The hottest AI Substack posts right now

And their main takeaways
Category
Top Technology Topics

AI #45: To Be Determined

Don't Worry About the Vase • 896 implied HN points • 04 Jan 24
🕹 Technology AI
  1. Many people are becoming increasingly concerned about the potential risks of advanced AI technologies, as the complexity of the alignment problem becomes more apparent.
  2. Some politicians, like Senator Cory Booker, are expressing worries about the societal impacts of AI technology and its current prevalence in daily life.
  3. Even with concerns, there are still lighthearted and creative discussions about the future of AI, including speculative scenarios involving children and AI-powered career choices.

AI Web scraping use-cases for Data Teams: Intelligence gathering

The Orchestra Data Leadership Newsletter • 39 implied HN points • 21 May 24
🕹 Technology AI
  1. Web scraping with AI can enhance intelligence gathering by efficiently collecting and processing data from various public sources on the internet.
  2. Leveraging Large Language Models (LLMs) can improve the accuracy and robustness of web scraping systems when dealing with changes in HTML code structure.
  3. Using tools like Nimble for web scraping allows for more efficient and accurate data collection by training models on different types of websites for specific use cases.

Monopoly Profits, AI Arms Races, & the Mirage of Silicon Valley Platform-Oligopoly Disruption

Brad DeLong's Grasping Reality • 130 implied HN points • 14 Jun 25
🕹 Technology AI
  1. Users might benefit greatly from advanced AI technology if tools like chatbots serve them well. It's all about whether these tools work for us or the other way around.
  2. Tech giants like Google and Microsoft are facing challenges as AI rapidly evolves. Their old methods of operation may not keep up with new AI advancements.
  3. Even though AI is changing the tech landscape, it might not lead to the rise of new dominant companies. The existing major players could still remain strong despite the disruption.

OpenClaw & The Lobster Trap: Why the First Real AI Assistant is Also the Most Dangerous

Divinations • 8 implied HN points • 27 Jan 26
🕹 Technology AI
  1. A new class of AI agents can act autonomously on your machine, managing email, calendars, and multi-step workflows by keeping persistent personal memory and exercising deep system access.
  2. That deep local access creates serious security and identity risks: the agent can act as you, enable data exfiltration or ransomware, and become an uncontrolled enterprise risk if deployed widely.
  3. The project’s open-source virality shows huge demand for personal AI agency and will push larger companies to build safer, polished versions, but the current system is a powerful prototype, not a consumer-ready product.

On the FLI AI-Risk Open Letter

Don't Worry About the Vase • 1523 implied HN points • 30 Mar 23
🕹 Technology AI
  1. The FLI AI-Risk Open Letter calls for a pause in training AI systems more powerful than GPT-4, sparking mixed reactions.
  2. The letter combines short-term concerns with existential risks, suggesting high standards that might act as a de facto ban on AI development.
  3. Despite its flaws, the letter sets the stage for cooperation and lays the groundwork for future discussions around AI safety and regulation.
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How to personalize learning with AI 👩‍🎓

Friends of Parsnip • 196 implied HN points • 26 Oct 23
🚌 Education AI
  1. Mass education is often one-size-fits-all, expensive, and frustrating for both teachers and learners.
  2. Personalized education, like mastery learning and 1-to-1 tutoring, can be more effective in improving learning outcomes.
  3. Technology, such as AI-powered skill trees, has the potential to revolutionize learning by providing personalized, interactive, and scalable education.

First Floor #192 – Creeping Corporate Influence

First Floor • 196 implied HN points • 26 Oct 23
🎶 Music AI
  1. NTS Radio accepted a major investment from Universal Music Group, raising questions about corporate influence in the music industry.
  2. The newsletter addresses a plea for help from a freelancer facing payment issues.
  3. The issue covers a variety of electronic music news, release announcements, and track recommendations from different artists.

