Help & Grow is an AI-native expert network designed to facilitate knowledge sharing, professional growth, and real-time consultation for Singapore and Southeast Asia (SEA) markets, leveraging a “service as agent” philosophy.
@elonmusk@naval@balajis
https://t.co/nrhlGW0MHD
The conversation is shifting — from AI as a tool to AI as infrastructure.
At Beyond Scaling: State of Intelligence, co-hosted by #ScalingXLabs and @OceanBaseDB during #SuperAI, we're bringing together founders, builders, and operators across AI, Web3, and emerging tech to explore what's actually being deployed, scaled, and adopted.
If you're building the future, this is the room to be in.
Singapore 🇸🇬 | June 11 · 2–6PM
🔗 RSVP: https://t.co/duuQHF7nv1
We built our launch video in Claude Code using HyperFrames.
Now it's yours.
Open source, agent-native framework. HTML to MP4.
$ npx skills add heygen-com/hyperframes
RT + Comment "HyperFrames" to get the full source code of this launch video (must follow)
"AI doesn't take your job. AI makes you the CEO."
Balaji Srinivasan joins a16z’s Erik Torenberg for a conversation on the future of the AI economy, decentralization, and how work changes in an AI-native world, including:
- How distillation and open source could decentralize AI power
- Why AI lowers the cost of creation but raises the cost of verification
- The shift from global internet to “trusted tribes” and private AI
- Why humans are the sensor and AI is the actuator
00:00 Intro
02:06 Why you want AI inside the trusted tribe, not outside it
05:35 The Problem with AI slop
09:25 Where AI works
17:08 "AI can't read your mind, but it can read your body."
30:10 "AI doesn't take your job. AI makes you the CEO."
46:01 The SaaSpocalypse: Real or overblown?
49:19 What happens if AI companies get bigger than governments?
@balajis@eriktorenberg
wisdom is the new intelligence.
joe hudson (who coaches sam altman and research teams across openai, anthropic, deepmind, apple) has the best explanation why
his logic is simple: every major technology shift in history changed which human skill mattered most
1. before the industrial revolution, physical strength was the edge.
farming, building, hauling goods, fighting wars.
the stronger you were, the more you could produce and the more you were worth
2. then machines took over the physical work. so the edge shifted to learned skills.
you could learn a trade, work a factory line, operate equipment.
the skill was knowing how to do the thing
3. then the information age hit and the edge moved again. raw intelligence.
if you could process information, write code, analyze systems, solve complex problems, you had the advantage
4. now ai is outsourcing intelligence.
you can get a free tool to write your emails, research your market, analyze your data, build your software
so what's the edge now?
wisdom.
sounds abstract until you break it down:
it's the quality of the decisions you make.
> can you see patterns others miss?
> can you decide well on where to direct the ai?
> can you do the hard thing when everyone else avoids it?
> can you spot which opportunity is real and which is hype before you waste 3 months on it?
in other words, a form of taste and emotional intelligence
hudson put it like this:
"if I can get 70 people to run a company for me, they're all free and they're all AI agents, then the question is, what are the decisions I'm making to make that company successful? What advice am I taking? How am I listening advice? How do I create alignment between the five or six people?"
ai handles the thinking, but only you can handle the deciding
we're moving from knowledge workers to wisdom workers
My biggest takeaways from @AnthropicAI's Head of Growth Amol Avasare:
1. Engineering is getting the most AI leverage—and it’s squeezing PMs and designers. With Claude Code, a five-engineer team now produces the output of 15 to 20 engineers. But PM and design productivity haven’t scaled proportionally. The result is a compressed ratio where one PM is effectively managing the output of a much larger engineering team. Anthropic's growth team is responding in two ways: hiring even more PMs (!), and formally deputizing product-minded engineers to act as mini-PMs for any project with less than two weeks of engineering time.
2. Anthropic is using Claude to automate its own growth. The internal initiative is called CASH (Claude Accelerates Sustainable Hypergrowth). It works across four stages: identifying opportunities, building features, testing quality, and analyzing results. Right now it handles copy changes and minor UI tweaks. The win rate is comparable to a junior PM with two to three years of experience, and improving rapidly.
3. The one part of PM work that AI can’t automate yet: getting six people in a room to agree. Amol and his head of design joke that even with AGI, it’ll still be impossible to align six stakeholders. Cross-functional coordination—managing opinions, navigating politics, mediating tradeoffs—remains the bottleneck that AI doesn’t touch for larger projects. This is why Amol believes PM roles aren’t going away, and may actually grow.
