The hottest AI Substack posts right now

And their main takeaways
Category
Top Technology Topics

AGI won't use computations

John Ball inside AI • 79 implied HN points • 23 Jun 24
🕹 Technology AI
  1. Artificial General Intelligence (AGI) might be achieved by focusing on pattern matching rather than traditional computations. This means understanding and recognizing complex patterns, just like how our brains work.
  2. Current AI systems struggle with tasks like driving or conversing naturally because they don't operate like human brains. Instead of tightly-coupled algorithms, more flexible and efficient pattern-based systems might be the key.
  3. Patom theory suggests that brains store and match patterns in a unique way, which allows for better learning and error correction. By applying these ideas, we could improve AI systems to be more human-like in understanding and interaction.

Security is Not an AI Problem

Security Is • 159 implied HN points • 02 May 24
🕹 Technology AI
  1. AI doesn't really fix security problems well. Many times, the technology just doesn't work in the tough, unpredictable environments that security deals with.
  2. The best results in security often come from simple, clear procedures, not from complex machine learning models. Basic rules can solve most problems effectively.
  3. Generative AI can help with minor tasks but isn't a magic solution for security. It might even confuse people about important issues, rather than clarify them.

OpenAI Is in Trouble (For Real This Time)

The Algorithmic Bridge • 392 implied HN points • 01 Jul 25
🕹 Technology AI
  1. OpenAI is facing tough competition from Meta and Microsoft, which is creating tension and challenges for the company. It looks like these big companies are making moves to poach OpenAI's best talent.
  2. Historically, OpenAI has gone through multiple crises but has managed to bounce back each time. This current situation seems serious, but it's part of a pattern of troubles the company has faced before.
  3. There are concerns about OpenAI's leadership under Sam Altman. Some employees worry that a lack of open communication and differing opinions could hurt the company's ability to innovate.

💥 Tech Talks Weekly #25

Tech Talks Weekly • 59 implied HN points • 01 Aug 24
🕹 Technology AI
  1. Tech Talks Weekly shares fresh talks from over 100 conferences every week. It's a great way to stay updated without sifting through a lot of content.
  2. This edition highlights talks from major events like ReactConf and JCON Europe. The featured talks include exciting topics like new features in React and domain-driven design.
  3. Readers are encouraged to fill out a form to help improve content and to spread the word about the newsletter. It's all about building a community around tech discussions!
Get a weekly roundup of the best Substack posts, by hacker news affinity:

We're Finding Out What Humans are Bad At

Am I Stronger Yet? • 799 implied HN points • 18 Feb 25
🕹 Technology AI
  1. Humans are not great at some tasks, especially ones like multiplication or certain physical jobs where machines excel. Evolution didn't prepare us for everything, so machines often outperform us in those areas.
  2. In tasks like chess, humans can still compete because strategy and judgment play a big role, even though computers are getting better. The game requires thinking skills that humans are good at, though computers can calculate much faster.
  3. AI is advancing quickly and becoming better at tasks we once thought were uniquely human, but there are still challenges. Some complex problems might always be easier for humans due to our unique brain abilities.

Global Risks Report 2024

AI Supremacy • 334 implied HN points • 07 Feb 24
🌍 World Politics AI
  1. The Global Risks Report 2024 provides insights from various leaders and explores severe risks to economies over the next two and ten years due to governance systems being stretched.
  2. Some top global risks for 2024 include China's debt crisis, conflicts in the Middle East, potential banking crises in the U.S., and mismanagement of AI in military applications.
  3. The World Economic Forum highlights environmental threats, societal polarization, cyber insecurity, and economic challenges as key risks from 2024 to 2034.