January 2024

Product Composition • 117 implied HN points • 21 Jan 24
🕹 Technology AI
  1. Alex Vilinskyy is looking for specific roles to fill in his ventures like Payment Integrationist, Email Expert, and Head of Design.
  2. He's also seeking partners interested in addressing issues in the Future of Work, Communication, Computing, Entrepreneurship, and Media.
  3. The post shares info about the authenticity of Instagram, upcoming plans for new products, and recommends cool apps like PhotoStudio and Factorio.

The return of the press release

Alex's Personal Blog • 98 implied HN points • 30 Jul 25
💼 Business AI
  1. Press releases are becoming more important again because companies want to share news, and AI tools are hungry for information. This makes companies release more press releases to get noticed in AI searches.
  2. With the rise of AI, press releases may start to be longer and more focused on providing a lot of information instead of being catchy for humans. This could change the way companies communicate important updates.
  3. As press releases grow more valuable, companies may make bolder claims without the usual human skepticism. This means PR work will become even busier and might lean more on AI tools.

💥💥 Two takes on America’s emerging (maybe) productivity boom

Faster, Please! • 822 implied HN points • 08 Feb 24
💰 Finance AI
  1. There are signs of a significant economic transformation with productivity growth outpacing historical averages.
  2. The American economy may be experiencing a new productivity boom, setting it apart from other advanced economies.
  3. The recent productivity upshift is likely a response to a tight hiring environment, prompting firms to boost efficiency and automation.

Singh & Sins of AI 💦

Sector 6 | The Newsletter of AIM • 99 implied HN points • 13 Feb 24
🕹 Technology AI
  1. The Indian AI scene is growing, with many new language models being developed based on Meta's Llama 2. This shows a collaborative spirit in the open-source community.
  2. There are specific models being made for different Indian languages like Kannada, Telugu, Odia, and Tamil. These models help in making AI more accessible to people speaking these languages.
  3. There is a strong need for India to create its own unique open-source AI model. This would allow other developers to build on it rather than relying on external sources.

Most Human Wins

Not Boring by Packy McCormick • 270 implied HN points • 22 Jan 25
💼 Business AI
  1. As technology advances, many skills we thought were unique to humans are becoming easier for machines to do. However, this doesn't mean that humans are being made irrelevant; rather, we need to find what makes us unique.
  2. The process of commoditization means that things that were once rare and valuable are now easier and cheaper to access. This opens up new opportunities for what skills can be considered valuable in a changing economy.
  3. It’s important to adapt and use the tools at our disposal creatively. As machines take over more tasks, we should focus on our human strengths and experiences, making them central to our endeavors.

AI Companies Have Lost the Mandate of Heaven

The Algorithmic Bridge • 339 implied HN points • 04 Dec 24
🕹 Technology AI
  1. AI companies are realizing that simply making models bigger isn't enough to improve performance. They need to innovate and find better algorithms rather than rely on just scaling up.
  2. Techniques to make AI models smaller, like quantization, are proving to have their own problems. These smaller models can lose accuracy, making them less reliable.
  3. Researchers have discovered limits to both increasing and decreasing the size of AI models. They now need to find new methods that work better while balancing cost and performance.

We don’t need another SQL chatbot

benn.substack • 1227 implied HN points • 14 Jul 23
🕹 Technology AI
  1. We want chatbots to handle tedious job tasks but maybe not the fun parts.
  2. Building a good text-to-SQL bot requires more than just using large language models like GPT.
  3. Technology can help us focus on creative tasks rather than just automating mechanical work.

Last Week in AI #248: ChatGPT's 1st birthday, Amazon's new AI chatbot Q, the energy cost of generative AI, Sam Altman makes statement after return, and more!