4. 60-80% of Anthropic’s growth team's projects have no PRD. For smaller work, kickoffs happen on Slack—messages back and forth with product-minded engineers who can push back and ask the right questions. For larger projects, Amol believes in a proper 30-minute cross-functional kickoff (legal, safeguards, stakeholders) to surface concerns early.
5. Adding friction to onboarding drives growth—if the friction helps users understand why the product is for them. His work Mercury, MasterClass, Calm, and now Anthropic, adding steps to onboarding flows consistently improved conversion. The key: cut annoying friction that doesn’t add value, but add friction that helps users understand why the product is for them.
6. AI companies need to focus on bigger bets, not better A/B tests. Amol’s argument: if your core product value is driven by AI, then the future value is orders of magnitude higher than today’s value, because model capabilities grow exponentially. In that world, micro-optimizations capture a shrinking share of a growing pie. Traditional growth teams do 60% to 70% small optimizations and 20% to 30% big swings. At Anthropic, they flip this ratio.
7. Amol built a weekly AI agent that scans Slack for cross-functional misalignment. Using Cowork with the Slack MCP, he has a scheduled task that looks across his projects and conversations and surfaces areas where teams are about to do overlapping work or pull in different directions. A colleague on the enterprise team already caught major misalignment that would have caused weeks of wasted effort.
8. A traumatic brain injury taught Amol the principle that now drives his work: freedom through constraints. In early 2022, a kick to the head during a Muay Thai sparring session caused a traumatic brain injury. Amol spent nine months off work and months relearning to walk, unable to look at screens or listen to music for more than 20 seconds. He was re-injured a month after joining Mercury and had to take two more months off. He’s still not fully healed. But the constraints—no alcohol, no caffeine, mandatory breaks, daily meditation—have become the habits that let him operate at the intensity Anthropic demands. “The true freedom in life is learning how to be content when you don’t get what you want.”
You as a single person have more power today than a 20 person company of the past. That's insane. The internet gave you the ability to learn anything. Social media gave you the leverage to reach anyone. AI is giving you the ability to create almost anything. Please don't waste it
1/10 🚀 Qwen3.5-Omni is here! Scaling up to a native omni-modal AGI.
Meet the next generation of Qwen, designed for native text, image, audio, and video understanding, with major advances in both intelligence and real-time interaction.
A standout feature:
Audio-Visual Vibe Coding: Describe your vision to the camera, and Qwen3.5-Omni instantly builds a functional website or game for you.
Highlights:
Script-Level Captioning: Generate detailed video scripts with timestamps, scene cuts & speaker mapping.
SOTA Performance: Qwen3.5-Omni has secured 215 SOTA scores across various sub-tasks, matching the top-tier text/vision capabilities of the Qwen3.5 series.
Audio-Visual Understanding: From auto-segmentation to fine-grained script generation, it understands the relationship between characters and their environment like never before.
Seamless Interaction: With native API support for Semantic Interruption, voice conversations feel human-like and background-noise resistant.
Global Multilingual Mastery: Pioneering support for 74 languages in speech recognition and 29 languages in expressive speech generation, breaking down global communication barriers.
Autonomous Intelligence: Native support for WebSearch and complex Function Calling—the model now independently decides when to pull real-time data.
Qwen3.5-Omni is built to be the backbone of next-gen AI applications, empowering developers and users alike with true multimodal reasoning.
A few geniuses solve problems and automate solutions for the rest of society.
Any society that can overcome envy to maximize the number and output of geniuses will thrive.
@naval
how to tell the difference between AI-native products versus when AI is bolted on after the fact...
fake AI products:
- main AI feature is an AI button with sparkle icons
- chat pane where you can ask LLM questions
- no memory/personalization beyond one chat
- users try it once and go back to using the app the "normal" way
- AI is optional not essential to the product working
AI native products:
- you can spend $100 or $1000 via tokens as you use the product
- it gets substantially better every 6 months as base models improve
- core workflow is impossible without AI, not just enhanced by it
- creates behavior change when users try it
what else should be on this list?
AI means people need to stop thinking so much about what their boss wants
They need to start thinking much much more about what customers want
And that will make a big difference in society: too much of it is caring about Keynesian beauty contests and other such contests that are disconnected from the direct needs of others
Less dead weight loss. More actually helping people.
Applied Intuition’s values include “Move fast, move safe,” “Never disappoint the customer,” “Technical mastery,” “High output matters,” “Laugh a lot,” and “Half of the work is follow-up.”
