⚡Coding is Free and Power Scarce. Is there a Solution?Plus: AI Is Improving at Math Problems That Stumped Humans for Decades.
Hello Team, Happy Valentine’s Day. Code is now advancing so fast that it’s not just writing software, it’s improving itself and autonomously creating apps and platforms. As Chinese AI models continue closing the gap with Silicon Valley and AI systems grow stronger at math and reasoning, development is accelerating exponentially. The real constraint is no longer talent or ideas, but who controls the compute, data, and capital needed to build and scale it all.
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Coding is Free and Power Scarce
This week’s signals from Spotify, Elon Musk, and insiders at OpenAI and Anthropic all point to the same reality: “Software is becoming frictionless.” Spotify says its top engineers haven’t written code since Dec 2025, shipping 50+ features in 2025 using AI systems that deploy fixes from a phone, while Musk predicts that by 2026–2027. As we shared earlier, code is coding itself, and it will not change anytime soon. AI will bypass programming languages entirely and turn human intent directly into optimized machine code, shrinking the idea-to-product cycle from months to minutes. At the same time, AI researchers are resigning and warning of existential risks as capabilities scale faster than regulation can keep pace, even as valuations surge toward $380B and political spending rises. The common denominator is clear, and it’s that execution is being commoditized, productivity per worker is rising 5x–20x+, and power is concentrating around data, compute, and distribution. Over the next 1–3 years, most software will be built by autonomous agents; within 5 years, human coding will be a niche, with companies of five doing the work of hundreds. The upside is massive, cheaper innovation, faster science, and lower startup costs, but the downside is equally large: rapid job displacement, monopolized infrastructure, large-scale manipulation, and fragile systems humans no longer fully understand. In the AI economy, skill is becoming abundant, code is dissolving, and the real bottleneck is shifting to judgment, ethics, and who controls the machines that now build everything. China’s AI Models continue to close the gap and undercut Silicon Valley.China’s AI race is accelerating as MiniMax launched its new open-source flagship model M2.5, positioning it as a low-cost alternative to U.S. leaders like Anthropic for coding and agentic tasks, a move that helped push MiniMax’s Hong Kong-listed shares up nearly 10%. The release is part of a broader surge in Chinese models gaining global traction, including Zhipu AI’s GLM-5, praised for coding performance, and Moonshot AI’s Kimi K2.5, which recently became the most-used model on OpenRouter. Together, these launches signal that China is rapidly closing the gap in high-quality, low-cost AI models, increasing competitive pressure on U.S. providers and giving developers more affordable options worldwide. 📚Learning CornerDashboard Builders
AI Is Improving at Math Problems That Stumped Humans for Decades.AI is starting to meaningfully assist mathematicians by rapidly searching literature, combining known results, and occasionally generating valid proofs, helping resolve around 100 long-standing Erdős problems since late 2025. Researchers at OpenAI and Google DeepMind are increasingly using LLMs as research assistants that can cut weeks of work to days, though they still produce large volumes of confident but incorrect results. A new benchmark called First Proof is testing whether AI can solve unpublished problems, with mixed early outcomes. While no major journal has yet published an AI-assisted proof, many experts expect 2026 to be the turning point, positioning AI as a powerful collaborator rather than a replacement for human mathematicians. 🧰 AI Tools of The DayMath Help
🚀 Showcase Your Innovation in the Premier Tech and AI Newsletter (link) As a vanguard in the realm of technology and artificial intelligence, we pride ourselves in delivering cutting-edge insights, AI tools, and in-depth coverage of emerging technologies to over 55,000+ tech CEOs, managers, programmers, entrepreneurs, and enthusiasts. Our readers represent the brightest minds from industry giants such as Tesla, OpenAI, Samsung, IBM, NVIDIA, and countless others. Explore sponsorship possibilities and elevate your brand's presence in the world of tech and AI. Learn more about partnering with us. You’re a free subscriber to Yaro’s Newsletter. For the full experience, become a paying subscriber. Disclaimer: We do not give financial advice. Everything we share is the result of our research and our opinions. Please do your own research and make conscious decisions.
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Friday, February 13, 2026
⚡Coding is Free and Power Scarce. Is there a Solution?
Monday, February 9, 2026
👩💻Move Over Vibe Coding "Agentic engineering" is here.
👩💻Move Over Vibe Coding "Agentic engineering" is here.Plus: My conversation with Caltech's Data Architect about coding, SQL, and Humble engineers.