AI Certifications to Pursue in 2024

Rod’s Blog • 436 implied HN points • 29 Dec 23
🚌 Education AI
  1. AI certifications can boost career prospects and earning potential in a fast-growing field like Artificial Intelligence.
  2. When choosing an AI certification, consider whether you want a formal certification from a professional body or an educational institution, the specific topics and domains that interest you, and the features and benefits of the program.
  3. Some recommended AI certification programs for 2024 include Microsoft Certified: Azure AI Engineer Associate, Certified Artificial Intelligence Scientist by ARTIBA, and Jetson AI Courses and Certifications by NVIDIA.

Analyze Brain Regions, not Neurons

John Ball inside AI • 59 implied HN points • 08 Jul 24
🔬 Science AI
  1. It's better to study brain regions rather than just neurons because brain regions are responsible for specific functions, and damage to these regions leads to predictable problems.
  2. AI development has focused too much on the workings of individual neurons instead of understanding how brain regions connect and work together as a system.
  3. Understanding meaning is crucial for AI to function like human brains, as language and thought come from the brain's ability to store and connect experiences.

Bringing Security Out of the Shadows

Resilient Cyber • 99 implied HN points • 06 Jun 24
🕹 Technology AI
  1. Shadow usage happens when employees use technology without telling the IT or security teams. This is easy to do, especially with things like personal devices and remote work.
  2. Cybersecurity teams often react to problems instead of staying ahead of technology trends. Instead of waiting for issues to arise, they should explore and adapt new technologies early.
  3. Long-lasting issues between security teams and other departments lead to frustration. If security teams work better with others, they can create a smoother, more productive environment.

AI is breadth-first, not depth-first

The AI Frontier • 99 implied HN points • 06 Jun 24
🕹 Technology AI
  1. AI works well across many tasks but struggles with the details. It can help with brainstorming or basic coding but doesn't replace expert-level understanding.
  2. When building AI products, think beyond one industry or function. There are opportunities where different jobs connect and can benefit from shared data.
  3. It's important to understand what experts want from your AI. They expect quality insights, so your AI should be ready to provide that next level of detail.

We are all doing AI wrong

Experiments with NLP and GPT-3 • 23 implied HN points • 30 Jan 26
🕹 Technology AI
  1. People are tired of AI being shoved into every product; users just want things that work reliably.
  2. Companies aren't using their own AI to fix basic bugs and bad interfaces, which suggests the tech either isn't ready for heavy lifting or it's being used more as marketing than as a solution.
  3. Stop adding gimmicky AI features and focus on fixing small, annoying problems so tools become reliable, private, and actually helpful.

Looking for a better story

imperfect offerings • 239 implied HN points • 18 Mar 24
🕹 Technology AI
  1. The future of AI may not necessarily be as promising as it has been hyped, with concerns about inflated expectations and potential limited use cases.
  2. The use of generative AI can have unintended negative consequences, such as detrimental effects on academia, exploitation of data workers, and potential harm to minority languages.
  3. AI's impact on the environment, from excessive water usage to electricity consumption, raises concerns about accelerating climate change and misinformation.

Where is the Moat in AI?

In My Tribe • 258 implied HN points • 11 Aug 25
💼 Business AI
  1. Intangible assets, like brand loyalty and know-how, can create lasting profits that are harder to compete against than physical resources like data centers.
  2. With AI, some jobs, especially mid-level roles in fields like finance and law, may decline in value as companies adopt technology to cut costs.
  3. Successful companies will be those that maintain strong relationships and moats now, as they navigate competition with AI model developers.

Why security has become easier

Frankly Speaking • 355 implied HN points • 02 Jul 25
🕹 Technology AI
  1. Security tools have improved a lot and are easier to use now. Companies can set up basic security measures quickly without needing huge teams.
  2. AI helps security teams by automating tasks and making their work faster. When used correctly, it can save time on repetitive tasks.
  3. There is now better data on security breaches which helps teams prioritize what risks to focus on. This makes good security practices more accessible and easier to implement.

