The hottest Innovation Substack posts right now

And their main takeaways
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Top Technology Topics

On GPT-4.5

Don't Worry About the Vase • 1344 implied HN points • 03 Mar 25
🕹 Technology AI Computing Innovation Gadgets Software
  1. GPT-4.5 is a new type of AI with unique advantages in understanding context and creativity. It's different from earlier models and may be better for certain tasks, like writing.
  2. The model is expensive to run and might not always be the best choice for coding or reasoning tasks. Users need to determine the best model for their needs.
  3. Evaluating GPT-4.5's effectiveness is tricky since traditional benchmarks don't capture its strengths. It's recommended to engage with the model directly to see its unique capabilities.

If You're So Smart, Why Can't You Die?

Desystemize • 3933 implied HN points • 16 Feb 25
🕹 Technology AI Intelligence Philosophy Computing Innovation
  1. AI improvements are not even across the board. While some tasks have become incredibly advanced, other simple tasks still trip them up, showing that not all intelligence is equal.
  2. We should be cautious about assuming that increases in one type of AI ability mean it can do everything we can. Each skill in AI may develop separately, like bagels and croissants in baking.
  3. Understanding what makes intelligence requires looking deeper than just performance. There is a difference between raw capabilities and the contextual, real-life experiences that truly shape how we understand intelligence.

Government Science and the Prestige Economy

Richard Hanania's Newsletter • 1219 implied HN points • 14 Jun 25
🔬 Science Research Policy Innovation Economics History
  1. Government funding for science is important because there are some types of research that private companies won't invest in, even though they can benefit society. Basic research is valuable, even if it doesn't have immediate economic benefits.
  2. The idea of crowding out suggests that government funding may take talent away from private companies, but research shows that government support often leads to more innovation in the private sector as well.
  3. Prestige economies, which reward knowledge and research, can motivate scientists to do valuable work even if it doesn't yield direct financial gains. This is different from private sector jobs where profit is the main goal.

Innovation Emergency with Trump 1.0's Patent Director

ChinaTalk • 237 implied HN points • 19 Feb 25
🇺🇸 U.S. Politics Intellectual Property Legislation Innovation Technology Policy Trade Relations
  1. China is now granting way more patents than the United States, which may indicate they're leading in innovation. This shift in patent dominance could be a warning sign for the US economy.
  2. There's a tension in patent law between protecting inventors and allowing the public access to innovations. Strong patent rights can encourage investment in risky new technologies, but if they're too strong, they can limit public access.
  3. US companies sometimes prefer to enforce patents in China because their courts can provide quicker and more effective rulings. This shows a potential weakness in the American patent system that could need serious reforms.
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Frequently Asked Questions About US Government Funding for R&D

New Things Under the Sun • 96 implied HN points • 19 Feb 25
🇺🇸 U.S. Politics Government funding Research & Development Economic Growth Public Policy Innovation
  1. The US government spent about $160 billion on research and development (R&D) in 2022, but this is a declining share of overall GDP. In contrast, the private sector spends significantly more on R&D.
  2. Averaging across sectors, every dollar spent on R&D can generate about $5.50 in economic growth, with some estimates suggesting even higher benefits when considering broader impacts.
  3. Government funding is important because it explores research areas that might be overlooked by private companies, ensuring that valuable scientific knowledge is developed for public benefit.

In the Light of Victory, He Himself Shall Disappear

The Intrinsic Perspective • 11333 implied HN points • 05 Jun 25
🕹 Technology AI Employment Innovation Society Economy
  1. AI is changing the job landscape quickly. Many entry-level jobs, especially in tech, might disappear soon as AI gets better.
  2. Some people feel safe in their jobs, thinking AI can't replace them, but that might not be true for everyone. Many workers could end up feeling like outdated lamplighters.
  3. Progress often comes with loss. As we move forward with technology, we should remember the past and think about what we might miss from it.

Why China might lead the Robot Revolution

Faster, Please! • 731 implied HN points • 04 Mar 25
🕹 Technology Robotics AI Manufacturing Innovation Automation
  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.

