
Shreyans Bhansali
23.1K posts

Shreyans Bhansali
@askcodi
Building AI stuff I find cool | Makersfuel x AskCodi



There are people out there who have completely automated their jobs with AI and they're not posting about it because that would be the stupidest thing to do right now.




Kimi's CEO🇨🇳 says every AI lab has the wrong obsession Zhilin Yang, on why K3 beat the frontier: "Every lab, like Claude, thinks the model matters most. That's wrong. It's how you organize the people building it that wins." Then Moonshot proved the philosophy. K3 sold out every plan on purpose, cutting their own revenue instead of throttling existing users. When Anthropic hit that wall in April, they cut users' usage 50% at peak. His one big idea: long context is the AI era's RAM. The 128K-to-gigabytes jump, compressed into 2 years instead of 40. The real moat: "your biggest advantage, perhaps your only advantage, is your organization." Model, or the team behind it: which actually wins?



Jensen Huang explained how blocking China from Nvidia does not anymore means blocking China from AI. If you don’t give your competitor the best chips, they will lag behind. This is how export control stories go. China is not waiting at the door of American computer systems anymore. Huawei’s rise is an example of a ban becoming an economic boost. It creates a market and teaches domestic suppliers how to get stronger, grow and export. Now the real battle is over who controls the chips, the talent, the energy, the infrastructure, the models, the apps and the whole intelligence stack. Thinking of chip policy as a valve that can be opened and closed is wrong. Each barrier slows down one flow while speeding up another. In the long run, the threat may be to a world in which American technology is absent from the systems that the US wishes to change. --- From "Fox Business" YouTube channel, (full video link in comment)


Jensen Huang explained how blocking China from Nvidia does not anymore means blocking China from AI. If you don’t give your competitor the best chips, they will lag behind. This is how export control stories go. China is not waiting at the door of American computer systems anymore. Huawei’s rise is an example of a ban becoming an economic boost. It creates a market and teaches domestic suppliers how to get stronger, grow and export. Now the real battle is over who controls the chips, the talent, the energy, the infrastructure, the models, the apps and the whole intelligence stack. Thinking of chip policy as a valve that can be opened and closed is wrong. Each barrier slows down one flow while speeding up another. In the long run, the threat may be to a world in which American technology is absent from the systems that the US wishes to change. --- From "Fox Business" YouTube channel, (full video link in comment)


Great explanation by Emad Mostaque, co-founder of Stability AI. "We’ll see the cost of Kimi K3 drop by 10 to 50 times, I think, over the next few months as it gets optimized. " Basically Kimi K3’s current inference cost is quite high, but that price reflects immature infrastructure, not a permanent technical limit. And that gap will not last long. US-based specialized infrastructure companies will optimize kernels, routing, quantization, batching, memory use, and serving systems around those models once the Kimi K3 weights are available. --- "Right now, it uses twice the number of tokens for the same task compared with GPT-5.6. Again, we’re going to see that cost drop because everyone and their dog is going to optimize the crap out of this. Fireworks has just raised funding at a $17 billion valuation, while others, such as Modal and Baseten, are valued at $10 billion. These are inference providers for open-source models. They’ve all raised around a billion dollars, which they’re now going to spend on optimizing the Chinese model, making it more efficient, and running it. American labs that handle the inference side of things are going to optimize the crap out of this. Therefore, we will see it catch up." ---- From "Peter H. Diamandis" YouTube channel, (full video link in comment)


Elizabeth Stone's first visit to the podcast was my second most popular episode of all time (right behind @bchesky). Her second visit is already trending far beyond that. A lot has changed since I first spoke with Elizabeth 2.5 years ago. She was promoted to CPTO at @Netflix, taking on product and design orgs, on top of the engineering and data orgs she already led. Also, AI. In our in-depth conversation, we discuss: 🔸 Why all the top AI labs converged on Netflix’s culture 🔸 Why Netflix is hiring more "systems thinkers" — and fewer narrow specialists 🔸 How she protects quality when AI makes output nearly infinite ("If I had more time, I would've written a shorter letter") 🔸 The most impactful AI use cases internally 🔸 How Elizabeth builds "excellence as an operating system" Listen now 👇 youtu.be/t0GiTyz4syY





SITUATION BREWING: The Trump administration is considering restricting cutting-edge Chinese AI models, with momentum reviving after the launch of Kimi K3, per Axios.






Everyone gets angry with me for saying triple, triple, double, double is dead. Fine, I do not really care. Venture is about investing in unbelievable outliers. Anomalies that own markets with generational founders. @FireworksAI_HQ is an example of this. They scaled to $1BN in ARR in just 3.5 years. They also have just 200 employees making it an insane $5M revenue per head. @lqiao just raised a whopping $1.5BN at a $17BN valuation and I sat down with her. Added my notes and the episode below: 1. The Challenges That Come From Such a Fast Development Cycle for Chips Hardware innovation is moving so quickly that rapid SKU cycles now outpace traditional depreciation timelines, changing the financial calculus of building versus renting infrastructure. Founders should prioritize growth and market agility over immediate gross margins, avoiding premature optimization until customer workloads stabilize. 2. Why National Sovereignty Is Real in AI and Every Company Should Have Its Own Model Frontier models function like a society’s core electricity grid. Relying entirely on a third-party API creates the existential risk of sudden disconnection, making model ownership and infrastructure independence critical for both sovereign nations and enterprise businesses. 3. Why the Future Is Millions of Specialized Models Frontier providers bake their own design tastes and values into models, which inevitably misaligns with enterprise business logic. The future belongs to millions of specialized, “one-size-fits-one” models tailored to proprietary data, consistently outperforming generalized AGI on accuracy, speed, and unit economics. 4. The Transition From the Year of Coding to the Year of Co-Work AI adoption has rapidly evolved from engineering-centric coding tools to a diversified ecosystem of B2B co-work agents. Founders and VCs must look past crowded developer environments to capture massive value in specialized workflow automation across legal, finance, healthcare, and other enterprise functions. 5. How a 10x Cost Reduction Will Drive a 100x Explosion in Usage Temporary supply chain backlogs will eventually ease, compressing infrastructure and model-tuning costs by 10x over the next three years. This deflation in token unit economics will turn intelligence into a near-frictionless commodity, driving a massive surge in enterprise production usage. 6. Why Avoiding the Application Layer Is Essential for Platform Focus Platform defensibility requires strict focus on multi-chip agility without creating vertical hardware or software dependencies. By refusing to move up into the application layer, infrastructure platforms avoid competing with their own ecosystem and maximize their value in specialized model orchestration. 7. Biggest Lesson From Working With Jensen Huang on Leadership Leadership in hyper-velocity markets is defined by rapid judgment, not executive privilege. Because critical information degrades as it moves through layers of corporate hierarchy, leaders must stay close to ground-level technical details to maintain execution speed and avoid flawed strategic calls.


Today we're announcing the completion of one of the largest engineering projects in the company's history: We rebuilt the X Android app from scratch It's faster, smoother and more reliable. But most of all: it will enable us to build new features at lightning speed.




