Replica Man Issue 15 AW25 31 years. Two sides of me. . #replicaman# #Replicamanmagazine# . #JACKSONWANGxReplicaMan# #MAGICMAN2# #JACKSONWANG# #王嘉爾# #잭슨# #TEAMWANGrecords# @teamwang

With the rapid development of embodied artificial intelligence (AI), a new profession - trainers of embodied AI - has emerged, which comprise roles such as data collectors and data annotators. Jinan and Qingdao in Shandong Province have established robot data collection and training centers tailored to various scenarios. Equipped with simulated workshops replicating real-world environments such as automotive manufacturing, 3-C factories, logistics, retailing, and household care-giving, these centers allow trainers to guide robots one on one through tasks like grasping, identifying, walking, and delivering. (Source: Xinhua)

hyperliquid just restructured its entire revenue model. circle deploys $5b USDC treasury, 90% of yield flows to HYPE buybacks. that's ~$180m/year in structural buy pressure before counting $150m in trading revenue. circle and coinbase each staked $20m as validators. they sunset USDH to make it happen. traded ecosystem sovereignty for $330m combined annual revenue at a 31x P/S. dydx, gmx, vertex have no path to replicating this. the perps war just became a two-tier market and the second tier doesn't have regulated validators buying their token with treasury yield

Each Gigafactory we build is more efficient than the last, reducing our vehicles' carbon footprint further. This includes improving factory layouts, covering roof space with solar panels & leveraging AI to improve how we use energy. We also reduced water use in vehicle manufacturing by 15% year over year & currently use less water per vehicle than almost any ICE carmaker. — We support responsible mining & refining. Specifically: - We source directly from mining & refining companies. Avoiding the middleman enables more transparent & easily traceable supply chain data. - We use external mining initiatives & expect suppliers to undergo independent audits. In addition, vertical integration & regionalization help us have more control over supply. None of our batteries go to landfills—at the end of their usable life, we recycle them to build brand-new battery packs. — Product affordability is fundamental to our mission. The more products we sell, the more we move in the needle in favor of a sustainable future. Model 3 is the first EV to be priced on-par with ICE vehicle equivalents. Tesla vehicles are engineered to be the safest in the world. By collecting anonymous data from 4M+ vehicles in our fleet & replicating real-world accidents, we’re able to make current & future vehicles even safer. In 2022, a Tesla with Autopilot engaged experienced 0.18 accidents per million miles driven, while the US average was more than 8x higher.

Whether it’s existing consulting firms, new ones that emerge, FDEs from agent vendors, or new internal agent engineering roles, the amount of work that is going to be created to implement agents in enterprises will exceed anything we imagine today. The complexity of implementing agents in any existing organizations is very real. When I talk to large enterprises, as you move from a chat paradigm to agents that participate in meaningful workflows, there are a number of things they need to do. First, you have to get agents to be able to talk to your data securely across your systems. In many cases, enterprises have decades of legacy infrastructure that contain the valuable context for AI agents. That’s going to take a ton of work to go modernize and move to systems that work well with agents. Then, you need to ensure that you’ve implemented agents with the right access controls and entitlements, the right scopes to be safely used, and have ways of monitoring, logging, and securing the work that they do. Next, you need to actually document the processes in the organization in a way that agents can utilize for doing the work. You also need to figure out what the new workflow looks like when agents and people are working together on a process, and who steps in where. Just replicating the old workflow will mute the gains. Oh and you likely need to create evals for your top new end-state processes. Finally, you have to keep up with a rapidly changing set of best practices and architectural shifts happening in the agent space. While it’s fun for people to change their personal productivity tools on a dime, it’s 100X harder to do this in a business process. The speed of change is a blessing and a curse right now for anyone trying to keep a stable system design. All of this means that individuals and companies that develop expertise on the above set of components (and more) are going to be needed to help organizations actually implement agents at scale. This is also the rationale for vertical AI agents right now that can go in deep on a business domain and help bring automation to it. This is a huge opportunity right now whether you’re doing this internally or as an external business provider.