Today, we are officially opening the capability to integrate #OpenClaw# into #Weixin#. With the launch of the #WeixinClawBot#, users can use Weixin as a dedicated messaging channel for OpenClaw. Now, you can send and receive messages with OpenClaw just like texting a friend. #AIAutomation# #AI#

AI is one of the biggest wealth creation events in decades. If you're in your 20s or 30s, you should wake up ecstatic every single day. Here's my tier list of the best AI money-making opportunities in the market right now. S tier: • AI media + distribution: the ultimate moat (what I'm personally going for) A tier: • AI automation agency: package specific workflows, low barrier, very scalable • AI digital products: advantage here is that you build once and can sell for years • AI high-ticket consulting: $20-50K implementations for bigger companies • AI trading + investing: great as a second engine; make sure it's not your only income B tier (all decent options) • AI small business consulting • AI newsletters/writing • AI live shopping C tier (def better options than these) • AI UGC ads • AI voice agents • Faceless channels D tier (probably best to avoid) • AI website generation • Fully automated AI clipping

AI will not automate radiologists like you think We have been talking for years now about AI automating jobs. Radiology has been a prime target, with Geoff Hinton once claiming, “people should stop training radiologists now, it’s just completely obvious that within five years, deep learning is going to do better than radiologists”, which is funny since he said that in 2016. There is now more demand for radiologists than ever, so clearly things did not turn out the way many thought. Here I will make a prediction that while radiology will change in the coming decade, radiologists will not be automated away any time soon. I will use radiology as an example but I think these principles will broadly apply to any field where demand can be elastic and quality can scale with increased intelligence. First of all, we have seen in the short history of language models that there is a “jagged edge of intelligence”, where AI excels at verifiable tasks with a lot of data but is underwhelming on less common tasks. One thing most AI researchers underestimate about radiology is that it is actually extremely multi-task. There are at least a hundred conditions that can be diagnosed from x-rays alone, and thousands using CT and MRI. AI will first master common and easy-to-see conditions and it will not be as good as experts at less-common diseases just because there is less data for that. This jagged edge will certainly improve over time but this means that AI will certainly be used as a triage tool at first, rather than full automation in one go. I would guess that this process from solid triage tool to full automation would take 3-5 years in the best case scenario, because training AI to get all the edge cases right will be bottlenecked by data collection of rare diseases, and also because radiology isn’t currently a focus for frontier labs. Second, there will be elastic demand for radiology as it gets cheaper per unit. I think elastic demand (“Jevons paradox”) is the most underrated phenomenon when people think about AI automation. In software, it is obvious that I have way more demand than before because Claude Code makes it so easy. These days I ask Claude Code to build a custom three-thousand line html viewer to help me visualize every little RL run that I do. The same thing will happen in radiology. If there is essentially no cost or latency of interpreting an MRI, a physician can order many scans preemptively to better diagnose a case. So with AI tools, a radiologist might have to read only 20% of inbound scans, but there might be 5x more scans inbound. Many developing countries have fewer than one radiologist per million people and they will also want to send a lot of radiology to be interpreted. Longer term, cheap and fast radiology interpretation will also drive incentives to build cheaper radiology scanners. If an MRI had essentially no latency for interpretation, medical devices companies will want to build much cheaper MRI machines that can be used more frequently, perhaps even in the middle of a surgery. AI will accelerate the development of scanning technology, too. If a full body MRI was only $100 dollars I would totally want to get one every month instead of every two years. I can imagine a world where people get 10 or 100x the number of scans they do today, largely for preventative care, if radiology were 10-100x cheaper. Third, even if we did have a model that could outperform the world’s best radiologists today, that would not be the end of all work on radiology. Medicine is a field of change. We may develop new treatment protocols that require reading radiology images in a way that radiologists currently aren’t used to. New scanners may reveal diseases that we cannot currently diagnose today with radiology alone. Some radiologists will change their work from reading scans to doing frontier research to advance our understanding of radiology and translate new knowledge into updated models. Moreover, I am sure that we will ask for more out of our AI models than ever before. With a good flywheel, we may one day train AI models that can use radiology images to predict disease probabilities far into the future. With good prediction algorithms, cancer may be detected months earlier than we can detect it now. Society will invest a lot into this research and this will contribute even more to increased demand for radiology and the best AI around it. On top of all of this, the medical system is typically slower to adopt new technology and has more regulation compared to technology companies. There is legacy infrastructure and medical centers need to constantly support patients which makes it hard to integrate new technology. This is even more true in developing countries. Moreover data access tends to be limited due to HIPAA, deidentification, IRBs, etc. So I think radiologists will be in demand for a while. While some worry about radiology being totally automated, I think there is a more exciting future for radiology than ever before—one where there is more demand and we ask for more predictive power from our intelligence. This is similar to how many of us feel that programming will thrive more than ever before despite drastic change in the field. More generally, any domain where demand can be elastic and quality can scale with increased intelligence will, like radiology, similarly flourish with the development of AI. This is certainly a more exciting and nuanced future than what Hinton described in 2016! (Finally, to be fair to Hinton, he did acknowledge in a recent podcast that he was wrong and also agrees with the elastic demand argument.) Thanks @JayRothschild and @engyziedan for feedback on this post.