We’re Not Ready for the AI Power Surge

Common Sense with Bari Weiss • 426 implied HN points • 11 Jun 25
🕹 Technology AI
  1. The rapid growth of AI technology is increasing the demand for energy, which may strain the current power grid in America.
  2. New AI models are becoming more powerful, and their popularity is likely to lead to even higher energy consumption as usage increases.
  3. Some experts express concern about the future energy needs for AI, while others believe the impact on electricity usage per query is low.

Are You Ready for 2024?

Sector 6 | The Newsletter of AIM • 439 implied HN points • 26 Dec 23
🕹 Technology AI
  1. AMD is making big strides in AI, partnering with major customers to improve data center capabilities and deploying new technologies like MI300 accelerators.
  2. The market for data center AI accelerators is growing rapidly, with projections increasing from $150 billion to over $400 billion by 2027.
  3. AMD is also enhancing software development tools to better support AI workloads, making it easier for businesses to integrate AI into their operations.

The Saturday Selection, Vol. 60

Why is this interesting? • 361 implied HN points • 28 Jun 25
🕹 Technology AI
  1. New Zealand has a lot of potential for AI growth because of its unique resources and recent changes in rules.
  2. Many people can't tell the difference in audio quality between high-quality files and regular MP3s, which can be surprising.
  3. Using lotteries to offer people cash can encourage them to make better life choices and do positive things.

Notes on Google's Attempt at Self-Disruption

Brad DeLong's Grasping Reality • 292 implied HN points • 30 Jul 25
🕹 Technology AI
  1. Google is changing how it operates by using AI to summarize search results instead of just linking users to websites. This could reduce traffic to publishers who rely on clicks from Google.
  2. While fewer people might click on links due to AI summaries, Google claims that the advertisers are still willing to pay more for the remaining clicks, suggesting a shift in user intent and engagement.
  3. This big move to AI could be risky. If it works out, Google might dominate future online searches, but if it fails, they could end up with a lot of costly infrastructure without much to show for it.

Deep Research, information vs. insight, and the nature of science

Democratizing Automation • 775 implied HN points • 12 Feb 25
🕹 Technology AI
  1. AI will change how scientists work by speeding up research and helping with complex math and coding. This means scientists will need to ask the right questions to get the most out of these tools.
  2. While AI can process a lot of information quickly, it can't create real insights or make new discoveries on its own. It works best when used to make existing scientific progress faster.
  3. The rise of AI in science may change traditional practices and institutions. We need to rethink how research is done, especially how quickly new knowledge is produced compared to how long it takes to review that knowledge.

($) H20 and the Broken Export Control Debate

Interconnected • 293 implied HN points • 29 Jul 25
🕹 Technology AI
  1. The export control debate about Nvidia's H20 chip is complicated because both sides use the same evidence to argue their points. It shows that the argument is not fully addressing the real concerns.
  2. Chinese tech companies are placing large orders for these H20 chips, but they fear getting too reliant on Nvidia's products instead of developing their own. This means they want to ensure they have various options.
  3. Interestingly, many Chinese companies also dislike Huawei, as they don’t want to be stuck with a single supplier. They are looking for better choices in the tech landscape.

AI's meaning-making problem.

The Uncertainty Mindset (soon to become tbd) • 119 implied HN points • 22 May 24
🕹 Technology AI
  1. Humans can make meaning by assigning value to things, which is something AI cannot do. This includes deciding what's good or bad, worth doing, and how different things compare in value.
  2. AI systems depend on humans for meaning-making to produce useful outputs. When using AI, the skill of the user to interpret and edit outputs is essential for effectiveness.
  3. Understanding that meaning-making is a human ability helps in developing better AI systems. It shifts the focus from what AI can do to what humans do that AI cannot.

