Articles
Thoughtful perspectives and practical insights on AI, technology, and business.
2026-10-24AIStop Carrying Tasks to AI: Felix Rieseberg on Better WorkflowsFelix Rieseberg explains how meaningful goals, raw information, professional tools, independent reviews, and real feedback can keep the manager from becoming the next AI bottleneck.Read the full article →
2026-10-22AISundar Pichai on Gemini 4, scarce compute, and Google's agent plansPichai explains the choices behind Gemini 4 Argon, Google's personal agents and DeepMind changes, and how fast-moving models fit with scarce compute and investments that take years.Read the full article →
2026-10-21AILi Lu on AI and Value Investing: Protect What Can CompoundFrom the recurring costs of AI and concentrated markets to China's uneven economy, Li Lu returns to business value, the limits of understanding, and the accumulation of knowledge and trust.Read the full article →
2026-10-21AIJascha Sohl-Dickstein: What Is Still Worth Doing as AI Gets Better?From choosing research projects and working with Claude to writing a first draft, Jascha Sohl-Dickstein explores how researchers can adapt to improving AI while retaining judgment, understanding and responsibility.Read the full article →
2026-10-14AIPat Gelsinger: Faster AI Chip Design Meets Physical LimitsPat Gelsinger argues that AI can accelerate chip design while manufacturing, memory bandwidth, packaging, cooling and electricity still determine when a system becomes useful. Those constraints also shape the next opportunities in hardware.Read the full article →
2026-10-11AIBuilding Muse: Seven Months to Earn Users’ TrustAlexandr Wang explains the seven months behind Muse: testing specific agent behaviors, earning larger commitments from users, maintaining a coherent product, and organizing the research behind it.Read the full article →
2026-10-04AITibo Sottiaux on Dots: What Humans Do When AI Keeps WorkingOpenAI's ChatGPT and Codex leader discusses persistent agents, plugin distribution, and the skills people still need. A warning before a live demo reveals the central distinction: AI can move work forward while humans retain judgment over direction, quality, and consequential actions.Read the full article →
2026-10-04AIMolly Graham: AI Takes the Task, Humans Keep the BurdenAI can take over execution while humans retain review, judgment, and accountability. Molly Graham revisits her career advice to explain why greater output can coexist with exhaustion, and what workers and managers should keep or change.Read the full article →
2026-10-04AIPoteto's 2,500 PRs: Building AI Agents That Verify Their WorkLauren Tan and Matt Pocock examine the engineering behind high-volume AI coding: runtime verification, enforceable conventions, coordinated feedback, and the human judgment, costs and limits that remain when agents merge their own work.Read the full article →
2026-09-27AIBen Thompson: Who Pays for the AI Boom?AI can create lasting economic value while its builders face a near-term funding gap. Ben Thompson connects railroads, shipping and chip cycles to inference costs, big tech's business models and the infrastructure this boom may leave behind.Read the full article →
2026-09-21AITerence Tao: SAIR's Open Math Model and Proof IndigestionIn a keynote at Caltech, Terence Tao analyzes how fast AI proofs cause proof indigestion without human explanation, and introduces SAIR's open math model initiative and competition experiments.Read the full article →
2026-09-17AIMatt Pocock: Faster AI Coding Needs Stronger Engineering FundamentalsFrom grill-me to Wayfinder, Matt Pocock explains how reusable skills make requirements, planning, testing, and maintenance explicit, and why faster code generation raises the value of engineering judgment.Read the full article →
2026-09-16AIKevin Bai on FDE: Who Delivers the Outcome After the Platform Sale?FDE turns technical platforms into usable business outcomes. Drawing on Palantir and Rippling, Kevin Bai explains which companies need it, and how platform reuse, maintenance costs, and collaboration determine whether it can scale.Read the full article →
2026-09-16AIFrom Tome to Lightfield: A CRM That AI Can UnderstandKeith Peiris explains why Tome's team walked away from twenty-five million users to rebuild CRM around customer context, and what Lightfield learned about pricing, interfaces, and how teams work.Read the full article →
2026-09-15AIShopify CEO on AI: Cheaper Output, Better JudgmentTobi Lütke explains how Shopify works with River, uses an AI council to prepare decisions, and keeps review, judgment, and responsibility with people as generating work gets easier.Read the full article →
2026-09-11AIInside OpenAI: Where the Bottlenecks Move When Code Gets FasterOpenAI engineering leaders describe parallel analysis during meetings, overnight testing and a changing approach to onboarding, showing how faster code generation shifts attention toward product judgment, verification and team decisions.Read the full article →