Last Week in AI • 178 implied HN points • 04 Dec 23
🕹 Technology AI
  1. ChatGPT has made a significant impact in the past year with its interactive and conversational dialogue capabilities
  2. Amazon's new AI chatbot Q for companies has faced reliability issues, including hallucinations and data exposure during its public preview
  3. Generative AI, like image generation, consumes significant energy, equivalent to charging a smartphone, prompting a need to consider the environmental impact of AI technologies

State of AI: February 2026 newsletter

Guide to AI • 4 implied HN points • 09 Feb 26
🕹 Technology AI
  1. Agentic AI is triggering a massive market repricing as autonomous agents and rapidly advancing frontier models threaten the long-term recurring revenue that justified high SaaS valuations, wiping hundreds of billions from software stocks. Investors are racing to re-evaluate how to underwrite tech companies in a world where core workflows can be rebuilt AI-first.
  2. Geopolitics and infrastructure constraints are reshaping the AI landscape: governments are clashing with labs over military use and export controls, states are limiting data center builds, and China is aggressively scaling talent and commercial AI, all of which will affect where training clusters and supply chains can be built. These policy and resource shifts will influence competition, investment, and national strategy in AI.
  3. Rapid agent proliferation has produced both theatrical emergent behavior and serious security problems: viral agent networks blurred the line between human and AI activity, while open-source agents exposed widespread vulnerabilities, leaked credentials, and growing shadow-IT risks for enterprises. The combination of autonomy, data access, and external actions makes agent security a top priority.

BI is not ready for AI

HyperArc • 3 HN points • 06 Sep 24
🕹 Technology AI
  1. Business Intelligence (BI) needs both good models and great data to be effective with AI. Without quality data, AI can't really show its true power.
  2. Many BI tools only focus on successful outcomes, like specific metrics, while ignoring the complete journey of discovery. This limited data can lead to missing important insights.
  3. To improve AI's effectiveness in BI, we should include a wider range of experiences and exploration paths, not just successful queries. This fuller picture can help create better AI training sets.

Automating creativity

One Useful Thing • 1174 implied HN points • 13 Aug 23
🕹 Technology AI
  1. AI can generate creative ideas in real-life scenarios and can help people come up with better ideas.
  2. Highly creative individuals can still outperform AI in idea generation.
  3. AI excels at combining existing concepts to create new and innovative ideas, making it a valuable tool for generating creativity.

AI-generated slop is already clogging the arteries of the web

Simon Owens's Media Newsletter • 823 implied HN points • 19 Jan 24
🕹 Technology AI
  1. Many worry about AI-generated content replicating and stealing audiences, but the impact on publishers is still largely hypothetical.
  2. AI is already degrading the user experience of the web, causing harm and making content resources useless.
  3. Platforms like Amazon, Google News, and ad tech are flooded with AI-generated content, harming users and eroding trust in the information served.

Results from poll #3 (and updates)

The Counterfactual • 59 implied HN points • 11 Apr 24
🕹 Technology AI
  1. Tokenization won the recent poll, so there will be an in-depth explainer about it soon. This will help people understand how tokenization works in large language models.
  2. The visual reasoning task was a close second, so it might come up in the next poll for more ideas. This shows there is interest in how models think visually.
  3. There are updates about recent publications and discussions on related topics in AI and psychology. These will be shared in upcoming posts, expanding on interesting research topics.

LLMs and World Models, Part 1

AI: A Guide for Thinking Humans • 247 implied HN points • 13 Feb 25
🕹 Technology AI
  1. In the past, AI systems often used shortcuts to solve problems rather than truly understanding concepts. This led to unreliable performance in different situations.
  2. Today’s large language models are debated to either have learned complex world models or just rely on memorizing and retrieving data from their training. There’s no clear agreement on how they think.
  3. A 'world model' helps systems understand and predict real-world behaviors. Different types of models exist, with some capable of capturing causal relationships, but it's unclear how well AI systems can do this.

Links for 2024-01-18

Axis of Ordinary • 117 implied HN points • 18 Jan 24
🔬 Science AI
  1. AI system AlphaGeometry solves Olympiad geometry problems like a gold-medalist.
  2. AlphaGeometry consists of a neural language model and a symbolic deduction engine.
  3. OpenAI is developing a new model, GPT-5, to advance scientific discovery.