Reflections on studying Large Language Models

Philosophy bear • 393 implied HN points • 24 Jun 25
🕹 Technology AI
  1. It's important to understand what Large Language Models (LLMs) can currently do and limit excessive philosophical concerns. Focusing on their real capabilities helps us appreciate their strengths and weaknesses better.
  2. Critics often overlook the achievements of LLMs, making broad claims without specific evidence of what these models can't do. A careful look at their limitations and abilities is needed for a fair assessment.
  3. When thinking about LLMs, we should be cautious about using complex concepts like 'thinking' or 'creativity.' It's better to focus on what these models can actually accomplish instead of getting caught up in vague definitions.

Why China might lead the Robot Revolution

Faster, Please! • 731 implied HN points • 04 Mar 25
🕹 Technology AI
  1. China is likely to take the lead in humanoid robots because of its strong manufacturing skills. This makes it easier for them to produce these robots in large numbers.
  2. Humanoid robots could help fill job shortages in various industries like healthcare and logistics. As many people are retiring, robots might take on tasks that are hard to fill.
  3. While the US may not lead in making physical robots, it has a lot of smart technology for AI that powers these robots. The real competition will be between making the robots themselves and the technology that controls them.

Revisiting 2025 Predictions

next big thing • 46 implied HN points • 24 Dec 25
🕹 Technology AI
  1. Small, capital-efficient teams built AI-native products that scaled extremely quickly, creating many new businesses that reached tens of millions in revenue.
  2. AI shifted from being an assistant to a collaborator: code generation and app-building tools lowered the barrier to making software, but fully autonomous end-to-end AI workers still fell short of expectations.
  3. Markets and infrastructure tightened around AI — liquidity returned with major M&A and stronger exits, big tech earnings accelerated, and huge investments flowed into data centers and energy/cooling to support AI demand.

The Year AI Gets a Better Body

The Generalist • 2741 implied HN points • 04 Jan 24
🕹 Technology AI
  1. 2024 is the year consumer AI begins to try out new bodies.
  2. Innovators are experimenting with new AI hardware form factors.
  3. Big companies like Apple and smaller startups are investing in AI hardware innovation.

Clouded Judgement 2.6.26 - Software Is Dead...Again...For Real this Time...Maybe?

Clouded Judgement • 18 implied HN points • 06 Feb 26
💼 Business AI
  1. Public software valuations have collapsed — the median NTM revenue multiple is about 3.6x and roughly 39% of the index trades below 3x, as investors reprice the sector amid much higher uncertainty.
  2. AI agents are poised to capture much of the new incremental value on top of systems of record, effectively pushing legacy cloud software down the stack into lower-growth middleware; a small minority (maybe ~10%) of incumbents may successfully capture the agent-driven S-curve.
  3. The market reaction may be overdone in the short term because many companies still show solid results and enterprise cloud migrations continue, but real operational problems (heavy SBC, long CAC paybacks) plus greater terminal risk justify a lower, more cautious multiple environment.

What Could Possibly Go Wrong with Sam Altman’s New Ambitions?

Marcus on AI • 2489 implied HN points • 09 Feb 24
🕹 Technology AI
  1. Sam Altman's new ambitions involve projects with significant financial and technological implications, such as automating tasks by taking over user devices and seeking trillions of dollars to reshape the business of chips and AI.
  2. There are concerns about the potential consequences and risks of these ambitious projects, including security vulnerabilities, potential misuse of control over user devices, and the massive financial implications.
  3. The field of AI may not be mature enough to handle the challenges presented by these ambitious projects, and there are doubts about the feasibility, safety, and ethical implications of executing these plans.

How does ChatGPT work? A brief history of computational language understanding

prakasha • 648 implied HN points • 23 Feb 23
🕹 Technology AI
  1. A brief history of computational language understanding dates back to collaboration between linguists and computer scientists.
  2. Language models like ChatGPT use word embeddings to predict and generate text, allowing for effective context analysis.
  3. Neural networks, like Transformers, have revolutionized NLP tasks, enabling advancements in machine translation and language understanding.