Microsoft sees OpenAI as an existential threat

Enterprise AI Trends • 337 implied HN points • 23 Feb 25
🕹 Technology AI Software Enterprise Innovation Business
  1. Microsoft feels threatened by OpenAI because OpenAI is becoming powerful in the enterprise AI space. They worry that OpenAI's success could hurt Microsoft's own products.
  2. The 'AGI clause' gives OpenAI a strong advantage. It allows them to keep any advanced models from Microsoft, which could limit Microsoft's ability to compete effectively.
  3. Microsoft is trying to slow down AI adoption to regain control. They believe that if companies are hesitant to adopt AI quickly, it gives them time to improve their own offerings.

How the AI security space will evolve

Frankly Speaking • 203 implied HN points • 18 Feb 25
🕹 Technology AI Security Software Innovation Market Trends
  1. Many AI security companies may struggle to survive because large language models (LLMs) are easier and cheaper to use. Most businesses prefer using LLMs instead of creating their own models.
  2. The future of AI security is unpredictable because it's hard to guess when companies will start using their own AI models. This makes it a challenging space for startups to gain traction.
  3. There’s a lot of activity in both security and AI, making it tough to keep up. The combination of these two fast-evolving fields adds more complexity to security concerns.

Breaking: OpenAI's efforts at pure scaling have hit a wall.

Marcus on AI • 7825 implied HN points • 13 Feb 25
🕹 Technology AI Machine Learning Software Development Data science Innovation
  1. OpenAI's plan to just make bigger AI models isn't working anymore. They need to find new ways to improve AI instead of just adding more data and parameters.
  2. The new version, originally called GPT-5, has been downgraded to GPT 4.5. This shows that the project hasn't met expectations and isn't a big step forward.
  3. Even if pure scaling isn't the answer, AI development will continue. There are still many ways to create smarter AI beyond just making models larger.

Battery production is genuinely difficult

Intercalation Station • 99 implied HN points • 01 Nov 24
🕹 Technology Battery tech Manufacturing Sustainability Electric Vehicles 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.

Everything I warned about in Taming Silicon Valley is rapidly becoming our reality

Marcus on AI • 7114 implied HN points • 11 Feb 25
🕹 Technology Artificial Intelligence Regulation Innovation Cybersecurity Ethics
  1. Tech companies are becoming very powerful and are often not regulated enough, which is a concern.
  2. People are worried about the risks of AI, like misinformation and bias, but governments seem too close to tech companies.
  3. It's important for citizens to speak up about how AI is used, as it could have serious negative effects on society.

OpenAI is an App Company Now

Big Technology • 13260 implied HN points • 31 Jan 25
🕹 Technology AI Software Apps Innovation Data
  1. OpenAI is focusing more on building apps rather than just creating AI models. This shift reflects a need to stay competitive and profitable in the changing AI landscape.
  2. The market for AI applications is growing, and OpenAI's ChatGPT is performing well, far ahead of its competitors in earnings. This positions OpenAI favorably as it continues to innovate its products.
  3. While OpenAI aims to develop artificial general intelligence, it faces challenges as competition increases and cost structures change in the AI industry. Staying ahead will require continuous product improvements.

The race for "AI Supremacy" is over — at least for now.

Marcus on AI • 23595 implied HN points • 26 Jan 25
🕹 Technology AI Innovation Hardware Software Global Competition
  1. China has quickly caught up in the AI race, showing impressive advancements that challenge the U.S.'s previous lead. This means that competition in AI is becoming much tighter.
  2. OpenAI is facing struggles as other companies offer similar or better products at lower prices. This has led to questions about their future and whether they can maintain their leadership in AI.
  3. Consumers might benefit from cheaper AI products, but there's a risk that rushed developments could lead to issues like misinformation and privacy concerns.

A new generation of AIs: Claude 3.7 and Grok 3

One Useful Thing • 1968 implied HN points • 24 Feb 25
🕹 Technology AI Machine Learning Computing Innovation Research
  1. New AI models like Claude 3.7 and Grok 3 are much smarter and can handle complex tasks better than before. They can even do coding through simple conversations, which makes them feel more like partners for ideas.
  2. These AIs are trained using a lot of computing power, which helps them improve quickly. The more power they use, the smarter they get, which means they’re constantly evolving to perform better.
  3. As AI becomes more capable, organizations need to rethink how they use it. Instead of just automating simple tasks, they should explore new possibilities and ways AI can enhance their work and decision-making.