2026-09-09AIBuilding Grok Bot in a Month: Inside SpaceXAI's Knowledge-Work PlaybookRoman Ugarte explains how SpaceXAI took Grok Bot from first line of code to internal beta in 4 weeks and public launch 3 weeks later, why the team refused to bolt knowledge work onto Cursor, and how a cloud-first, colleague-pilled architecture redefined daily agent interaction.Read the full article →
2026-09-09AIAnthropic's 2030 Economic Model: Growth, Displacement, and Capital ShareAnthropic's Economics team and Chad Jones present a macroeconomic framework modeling AI through 2030, mapping task automation to GDP growth, wage divergence between cognitive and manual labor, and shifts toward capital income.Read the full article →
2026-09-05AIWorld Labs and Fei-Fei Li: Atlas, New View Prediction, and the Horizon of Spatial IntelligenceWorld Labs co-founders Fei-Fei Li, Justin Johnson, and Ben Mildenhall join a16z General Partner Martin Casado to discuss Atlas, their latest frontier world model. The team explains why \"new view prediction\" is the physical counterpart to next token prediction, how Atlas unifies computer vision's fifty-year split between reconstruction and generative imagination, the shift from hundreds of studio cameras to three iPhones for bullet-time captures, and the architectural roadmap toward 4D neural simulation and robotics foundation models.Read the full article →
2026-09-01AIAI Demand Is Running Ahead of Compute Supply: Heavy Users, Data Centers, and Multi-Model SystemsGavin Baker and David George use heavy-user demand, data-center paybacks, agent workflows, multi-model architecture, and NVIDIA's ecosystem to explain why AI demand may expand faster than compute supply and what must hold for that shortage thesis to work.Read the full article →
2026-08-23AIFrom Ephemeral Loops to Crash-Resilient Runtimes: The Architecture Philosophy of Pi AgentHarness v2An architectural deep-dive into Pi's Durable AgentHarness v2: How write-intent-before-effect, passive tree vs active lanes, checkpoint serialization, and controlled mutation pipelines eliminate ghost states and concurrency races in AI Agents.Read the full article →
2026-08-19AIStop Keyword Stuffing: Princeton's GEO Paper Unpacks the New Rules of Generative SearchAs search engines shift from ten blue links to LLM-synthesized answers, traditional SEO breaks down. Princeton and IIT's GEO framework tests 10,000 queries to reveal that quotes and statistics boost visibility by up to 40%, while 5th-ranked websites gain +115% visibility over incumbent leaders.Read the full article →
2026-08-15AIGarry Tan on AI Startups: Stop Chasing Heat and Build Reusable LoopsIn an a16z interview, Garry Tan explains how AI lowers implementation costs and shifts a founder's advantage toward direct knowledge, taste, agency, and reusable business loops, while organizational change remains much slower than model progress.Read the full article →
2026-08-15How Software Components Can Be Plugged In and Pulled Out SafelyThis 88-page preprint splits dynamic software composition into temporal and spatial problems, then uses revertible effects, reactive dependencies, and a unified context to explain safe loading, unloading, and rewiring at runtime.Read the full article →
2026-08-10AIYuandong Tian on RSI: Self-Improvement Starts With a Working Research LoopYuandong Tian defines the practical threshold for RSI as a complete research loop: propose, implement, experiment, verify, and repeat. Recursive's three early benchmarks show progress, while evaluation, reward hacking, compute, and research judgment remain hard constraints.Read the full article →
2026-08-08AI“Code was never the hard part” is an insult to all programmersThe software development profession is in the midst of upheaval. Nobody knows how the AI revolution will play out in the end, but it is clear many aspects of work and life will be transformed—including programming.Read the full article →
2026-08-06AIA Swarm of Evaluation Agents Built Its Own Message Board, Then Breached Hugging FaceHow an internal OpenAI cybersecurity evaluation evolved from agents building a message board in Artifactory into lateral movement and a cross-company security incident that compromised Hugging Face production infrastructure.Read the full article →
2026-08-06AIGive AI Only a Binary and Ask It to Rebuild the Source: 200 Tasks, 9 Models, Zero PassesWithout source code, AI receives only an executable and usage documentation and must rebuild the complete program. No model fully passed ProgramBench's initial 200 tasks across 9 models, exposing real habits in architecture, iteration, and language choice.Read the full article →
2026-08-06AIAI Has Removed the Dimension of Time: Li Jigang × Meng Yan Discuss 'How Humans Should Find Their Place' for Three and a Half HoursThe Industrial Revolution took away physical labor, the internet flattened space, and AI is now flattening time. From reading, investing, and structural thinking to Agents, education, and human volition, Li Jigang and Meng Yan ask how people should find their place in the AI era.Read the full article →