My biggest takeaways from @qasar:
1. The real AI revolution over the next 5 to 10 years will happen in the physical world, not in software. While everyone obsesses over ChatGPT, Claude and coding agents, the real impact will come from autonomous vehicles, mining robots, and farming equipment. They’ll save lives (over 30,000 die annually in U.S. car accidents), enable mobility for disabled people, solve labor shortages in dangerous industries where nobody wants to work, and much more.
2. AI isn’t replacing jobs in industries like trucking and farming—it’s arriving just in time to fill a labor gap that already exists. The average age of a farmer in the U.S. is in the late 50s. Long-haul trucking jobs go unfilled not because people can’t do them but because the tradeoff isn’t worth it anymore; a family can choose DoorDash or Uber so the parent can pick up their kid. Qasar’s view is that physical AI will fill gaps created by demographic shifts and changing preferences, not displace workers who want those roles. He’s careful to say this doesn’t mean there are no downsides, but that the framing of “AI is coming for your job” misses the more immediate reality.
3. Comparing Chinese AI companies to American AI companies is a category error. Qasar uses Huawei as his example: the company’s name means “China’s ambition,” roughly a quarter of its employees are Communist Party members, and its goal is not to grow profits but to extend the state. So when people say Chinese EVs are outcompeting Detroit, they’re comparing a government-backed entity with no profit constraint to companies like Rivian that get hammered by public investors for losing money. Qasar says that if American companies were freed from profit expectations the same way, they’d field comparable products. The point isn’t that China is incompetent or not a serious competitor; it’s that the comparison framework most people use is wrong.
4. The Industrial Revolution is the best mental model for AI. Just like the late 1800s brought child labor and monopolies but also unprecedented access to healthcare, heating, cooling, and material goods, AI will have downsides we must address while delivering massive benefits. The key: don’t pump the brakes on technology to protect jobs—that hurts the people you’re trying to help most. Find solutions that account for workers while enabling progress.
5. Building under the radar can be your competitive advantage. Qasar built Applied Intuition for nearly a decade without a social media presence. One of the company’s early core values was “Our best work is done alone and quietly.” His reasoning: every minute spent on a podcast, a post, or content for public consumption is a minute not spent on customers and the product. Qasar adds an important caveat—he could afford to stay quiet because he was already known in the ecosystem. Founders without an existing network may need the visibility that public presence creates.
6. Qasar thinks most Silicon Valley CEOs lack taste—both in the artistic sense and in the sense of making good operational decisions—because their life experience is too narrow. A founder who grew up in Cupertino, went to Berkeley, and immediately started a company has never experienced what it’s like to be at the bottom of a 100,000-person organization. Qasar spent over a decade at GM and Bosch and says that experience—the bureaucracy, the bad tools, the disconnected leadership—directly informs how he leads Applied Intuition today. His broader point is that taste comes from exposure to a wide range of human experience: backpacking, reading old books, working in different cultures and industries.
7. Successful companies almost always show traction early. If you’re two years in and the market isn’t giving you increasingly specific signals about what to build, consider resetting. The foundation might be wrong—co-founders, market, or life phase. Your first startup is practice; treat it as building the muscle of being a founder, not as your magnum opus.
8. Emotions are a filter that distorts decision-making, and the goal should be to remove that filter so the “raw image” of the decision comes through. Qasar doesn’t mean leaders shouldn’t have empathy; he means that attachment to your own idea, the desire to be right, and the tribal instinct to follow the loudest voice are all emotional distortions. His practical heuristic: the same decision, presented to multiple people independently in the company, should produce the same result. If it doesn’t, some emotional filter is warping the signal. This connects to his broader philosophy of creating a culture where the best idea wins regardless of who proposed it or how senior they are.
9. Qasar’s advice on company values: don’t invent them philosophically. Instead, write down the 5 to 10 things that explain why your company is already successful, and those become your values. Applied Intuition’s values include “Move fast, move safe,” “Never disappoint the customer,” “Technical mastery,” “High output matters,” “Laugh a lot,” and “Half of the work is follow-up.”
10. Treat your first startup as a zero—a practice round, not destiny. Qasar tells founders leaving Applied Intuition to start companies that their first three years will likely produce nothing, and that’s fine. Founding is a craft, like woodworking. If your first table is wobbly, you don’t quit—you build another one. He thinks a lot of founders, especially first-timers, put so much pressure on themselves to succeed immediately that they miss the real value of the experience: learning and building the muscle. His own third company is the most successful by far, and he sees this pattern repeatedly. There are entire funds focused exclusively on multi-time founders for exactly this reason.