Grok 3 and an accelerating AI roadmap

Democratizing Automation • 482 implied HN points • 18 Feb 25
🕹 Technology AI Machine Learning Innovation Software Data science
  1. Grok 3 is a new AI model that's designed to compete with existing top models. It aims to improve quickly, with updates happening daily.
  2. There's increasing competition in the AI field, which is pushing companies to release their models faster, leading to more powerful AI becoming available to users sooner.
  3. Current evaluations of AI models might not be very practical or useful for everyday life. It's important for companies to share more about their evaluation processes to help users understand AI advancements.

Irony too funny for words

Marcus on AI • 8813 implied HN points • 06 Feb 25
🕹 Technology AI Software Development Ethics Innovation
  1. Once something is released into the world, you can't take it back. This is especially true for AI technology.
  2. AI developers should consider the consequences of their creations, as they can lead to unexpected issues.
  3. Companies may want to ensure genuine communication from applicants, but relying on AI for tasks is now common.

Morris Chang and the Origins of TSMC

Construction Physics • 46767 implied HN points • 31 Dec 24
🕹 Technology Semiconductors Innovation Manufacturing Entrepreneurship History
  1. Morris Chang founded TSMC in 1985, turning it into a key player in the semiconductor industry. He saw the need for a company that could manufacture chips for others, which allowed many new companies to emerge.
  2. Chang's journey was not smooth; he faced many challenges and failures before achieving success with TSMC. Much of his early career included tough breaks, but he persevered and created something significant.
  3. TSMC's unique business model changed how semiconductor companies operated by providing manufacturing services without competing directly with clients. This innovation helped TSMC grow quickly and become vital for tech giants like Apple and Intel.

Founders over fifty.

The Common Reader • 1842 implied HN points • 08 Feb 25
💼 Business Entrepreneurship Investing Venture Capital Startups Innovation
  1. Older founders often have more experience and valuable connections than younger ones. This helps them spot opportunities that others might miss.
  2. Studies show the average age of successful entrepreneurs is around 45. Older founders are more likely to make successful sales than younger founders.
  3. Experience from past failures can improve the chances of success in new ventures. Many middle-aged entrepreneurs have the financial stability to start their own businesses.

Achievement, here and there

benn.substack • 613 implied HN points • 14 Feb 25
💼 Business Startups Entrepreneurship Innovation Technology Economic Development
  1. Many startups often pivot to new ideas after their initial product fails. This happens so frequently in Silicon Valley that it’s often seen as a normal part of business.
  2. Founders usually start companies not just to solve problems, but also to gain status and success. They might care more about how they look to others than the specific product they offer.
  3. There's a growing trend where success in technology is being intertwined with politics. People are now noticing and valuing the impact of policy making as much as tech achievements.

Shame on Google, twice

Marcus on AI • 4703 implied HN points • 09 Feb 25
🕹 Technology AI Software Innovation Data Ethics
  1. Large language models (LLMs) can make mistakes, sometimes creating false information that is hard to spot. This is a recurring issue that has not been fully addressed over the years.
  2. Google has been called out for its ongoing issues with LLMs failing to provide accurate results, as these problems seem to occur regularly.
  3. The idea of rapid improvements in AI technology may be overhyped, as the same mistakes keep happening, indicating slower progress than expected.

The Sequence Research #663: The Illusion of Thinking, Inside the Most Controversial AI Paper of Recent Weeks

TheSequence • 105 implied HN points • 13 Jun 25
🕹 Technology AI Research Innovation Computing Data science
  1. Large Reasoning Models (LRMs) can show improved performance by simulating thinking steps, but their ability to truly reason is questioned.
  2. Current tests for LLMs often miss the mark because they can have flaws like data contamination, not really measuring how well the models think.
  3. New puzzle environments are being introduced to better evaluate these models by challenging them in a structured way while keeping the logic clear.

GPT-4.5 Feels Like a Letdown But It’s OpenAI’s Biggest Bet Yet

The Algorithmic Bridge • 605 implied HN points • 28 Feb 25
🕹 Technology AI Machine Learning Software Innovation Data science
  1. GPT-4.5 is not as impressive as expected, but it's part of a plan for bigger advancements in the future. OpenAI is using this model to build a better foundation for what's to come.
  2. Despite being larger and more expensive, GPT-4.5 isn't leading in new capabilities compared to older models. It's more focused on creativity and communication, which might not appeal to all users.
  3. OpenAI wants to improve the basic skills of AI rather than just aiming for high scores in tests. This step back is meant to ensure future models are smarter and more capable overall.