Here is what we have today, team:
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My Conversations with Caltech Data Architecture.Vibecoding, future-proofing your career, data, SQL, humble engineers and more.
I had the pleasure to sit down with and interview, Armando Plasencia, Caltech Data Architect with 25 years of experience in the Data and Tech world. While chatting, he shared that long-term success in tech will be driven less by mastering specific tools and more by curiosity, humility, and community. He stresses that engineers must keep learning daily as AI automates routine coding, while strong collaboration and openness to feedback now outperform solo expertise. Plasencia highlights open-source as a fast-track career path, noting that contributors gain real-world experience with software used by hundreds of thousands of companies and build trusted professional networks. He also points to low-code and no-code platforms that can reduce development from months to minutes, shifting software creation toward business logic rather than syntax, while emphasizing SQL and data sovereignty as critical skills in an AI-driven era. This is the entire conversation, and we will be uploading shorts on our Substack Notes account.  Move Over Vibe Coding Agentic engineering is here.
A year ago, although it seems many more than that, Andre Karpathy coined the term Vibecoding. During that year we have seen apps like cursor more than 10x in value and usage, and other apps like lovable and repplit become darlings for any one wanting to build apps but lacking coding knowledge. The hype of prompting AI to build Apps and create code is now in the past and we are moving to Agentic Engineering, a new term coined by Andre as well, which is the act of AI coding itself. Researchers have demonstrated that large multi-agent AI systems can now run for days with minimal human input, generating ~1,000 software commits per hour and executing 10+ million actions while building and maintaining complex products like a web browser. This signals the rise of “self-driving codebases,” where AI increasingly designs, writes, tests, and fixes software on its own, a trend that could automate 80%+ of enterprise development within five years and cut software costs by 50–70%. While this does not yet qualify as full AGI, it shows early AGI-like behavior in narrow domains such as engineering, with systems capable of long-term planning, self-correction, and collaboration. As technology begins to build itself, human value will shift toward problem framing, system design, and governance rather than manual coding, making these skills essential for today’s youth. At the same time, cybersecurity risks will rise sharply, as autonomous systems can discover and exploit vulnerabilities in hours, enable self-evolving malware, and amplify supply-chain attacks, meaning future digital security will depend as much on controlling AI behavior as on defending traditional infrastructure. 📚Learning CornerReinforcement Learning: An Introduction – Sutton & Barto
What is the Key to Physical AGI?
Robots Will Learn Like Humans. Data, Not Hardware, Is the Key to Physical AGI Researchers argue that the main barrier to achieving Physical AGI in robotics is not hardware or algorithms, but data, as today’s robots are trained on only thousands of hours of controlled demonstrations compared to the billions of human-years behind modern language and vision models. The only scalable solution is capturing massive amounts of human egocentric video, potentially 100+ million hours, equivalent to 150 lifetimes of experience, and using it to train “world models” that learn physics, cause-and-effect, and task dynamics by predicting how scenes evolve over time. By learning from human experience first and then transferring that knowledge to robots, early systems like DreamZero have already shown 40%+ performance gains from just minutes of video data, suggesting that robots can acquire general physical intelligence with minimal retraining. This approach favors humanoid or human-like machines, which reduce the gap between human and robot movement, and points to a future where robots learn primarily by watching people rather than being manually programmed. If successful, this paradigm could enable robots to perform complex real-world tasks with little supervision, marking a realistic path toward Physical AGI driven by large-scale human experience rather than handcrafted robotics data. 🧰 AI Tools of The DayGenerative Video & World-Model Architectures
🚀 Showcase Your Innovation in the Premier Tech and AI Newsletter (link) As a vanguard in the realm of technology and artificial intelligence, we pride ourselves in delivering cutting-edge insights, AI tools, and in-depth coverage of emerging technologies to over 55,000+ tech CEOs, managers, programmers, entrepreneurs, and enthusiasts. Our readers represent the brightest minds from industry giants such as Tesla, OpenAI, Samsung, IBM, NVIDIA, and countless others. Explore sponsorship possibilities and elevate your brand's presence in the world of tech and AI. Learn more about partnering with us. You’re a free subscriber to Yaro’s Newsletter. For the full experience, become a paying subscriber. Disclaimer: We do not give financial advice. Everything we share is the result of our research and our opinions. Please do your own research and make conscious decisions.
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