LLM Disruption in Chatbot Development Frameworks

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 05 Jul 24
🕹 Technology AI
  1. Large Language Models (LLMs) make chatbots act more like humans, making it easier for developers to create smart bots.
  2. Using LLMs reduces the need for complex programming rules, allowing for quicker chatbot setup for different uses.
  3. Despite the benefits, there are still challenges, like keeping chatbots stable and predictable as they become more advanced.

Tencent CEO Pony Ma's speech at the firm's annual staff meeting

Ginger River Review • 98 implied HN points • 12 Feb 24
💼 Business AI
  1. Tencent CEO discussed the future of key business segments like WeChat, AI, Platform and Content Group, Cloud and Smart Industries Group, and games.
  2. Internal management and leadership were emphasized, advocating for a return to product experience and detailing strategic approaches for growth.
  3. Focus was placed on global expansion in the gaming industry, the importance of employee morale, signifying revolutionary changes in Tencent's business approach.

Missionaries vs. Mercenaries

Teaching computers how to talk • 110 implied HN points • 10 Jul 25
🕹 Technology AI
  1. OpenAI has a huge ambition to grow like Meta, needing to hit a target of $125 billion in revenue by 2029. This is a really tough goal and they have to compete aggressively.
  2. Sam Altman believes that teams driven by passion and purpose (missionaries) will outperform those just focused on making money (mercenaries). He wants to create an inspiring work culture at OpenAI.
  3. OpenAI has taken on a lot of investment, which means they need to deliver high returns quickly. This pushes them to make bold moves in the AI market.

Some interesting new robots just announced!

Robots & Startups • 79 implied HN points • 09 Mar 24
🕹 Technology AI
  1. AI learning starting with text may be going backwards for language development, particularly for speech and social interaction.
  2. Human-robot interactions often differ from our collective fantasies, with instances of people mistreating robots in public like playing 'kick the robot dog' or interfering with autonomous cars.
  3. Robots posing as scooters in public encounters negative behaviors due to lack of proper treatment and consideration towards the technology.

AI-generated Anki cards are not to be trusted

De Novo • 121 implied HN points • 13 Jun 25
🚌 Education AI
  1. AI-generated Anki cards can have mistakes that may lead to learning incorrect information. It's important to double-check AI content, especially on complex topics.
  2. Relying on AI for learning new material may not be wise, as it could lead to errors that are not obvious at first glance.
  3. Even when asking different AI systems to review the same content, they can miss errors or indicate correct information as wrong. Human oversight is crucial.

Accepting it's over might be your best survival plan

Make Work Better • 136 implied HN points • 28 May 25
💼 Business AI
  1. AI is rapidly evolving and may soon be able to replace many jobs. This change is happening faster than we can adapt, making it important to stay aware of new developments.
  2. Many companies are not yet embracing AI, leading to a divide between those who are prepared for changes and those who are not. This could create job insecurity for many workers in the future.
  3. It’s crucial for individuals and organizations to invest in learning new skills related to AI. Accepting the reality of potential job loss can help us prepare for what's ahead.

An AI wrote this

antoniomelonio • 99 implied HN points • 23 Jul 25
🕹 Technology AI
  1. It's hard to tell if something was written by a human or an AI. With AI getting better, you might not even know if you're reading real thoughts or generated text.
  2. Many writers on platforms like Substack might not be creating original content anymore. This raises questions about authenticity and what it means to share thoughts.
  3. AI is improving quickly and it's changing the world we know. We need to accept that these changes are real and think about how we'll adapt.

My non-prediction for generative AI in 2024

ailogblog • 119 implied HN points • 12 Jan 24
🕹 Technology AI
  1. The energy consumption of generative AI for tasks like image generation and question answering can be significant.
  2. The use of generative AI may impact freelance job opportunities for illustrators and writers.
  3. There is uncertainty about the future of generative AI, with questions about its social costs, technological advancements, and ethical considerations.