Some Regulatory Certainty

Points And Figures • 612 implied HN points • 28 Feb 25
🔮 Crypto Regulation Innovation Investment Market Trends Financial Technology
  1. The SEC has decided that crypto memecoins are not considered securities, giving the industry more regulatory clarity. This is a positive change compared to the confusion that existed before.
  2. While crypto hasn't become essential for everyone's daily life yet, there are potential future uses, like tokenizing assets or using stablecoins for easier international payments.
  3. Regulation can sometimes create unfair advantages for big companies and stifle competition. It's important to be aware of these effects while also ensuring that people aren't misled by things like memecoins.

Deep dive in hybrid battery packs

Intercalation Station • 159 implied HN points • 30 Oct 24
🕹 Technology Batteries Energy Electronics Automotive Innovation
  1. Hybrid battery packs mix different battery chemistries to improve performance. This allows for better energy management and potentially raises the accuracy of state-of-charge readings.
  2. These new packs can perform better in low temperatures and support faster charging. By combining different cell types, they can work more efficiently across different conditions.
  3. While hybrid batteries have advantages, they can also be more expensive and heavier. This extra cost might make them less appealing for some applications, though prices for certain battery types are dropping.

Build AI or Be Buried By Those Who Do

Contemplations on the Tree of Woe • 3574 implied HN points • 30 May 25
🕹 Technology AI Machine Learning Innovation Automation Data science
  1. There are three main views on AI: believers who think it will change everything for the better, skeptics who see it as just fancy technology, and doomers who worry it could end badly for humanity. Each group has different ideas about what AI will mean for the future.
  2. The belief among AI believers is that AI will become a big part of our lives, doing many tasks better than humans and reshaping many industries. They see it as a revolutionary change that will be everywhere.
  3. Many think that if we don’t build our own AI, the narrative and values that shape AI will be dominated by one ideology, which could be harmful. The idea is that we need balanced development of AI, representing different views to ensure freedom and diversity in thought.

Literary culture can't just dismiss AI.

The Common Reader • 3402 implied HN points • 29 Jan 25
🎭️ Culture Literature Art Media Innovation Technology
  1. AI is changing how we think about literature and culture. It's important to embrace this change rather than ignore it.
  2. Modern literature needs to engage with new technologies to stay relevant. Just like past innovations, AI can be a tool for creativity instead of a replacement.
  3. Writers should adapt to the evolving cultural landscape. Accepting AI as part of the literary world can help create a richer and more diverse artistic expression.

Deep Into the Depths

Atlas of Wonders and Monsters • 339 implied HN points • 27 Feb 25
🕹 Technology AI Research Innovation Computer Science
  1. AI tools have started using the term 'deep' to suggest they dig into more complex information, but this may often not be the case. Many still just skim the surface instead of really exploring.
  2. While AI is getting better at research by gathering information quickly, true deep research requires more human-like exploration and understanding. It's about going beyond just looking up facts.
  3. Don't be fooled by the hype around AI's 'deep research' capabilities. They are useful, but they aren't as profound or groundbreaking as some might claim.

Back to the Future with Gordon Moore

The Chip Letter • 5897 implied HN points • 28 Jan 25
🕹 Technology Semiconductors AI Innovation Computing Industry Trends
  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.

Tech journalists that hate tech

Machine Learning Everything • 459 implied HN points • 11 Feb 25
🕹 Technology Journalism Criticism Tech Culture Innovation
  1. Some tech journalists seem to focus only on the negative aspects of technology and businesses. This makes their articles feel less relevant to people who actually care about tech advancements.
  2. Independent tech commentators are becoming more popular because they show a real passion for their subjects. They talk about technology in a way that's exciting and authentic, unlike some critics.
  3. Criticism of tech leaders often lacks balance, focusing only on their flaws without acknowledging their successes or innovations. This one-sided view can lead to a misunderstanding of the tech industry.