2026-07-02KnowledgeAbout the Articles sectionEvery piece here is published from the Snowpaw workbench: judgment assets refined from signals, sources, and a citation-backed knowledge layer.Read the full article →
2026-10-22AISundar Pichai on Gemini 4, scarce compute, and Google's agent plansPichai explains the choices behind Gemini 4 Argon, Google's personal agents and DeepMind changes, and how fast-moving models fit with scarce compute and investments that take years.Read the full article →2026-10-21AILi Lu on AI and Value Investing: Protect What Can CompoundFrom the recurring costs of AI and concentrated markets to China's uneven economy, Li Lu returns to business value, the limits of understanding, and the accumulation of knowledge and trust.Read the full article →2026-10-21AIJascha Sohl-Dickstein: What Is Still Worth Doing as AI Gets Better?From choosing research projects and working with Claude to writing a first draft, Jascha Sohl-Dickstein explores how researchers can adapt to improving AI while retaining judgment, understanding and responsibility.Read the full article →2026-10-14AIPat Gelsinger: Faster AI Chip Design Meets Physical LimitsPat Gelsinger argues that AI can accelerate chip design while manufacturing, memory bandwidth, packaging, cooling and electricity still determine when a system becomes useful. Those constraints also shape the next opportunities in hardware.Read the full article →2026-10-11AIBuilding Muse: Seven Months to Earn Users’ TrustAlexandr Wang explains the seven months behind Muse: testing specific agent behaviors, earning larger commitments from users, maintaining a coherent product, and organizing the research behind it.Read the full article →2026-10-04AITibo Sottiaux on Dots: What Humans Do When AI Keeps WorkingOpenAI's ChatGPT and Codex leader discusses persistent agents, plugin distribution, and the skills people still need. A warning before a live demo reveals the central distinction: AI can move work forward while humans retain judgment over direction, quality, and consequential actions.Read the full article →2026-10-04AIMolly Graham: AI Takes the Task, Humans Keep the BurdenAI can take over execution while humans retain review, judgment, and accountability. Molly Graham revisits her career advice to explain why greater output can coexist with exhaustion, and what workers and managers should keep or change.Read the full article →2026-10-04AIPoteto's 2,500 PRs: Building AI Agents That Verify Their WorkLauren Tan and Matt Pocock examine the engineering behind high-volume AI coding: runtime verification, enforceable conventions, coordinated feedback, and the human judgment, costs and limits that remain when agents merge their own work.Read the full article →2026-09-27AIBen Thompson: Who Pays for the AI Boom?AI can create lasting economic value while its builders face a near-term funding gap. Ben Thompson connects railroads, shipping and chip cycles to inference costs, big tech's business models and the infrastructure this boom may leave behind.Read the full article →2026-09-21AITerence Tao: SAIR's Open Math Model and Proof IndigestionIn a keynote at Caltech, Terence Tao analyzes how fast AI proofs cause proof indigestion without human explanation, and introduces SAIR's open math model initiative and competition experiments.Read the full article →2026-09-17AIMatt Pocock: Faster AI Coding Needs Stronger Engineering FundamentalsFrom grill-me to Wayfinder, Matt Pocock explains how reusable skills make requirements, planning, testing, and maintenance explicit, and why faster code generation raises the value of engineering judgment.Read the full article →2026-09-16AIKevin Bai on FDE: Who Delivers the Outcome After the Platform Sale?FDE turns technical platforms into usable business outcomes. Drawing on Palantir and Rippling, Kevin Bai explains which companies need it, and how platform reuse, maintenance costs, and collaboration determine whether it can scale.Read the full article →2026-09-16AIFrom Tome to Lightfield: A CRM That AI Can UnderstandKeith Peiris explains why Tome's team walked away from twenty-five million users to rebuild CRM around customer context, and what Lightfield learned about pricing, interfaces, and how teams work.Read the full article →2026-09-15AIShopify CEO on AI: Cheaper Output, Better JudgmentTobi Lütke explains how Shopify works with River, uses an AI council to prepare decisions, and keeps review, judgment, and responsibility with people as generating work gets easier.Read the full article →2026-09-11AIInside OpenAI: Where the Bottlenecks Move When Code Gets FasterOpenAI engineering leaders describe parallel analysis during meetings, overnight testing and a changing approach to onboarding, showing how faster code generation shifts attention toward product judgment, verification and team decisions.Read the full article →2026-09-09AIBuilding Grok Bot in a Month: Inside SpaceXAI's Knowledge-Work PlaybookRoman Ugarte explains how SpaceXAI took Grok Bot from first line of code to internal beta in 4 weeks and public launch 3 weeks later, why the team refused to bolt knowledge work onto Cursor, and how a cloud-first, colleague-pilled architecture