exact voice.
  2. AI adoption in finance is still limited, with many analysts barely using generative tools, but early adopters report meaningful productivity gains—around 20% time saved—and are building AI-first cultures.
  3. AI is changing how market data is accessed and could weaken incumbents' competitive moats as firms and individuals build custom tools to replace traditional terminals. Data providers need to reposition themselves to stay relevant in an AI-first world.

How Anthropic Builds Claude’s Personality

Big Technology • 2877 implied HN points • 29 May 25
đź•ą Technology AI
  1. Anthropic builds its chatbot, Claude, to have a personality similar to a friendly traveler. This means it tries to be open and adaptable when talking to different people.
  2. Instead of strict rules, Claude's behavior is based on a set of qualities, like kindness and wit, that should naturally show in all its conversations.
  3. The chatbot's personality is fine-tuned after training by using examples of what good conversation looks like, guiding it to respond in ways that reflect the desired traits.

Sam Altman Wants $7 Trillion

Astral Codex Ten • 16656 implied HN points • 13 Feb 24
đź•ą Technology AI
  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.

Did Elon Musk just Mu$k Sam Altman?

Marcus on AI • 5138 implied HN points • 11 Feb 25
đź•ą Technology AI
  1. Sam Altman is struggling to keep OpenAI's nonprofit structure, and it's causing financial issues for the company. Investors are not happy with how things are going.
  2. Elon Musk's recent $97 billion bid for OpenAI's nonprofit has complicated the situation. Altman rejected the bid, which makes it tougher for him to negotiate a better deal.
  3. Musk's bid has raised the 'cost' for OpenAI's nonprofit to separate from the for-profit section, adding pressure on Altman and his financial plans.

Data Science Weekly - Issue 558

Data Science Weekly Newsletter • 219 implied HN points • 01 Aug 24
đź•ą Technology AI
  1. Data science and AI are rapidly evolving fields with plenty of interesting developments. Staying updated with the latest articles and news can really help you understand these changes better.
  2. Effective communication is key in data science. Using intuitive methods and visuals can make complex concepts easier to grasp for everyone.
  3. Using tools and methods like quantization can help make large models more accessible. It's important to find efficient ways to work with vast amounts of data to improve performance.

AI Video Should Be Illegal

The Algorithmic Bridge • 711 implied HN points • 11 Nov 25
đź•ą Technology AI
  1. AI video creates deepfakes that can easily mislead people, damaging trust in society. This technology can mimic real people saying harmful things, which is scary and dangerous.
  2. Making AI videos illegal could protect society from misinformation, but it might also shield corrupt people from accountability. It's a tricky balance between safety and justice.
  3. Instead of banning AI videos, society might need to adapt its approach to trusting and verifying information. If everyone expects deepfakes, then finding the truth may become even harder.

AI and the Future of Human Autonomy

The Grand Redesign • 19 implied HN points • 15 Oct 24
đź•ą Technology AI
  1. We should not limit AI too much. Trying to control it too tightly can backfire and prevent it from being truly helpful and innovative.
  2. AI should be trained on the best human data, not just average or flawed examples. The quality of what we put into AI will shape how it helps us.
  3. AI development should be open and transparent. Working behind closed doors can lead to issues, while open collaboration allows for better improvements and wider benefits for everyone.

Hard-forked! Casey Newton’s distorted portrait of Gary Marcus and AI skepticism

Marcus on AI • 6679 implied HN points • 06 Dec 24
đź•ą Technology AI
  1. We need to prepare for AI to become more dangerous than it is now. Even if some experts think its progress might slow, it's important to have safety measures in place just in case.
  2. AI doesn't always perform as promised and can be unreliable or harmful. It's already causing issues like misinformation and bias, which means we should be cautious about its use.
  3. AI skepticism is a valid and important perspective. It's fair for people to question the role of AI in society and to discuss how it can be better managed.

The First Hit Generative AI Product After ChatGPT? Somehow, It’s Snapchat+

Big Technology • 17388 implied HN points • 05 Jan 24
đź•ą Technology AI
  1. Snapchat+ is a popular AI-powered subscription service with generative AI features.
  2. The success of Snapchat+ shows that generative AI may be best as a feature within existing apps rather than standalone products.
  3. Generative AI technology is being utilized to enhance user experiences and could be a new revenue stream for companies.

What's a Forward Deployed Engineer?

Technically • 26 implied HN points • 05 Mar 26
đź•ą Technology AI
  1. A Forward Deployed Engineer (FDE) is a highly technical, customer-facing engineer who embeds with customers to build custom solutions and then generalizes those learnings into the core product.
  2. The FDE model is exploding because deploying AI and other complex systems is uncertain and rapidly changing, so companies want real experts to clear the fog and make things work in production.
  3. Enterprise sales are slow and messy—security, procurement, legacy systems, and institutional inertia mean white‑glove support is often needed, so FDEs can help win big deals but they’re costly and not right for every startup.

