Ian Gillespie

Dakota Johnson for Calvin Klein.

Every LLM is a walled garden in a race to beat the hell out of the next foundational model. They all are hoping it’s not like search with one dominant player. They have to invest like it might be. That won’t change for ???? Every enterprise has to keep up with their changing and new models and decide when to move. When to go side by side. When to delete. That’s going to be stressful. And as long as those models don’t truly integrate, and will that ever happen, the amount of work for enterprises to maintain AI and be competitive is going to keep on growing and getting more expensive. And there will be a time when genAI models will be superseded by world view models and who knows what comes after that It’s going to take so many people specializing in various layers and levels of AI In the next 5 years enterprise AI is going to be a mess, with all the different implementations and flavors and sources and models. It’s not inconceivable there can be hundreds of different models in each big enterprise. Just because the company got overwhelmed trying to keep everything tied together. Which in turn could lead very large companies to choose to divest subsidiaries rather than thinking there is benefit from scale. Scale may be a boat anchor to your business. Purely because of AI Curious what everyone thinks ?

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.

This is just stupid. So Sandra Day O’Connor’s appointment was illegal? Clarence Thomas’s? Amy Coney Barrett’s? All appointed by Republican presidents who specifically declared they would only put forward nominees of a particular sex or race.

For XPENG IRON, we developed a general-purpose framework that mimics human skeletal geometry and utilized a muscle-like lattice structure to replicate actual muscular movement.