Google DeepMind CEO Demis Hassabis: The Path To AGI, LLM Creativity, And Google Smart Glasses

Big Technology • 5754 implied HN points • 23 Jan 25
🕹 Technology Artificial Intelligence Machine Learning Innovation Tech industry Digital Transformation
  1. Demis Hassabis thinks we're still a few years away from achieving AGI, or human-level AI. He mentions that while there's been progress, we still need to develop more capabilities like reasoning and creativity.
  2. Current AI models are strong in some areas but still have weaknesses and can't consistently perform all tasks well. Hassabis believes an AGI should be able to reason and come up with new ideas, not just solve existing problems.
  3. He warns that if someone claims they've reached AGI by 2025, it might just be a marketing tactic. True AGI requires much more development and consistency than what we currently have.

𝗔𝗜 𝗚𝗲𝗼𝗽𝗼𝗹𝗶𝘁𝗶𝗰𝘀 𝗱𝗲𝗯𝗮𝘁𝗲! My conversation with China’s Victor Gao plus a hot take on new essay by Anthropic’s CEO Dario Amodei

Marcus on AI • 4979 implied HN points • 29 Jan 25
🕹 Technology AI Geopolitics Innovation Startups Export Controls
  1. In the race for AI, China is catching up to the U.S. despite export controls. This shows that innovation can thrive under pressure.
  2. DeepSeek suggests we can achieve AI advancements with fewer resources than previously thought. Efficient ideas might trump just having lots of technology.
  3. Instead of just funding big companies, we need to support smaller, innovative startups. Better ideas can lead to more successful technology than just having more money.

The hard thing about building AI applications

Generating Conversation • 140 implied HN points • 27 Feb 25
🕹 Technology AI Software Innovation Product Design User Experience
  1. Good AI should figure things out for you before you even ask. It should make your life easier by anticipating what you need without requiring a lot of input.
  2. Trust is key for AI systems. They should be honest about what they don't know and explain their level of confidence. This helps users rely on them more.
  3. AI should take complex information and boil it down to what's important and easy to understand. It should help you find insights quickly without overwhelming you with details.

Illumina's Roche Reaction

ASeq Newsletter • 21 implied HN points • 01 Mar 25
💼 Business Health tech Market Trends Corporate strategy Investments Innovation
  1. Illumina is facing several challenges, including layoffs and changes in their offerings. They've introduced a new spatial instrument but are cutting employee stock grants.
  2. In their response to Roche, Illumina emphasized that Roche's technology is complex and might not be as efficient. They believe their products, like the MiSeq i100, are on par in performance.
  3. Overall, Illumina didn't provide clear answers to questions about their strategy, leaving some uncertainty about their future direction in the market.

Secret SwiftUI: A practical use of _VariadicView

Jacob’s Tech Tavern • 2842 implied HN points • 10 Feb 25
🕹 Technology Software Programming App Development User Interface Innovation
  1. The \\_VariadicView feature in SwiftUI helps create custom components like flexible tab bars and lists. It's useful for developers wanting more control over their UI elements.
  2. Finding real-world examples for \\_VariadicView can be tough, but it can significantly help in building complex UIs like chat applications.
  3. A specific application of \\_VariadicView is creating a reusable 'ChatList' component that manages scroll inversion, making it easier to handle messaging apps.

What Learning by Doing Looks Like

Construction Physics • 25889 implied HN points • 12 Dec 24
🕹 Technology Engineering Innovation Research Manufacturing Energy
  1. Learning curves show that the more something is produced, the cheaper it gets. This happens because experience helps make production more efficient.
  2. The evolution of polycrystalline diamond drill bits shows that real-world experience is key to improving technology. Companies learned from failures and made better bits over time.
  3. Understanding how different bits work in different rocks was crucial for progress. Customizing the design of drill bits based on experience led to much better drilling performance.

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

Democratizing Automation • 760 implied HN points • 12 Feb 25
🕹 Technology AI Science Research Innovation Computing
  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.

Defending Open Source AI Against the Monopolist, the Jingoist, the Doomer and the Idiot

Future History • 200 implied HN points • 19 Feb 25
🕹 Technology Open Source AI Innovation Policy Economics
  1. Open source software, like Linux, is crucial for innovation and economic growth. If it were starting today, too many restrictions could hurt its potential.
  2. Different groups, like monopolists and jingoists, try to control technology by spreading fear or misinformation. This can lead to laws that stifle competition and creativity.
  3. It's important to support open source AI to encourage fairness and competition. When more people can innovate, technology can improve everyone's lives.