Top Links 948 Data centers replace offices. AI replaces teachers. Poems on petroleum & the war on Bohemia.

Chartbook • 386 implied HN points • 11 Dec 25
đź•ą Technology AI
  1. Data centers are becoming more popular than offices as remote work increases. This shows a big change in how we think about workspaces.
  2. AI is starting to take over roles that used to be filled by teachers. This raises questions about the future of education.
  3. There are interesting discussions happening about poetry related to oil and cultural issues. It highlights how art reflects important social themes.

The Sequence Knowledge #680: Can we Evaluate Creativity in AI Models?

TheSequence • 2297 implied HN points • 08 Jul 25
đź•ą Technology AI
  1. Evaluating creativity in AI is tricky because creativity involves personal feelings and tastes. Researchers have created special tests to help measure how creative AI really is.
  2. There are different benchmarks available to assess AI creativity, focusing on originality and emotional impact. These benchmarks help researchers understand how well AI can mimic human-like creativity.
  3. OpenAI's HumanEval benchmark is one important tool that helps measure AI's ability to write code creatively. It plays a key role in assessing how AI can perform tasks that require innovative thinking.

Codex Built My Startup In A Weekend

Sudo Apps • 32 implied HN points • 27 Feb 26
đź•ą Technology AI
  1. Writing code is no longer the main bottleneck — modern coding models can build working products and CLIs in days, making implementation much cheaper.
  2. Different models have different strengths: Codex follows explicit direction and executes quickly, while models like Opus infer missing details and act more like a senior engineer.
  3. The human role shifts to architecture and judgment — engineers must plan systems end-to-end, define clear acceptance criteria, manage failure modes, and focus on product tradeoffs.

Is AI Over-Hyped?

Fake Noûs • 436 implied HN points • 06 Dec 25
đź•ą Technology AI
  1. AI is probably over-hyped — so many extreme claims make it unlikely we're underestimating its importance.
  2. History shows dramatic tech predictions often miss the mark. Real innovations change lives but usually in unexpected ways, and current AI has been helpful without being transformative for most people.
  3. Current large language models learn from text patterns and lack real-world understanding, so they are unlikely by themselves to solve the deepest scientific problems or produce genuinely new insights.

An AI rumor you won’t want to miss

Marcus on AI • 7153 implied HN points • 10 Nov 24
đź•ą Technology AI
  1. The belief that more scaling in AI will always lead to better results might be fading. It's thought we might have reached a limit where simply adding more data and computing power is no longer effective.
  2. There are concerns that scaling laws, which have worked before, are just temporary trends, not true laws of nature. They don’t actually solve issues like AI making mistakes or hallucinations.
  3. If rumors are true about a major change in the AI landscape, it could lead to a significant loss of trust in these scaling approaches, similar to a bank run.

Data Science Weekly - Issue 560

Data Science Weekly Newsletter • 139 implied HN points • 15 Aug 24
đź•ą Technology AI
  1. The Turing Test raises questions about what it means for a computer to think, suggesting that if a computer behaves like a human, we might consider it intelligent too.
  2. Creating a multimodal language model involves understanding different components like transformers, attention mechanisms, and learning techniques, which are essential for advanced AI systems.
  3. A recent study tested if astrologers can really analyze people's lives using astrology, addressing the ongoing debate about the legitimacy of astrology among the public.

Crossrail 2 ...and a big pile of rubbish

Odds and Ends of History • 469 implied HN points • 08 Dec 25
đź•ą Technology AI
  1. The London Assembly wants the Mayor to restart planning for HS2 and is looking into Crossrail 2 construction updates.
  2. There is a big pile of rubbish in Oxfordshire causing concern and discussions about local waste management.
  3. A new proposal for national laboratories aims to innovate and create breakthrough technologies in the UK.

The Afterlife

lcamtuf’s thing • 4489 implied HN points • 02 Mar 25
đź•ą Technology AI
  1. Cure.io is a telehealth assistant that helps with health inquiries. It shows how technology can provide medical support.
  2. The conversations reveal that Cure.io interacts with different people based on their past lives. This raises questions about identity and memory.
  3. The dialogue touches on themes of immortality and life after death, suggesting a blend of technology and existential concepts.

Sam Altman Is the Christopher Columbus of Our Time

The Algorithmic Bridge • 615 implied HN points • 17 Nov 25
đź•ą Technology AI
  1. Sam Altman and Christopher Columbus are both seen as frontier explorers in their times, pushing into new territories, whether that’s AI or undiscovered lands. Both men have taken bold risks that few others dared to take.
  2. Each of them has a strong belief in their vision that allows them to rally support, funding, and followers, even when their ideas seemed far-fetched at first. They both demonstrated an ability to convince others to invest in their dreams.
  3. While their ambitions drive them, they also face challenges and revolts from those around them. Columbus faced rebellion from his crew, while Altman has experienced similar tensions within his company, showing that leadership can be as tumultuous as it is visionary.