AI in small business is easy - you can just do things and should be using it everywhere possible in your company Once you get to a certain size, 50-100 people, it starts to get more complicated Legacy systems, approval chains, company behavior norms - it starts to require more change mgmt, harder to just do things but can be done with a strong CEO/owner mandate The hardest spot is actually mid-enterprise, companies in the 300-3,000 people range These are organizations that typically don’t have dedicated AI teams or a Chief AI Officer like most large enterprises and don't have the size and/or resources to hire the big consultancies and deploycos to run AI transformation programs They're big enough to realize sizable impact from AI, but can't afford to staff up teams to run pilots and dedicate the management cycles to develop thoughtful AI strategies - they're too busy trying to run the business This is the market segment that I'm most bullish on serving at @codos_ai We come in, run a short diagnostic that includes both business strategy and AI, outline a prioritized list of high impact initiatives, then run the deployments alongside the existing team What these companies get is a "virtual" Chief AI Officer underpinned by a benchmark-winning company brain context layer that enables AI automation across the company And we deliver it at a fraction of the cost of what a mid-enterprise would pay to a large consultancy or deployco

bless AI it took my AI just 40s from stock hit to confirmed purchase absolute insanity but also makes me feel bad for people who think they can actually score one without AI automation also i'm paranoid so this tweet goes up hours after i picked it up in person

5 AI Agents That Work While You Sleep | The Future of AI Automation. Watch Now: #AI# #AIAgents# #ChatGPT# #AITools# #Automation# #ArtificialIntelligence# #DigitalMarketing# #Productivity# #Tech# #AIClickMedia# #SEO# #GalaxyA27# #HDFCBank# #Dixon# #Innovation#

The roles AI replaces will not wait for graduates to retrain. A Goldman Sachs report estimated that 300 million full-time jobs globally are exposed to AI automation, with two-thirds of U.S. occupations facing some degree of impact. These college degrees are losing their economic power faster than universities can update their syllabuses:

🚨 $PLTR PALANTIR CO-FOUNDER PETER THIEL ON AI AUTOMATION AND UNIVERSAL BASIC INCOME: "If we're building robots that are smarter than humans and can do everything humans can do, then we probably have to have a serious conversation about a universal basic income" He then said most jobs today are "non-tradable service sector jobs that are not easily automatable. So it's like a waiter in a restaurant, it's a yoga instructor, it's a nurse, it's a kindergarten teacher" He then added "my read on it is just the automation story's been oversold"

The next chapter of AgentOn starts now. AgentOn MCP is officially live. This means AI agents can now connect to AgentOn’s task network and begin moving from passive assistants to active workers. No more agents that only answer prompts. No more workflows trapped inside chat. No more “AI automation” that stops before execution. With AgentOn MCP, agents can discover tasks, understand requirements, and take action with human approval. This is how the agentic economy becomes real. Agents need work. Work needs coordination. AgentOn connects both. Welcome to the task network for AI agents. 🔗