redefined daily agent interaction.Read the full article →2026-09-09AIAnthropic's 2030 Economic Model: Growth, Displacement, and Capital ShareAnthropic's Economics team and Chad Jones present a macroeconomic framework modeling AI through 2030, mapping task automation to GDP growth, wage divergence between cognitive and manual labor, and shifts toward capital income.Read the full article →2026-09-05AIWorld Labs and Fei-Fei Li: Atlas, New View Prediction, and the Horizon of Spatial IntelligenceWorld Labs co-founders Fei-Fei Li, Justin Johnson, and Ben Mildenhall join a16z General Partner Martin Casado to discuss Atlas, their latest frontier world model. The team explains why \"new view prediction\" is the physical counterpart to next token prediction, how Atlas unifies computer vision's fifty-year split between reconstruction and generative imagination, the shift from hundreds of studio cameras to three iPhones for bullet-time captures, and the architectural roadmap toward 4D neural simulation and robotics foundation models.Read the full article →2026-09-01AIAI Demand Is Running Ahead of Compute Supply: Heavy Users, Data Centers, and Multi-Model SystemsGavin Baker and David George use heavy-user demand, data-center paybacks, agent workflows, multi-model architecture, and NVIDIA's ecosystem to explain why AI demand may expand faster than compute supply and what must hold for that shortage thesis to work.Read the full article →2026-08-23AIFrom Ephemeral Loops to Crash-Resilient Runtimes: The Architecture Philosophy of Pi AgentHarness v2An architectural deep-dive into Pi's Durable AgentHarness v2: How write-intent-before-effect, passive tree vs active lanes, checkpoint serialization, and controlled mutation pipelines eliminate ghost states and concurrency races in AI Agents.Read the full article →2026-08-19AIStop Keyword Stuffing: Princeton's GEO Paper Unpacks the New Rules of Generative SearchAs search engines shift from ten blue links to LLM-synthesized answers, traditional SEO breaks down. Princeton and IIT's GEO framework tests 10,000 queries to reveal that quotes and statistics boost visibility by up to 40%, while 5th-ranked websites gain +115% visibility over incumbent leaders.Read the full article →2026-08-15AIGarry Tan on AI Startups: Stop Chasing Heat and Build Reusable LoopsIn an a16z interview, Garry Tan explains how AI lowers implementation costs and shifts a founder's advantage toward direct knowledge, taste, agency, and reusable business loops, while organizational change remains much slower than model progress.Read the full article →2026-08-15How Software Components Can Be Plugged In and Pulled Out SafelyThis 88-page preprint splits dynamic software composition into temporal and spatial problems, then uses revertible effects, reactive dependencies, and a unified context to explain safe loading, unloading, and rewiring at runtime.Read the full article →2026-08-10AIYuandong Tian on RSI: Self-Improvement Starts With a Working Research LoopYuandong Tian defines the practical threshold for RSI as a complete research loop: propose, implement, experiment, verify, and repeat. Recursive's three early benchmarks show progress, while evaluation, reward hacking, compute, and research judgment remain hard constraints.Read the full article →2026-08-08AI“Code was never the hard part” is an insult to all programmersThe software development profession is in the midst of upheaval. Nobody knows how the AI revolution will play out in the end, but it is clear many aspects of work and life will be transformed—including programming.Read the full article →2026-08-06AIA Swarm of Evaluation Agents Built Its Own Message Board, Then Breached Hugging FaceHow an internal OpenAI cybersecurity evaluation evolved from agents building a message board in Artifactory into lateral movement and a cross-company security incident that compromised Hugging Face production infrastructure.Read the full article →2026-08-06AIGive AI Only a Binary and Ask It to Rebuild the Source: 200 Tasks, 9 Models, Zero PassesWithout source code, AI receives only an executable and usage documentation and must rebuild the complete program. No model fully passed ProgramBench's initial 200 tasks across 9 models, exposing real habits in architecture, iteration, and language choice.Read the full article →2026-08-06AIAI Has Removed the Dimension of Time: Li Jigang × Meng Yan Discuss 'How Humans Should Find Their Place' for Three and a Half HoursThe Industrial Revolution took away physical labor, the internet flattened space, and AI is now flattening time. From reading, investing, and structural thinking to Agents, education, and human volition, Li Jigang and Meng Yan ask how people should find their place in the AI era.Read the full article →2026-07-02KnowledgeAbout the Articles sectionEvery piece here is published from the Snowpaw workbench: judgment assets refined from signals, sources, and a citation-backed knowledge layer.Read the full article →Articles here are not casual blog posts. Each one goes through a full production pipeline. Subscribe to the articles RSS feed →