Does Decentralized Machine Learning Need Crypto? Bittensor (TAO)

DeFi Education • 599 implied HN points • 27 Oct 23
🕹 Technology AI
  1. Bittensor is a platform that uses decentralized machine learning to connect users with miners who run AI models. It aims to create a more open and fair AI ecosystem where everyone can participate.
  2. The platform rewards miners and validators with TAO tokens based on their contributions, similar to how Bitcoin operates. This incentive system encourages the best AI models to be selected for user queries.
  3. There's a growing trend of open source AI projects that show promise without needing huge corporate funding, making it possible for smaller teams to create effective AI tools without significant expenses.

Up Wing/Down Wing #35

Faster, Please! • 731 implied HN points • 01 Mar 25
🕹 Technology AI
  1. OpenAI has released a new AI model called GPT-4.5 that is better at understanding prompts and generating content. This improvement makes AI more reliable for writing and coding tasks.
  2. Amazon has launched its first quantum computing chip named Ocelot, which could tackle complex problems much faster than regular computers. This is a big step in the competition for advanced technology.
  3. AI is now helping organizations to better target aid for people in need by analyzing various data sources. This technology can make sure help reaches the right communities, improving ways to fight poverty.

Navigating Attention-Driving Algorithms, Capturing the Premium of Proximity for Virtual Teams, & New AI Devices

Implications, by Scott Belsky • 530 implied HN points • 18 Nov 23
🕹 Technology AI
  1. AI-powered algorithms are driving polarization by optimizing for attention-grabbing content, widening the surface area of topics that stoke anger.
  2. Our social media feeds are now sourced from algorithmic preferences rather than social networks, shaping the content we are exposed to.
  3. The benefits of physical proximity in fostering creativity and relationships for teams will lead to the emergence of new technologies and management strategies supporting hybrid and remote work environments.

"Great features, not products," leveraging found productivity, and the era where we can no longer believe our eyes.

Implications, by Scott Belsky • 648 implied HN points • 06 Mar 23
🕹 Technology AI
  1. Focus on creating great features, not just products, in the startup ecosystem.
  2. Consider how excess productivity from AI can be used in entertainment, philosophy, and creative projects.
  3. The rise of AI will shift jobs to industries less impacted by AI, emphasizing qualitative skills and the importance of imagination and creativity.

DuoLingo's monetization machine and Google banishes blue links

Department of Product • 648 implied HN points • 18 May 23
🕹 Technology AI
  1. Google is changing its search results page to prioritize AI-generated information over blue links, presenting a strategic dilemma for tech companies.
  2. DuoLingo saw impressive revenue growth thanks to their new monetization machine, DuoLingo Max, leveraging proprietary machine learning models and GPT-4.
  3. A new startup, Async, aims to streamline asynchronous voice communication for corporate environments, offering a potential solution for message management overload.

AI in DeFi

DeFi Education • 499 implied HN points • 29 Nov 23
🔮 Crypto AI
  1. Large Language Models (LLMs) are making it easier for people without coding skills to interact with the DeFi space. Now, you can ask questions and get quick responses without needing to be a tech expert.
  2. AI can help enhance the security of DeFi by automating smart contract audits and identifying vulnerabilities. This means it can make DeFi safer, but there’s also a risk that hackers might use AI for malicious purposes.
  3. LLMs can streamline tasks like monitoring Discord communities by filtering out spam and detecting issues. This could make managing online crypto communities much more efficient.

⤴⤵ Up Wing/Down Wing #28

Faster, Please! • 1005 implied HN points • 14 Dec 24
🕹 Technology AI
  1. New chips using fiber optics can transfer data way faster, which may cut down AI training times and save energy. This could really speed up tech advancements.
  2. Businesses are finding out that human skills are still important when using AI tools. People are getting new jobs related to organizing data so AIs can work better.
  3. SpaceX is becoming super important for US defense technology. Its innovations may give the US an advantage over rivals like China in military capabilities.