Navigating the Future of AI in the Creative Industries

Gradient Flow • 79 implied HN points • 07 Mar 24
🕹 Technology AI
  1. AI models like Sora have the potential to revolutionize video production by generating high-quality videos from text prompts.
  2. The automation wave in AI video generation is leading to rapid progress and competition among tech giants, but challenges remain in maintaining coherence and ethical considerations.
  3. The future of video production will require a balance of AI and human creativity, emphasizing the need for AI literacy, ethical content creation, and the preservation of uniquely human skills like creativity and strategic thinking.

OpenAI’s biggest worry isn’t DeepSeek

Enterprise AI Trends • 253 implied HN points • 31 Jan 25
🕹 Technology AI
  1. DeepSeek's release showed that simple reinforcement learning can create smart models. This means you don't always need complicated methods to achieve good results.
  2. Using more computing power can lead to better outcomes when it comes to AI results. DeepSeek's approach hints at cost-saving methods for training large models.
  3. OpenAI is still a major player in the AI field, even though some people think DeepSeek and others will take over. OpenAI's early work has helped it stay ahead despite new competition.

If AI Is So Great, Someone Should Tell GDP

The Algorithmic Bridge • 222 implied HN points • 05 Mar 25
🕹 Technology AI
  1. AI investments have been rising, but there's not much difference in overall economic growth or productivity. This makes us question if spending so much on AI is really worthwhile.
  2. Companies are unsure whether it's better to invest heavily in new AI technology or to optimize what they already have. It’s a tricky balance to strike.
  3. Despite the hype around AI, it hasn't significantly improved things like GDP or human well-being. It's clear that AI is still looking for its true role in boosting our economy.

The Sequence Knowledge #697: The Most Important Theory in Modern AI Interpretability

TheSequence • 91 implied HN points • 05 Aug 25
🕹 Technology AI
  1. Superposition is an important idea in AI that helps us understand how models can represent many concepts at once. This idea means that a single piece of data can hold multiple meanings, which is useful when analyzing complex information.
  2. There is a relevant paper that discusses superposition in cutting-edge AI models. Studying this paper can provide deeper insights into how modern AI understands and processes data.
  3. The concept of polysemanticity is linked to superposition and emphasizes the ability of AI models to interpret language and information in multiple ways. This flexibility is key to improving AI interpretation and performance.

Short Takes #1

Am I Stronger Yet? • 125 implied HN points • 16 Jun 25
🕹 Technology AI
  1. AI is changing cybersecurity, but it’s hard to predict how it will affect us. Experts are discussing the right questions to understand its impact.
  2. Meta AI is possibly having a bigger influence than we think, especially in emerging economies. Many people are using it regularly in their daily apps.
  3. AI models are evolving, and their new skills might bring both benefits and risks. There’s a growing concern that they could share harmful information as they get smarter.

Unleashing LLMs in Cybersecurity: A Playbook for All Industries

Gradient Flow • 259 implied HN points • 20 Apr 23
🕹 Technology AI
  1. Large Language Models (LLMs) are gaining interest in various industries, especially in cybersecurity, and can be used as a playbook for implementation in other domains.
  2. Custom LLMs can be created for cybersecurity applications, leading to potential advancements like specialized chatbots and content generation for enhanced security measures.
  3. LLMs are transforming automation processes in cybersecurity, offering improved accuracy and convenience, and displaying potential for impact across multiple industries through domain-specific adaptations.

OWASP LLM AI Cybersecurity & Governance Checklist

Resilient Cyber • 79 implied HN points • 06 Mar 24
🕹 Technology AI
  1. Organizations need to understand the unique risks of using Large Language Models (LLMs) and Generative AI, and they should create clear strategies for managing these risks.
  2. Having an AI asset inventory is crucial so that companies know what AI tools they are using and who is responsible for them.
  3. Safety training for employees on AI tools can help prevent misuse and create a culture of transparency within the organization.