Take whatever number of people you thought might be in jobs related to AI deployment in the enterprise and multiply it by 10. Then probably 10 again. A major topic that keeps coming up in talking to CIOs across enterprises of all sizes and industries is the implementation gap for getting agents to work at scale and organizations on mission critical work. As the task goes from implementing a chat system that’s basically an LLM plus search, to connecting to real production systems that both can deliver meaningfully better productivity gains but also introduces meaningfully more risk, a whole new set of work has to be done. You have to ensure the right level of protection of data, updates to access control controls, migration of legacy systems to common modern platforms, create observability across what agents are doing, implement new workflows, figure out the human in the loop moments, drive the change management of the new workflows, and more. Then, all of a sudden the model capabilities get updated and you have to do a set of the above steps over again. Half of what you’ve done is obsolete, and the other half needs to be upgraded to take advantage of new capabilities. Or, token budgets run hot and you have to peel off some of the workloads to lower cost models that will be more cost effective. But then you have to go through those same steps. Enterprise are trying to figure out what is the right set of roles to go and implement the systems in their organization to ensure that the workflows are actually being executed properly, ensure it’s not just slop being produced, and to make sure their organization remains safe and secure. Many companies are starting by repositioning existing IT talent in these functions, but there’s also a growing need for the equivalent of internal FDEs to go take on these tasks in an enterprise. The looks incrementally closer to software engineering than it does traditional IT implementation. Next, almost all AI vendors (labs and the software players) will have some form of next-gen FDE or Applied AI architecture functions to help support these use-cases. The benefit here will be these companies have an incentive to make their capabilities work well so they can bring best practices from a range of customers they’re seeing and directly from the product innovation. And finally, we’re seeing the rise of all new AI services firms or major parts of existing services firms move into AI implementation. Companies will often want to bring in ostensibly neutral players that can work across their tech stack but also have seen best practices across their vertical. There are going to be tons of new service providers that get launched to do this, and many will eventually go and disrupt (or get acquired) by the larger player. Either way, all told, we’re in for years of AI diffusion, and along with it tons of new roles and areas of work to be done to deploy AI at scale.

This is a fantastic post about why jobs aren’t going away in the way some predict. We are constantly making the mistake of confusing task completion with AI with being able to eliminate the whole job. Even as we can automate one or many tasks within a job, the definition of the job almost inevitably just expands to do vastly more of those tasks, do them at a higher quality, or move on to the type of task that hasn’t been automated yet. And as a result of being able to do more of the tasks or at a higher quality level, the job becomes valuable in a new way. And in many cases for now an entirely new audience as well. This will be true for coding, legal work, sales, or marketing. The small business or non-tech company that wants to now take on larger software projects finally can, and they’ll hire to do so. The small business that couldn’t afford a full marketing agency can hire or contract out to a marketer that can do as much as an agency did before now with agents. And so on. Don’t fall into the trap of confusing tasks with jobs.

Mass unemployment from AI is the obvious fear. But the humiliation of being discarded — replaced by a machine, written off by an economy that's moving on — that's what creates real instability. I have seen what happens to people when society decides they don't matter anymore. We cannot let AI do that to an entire generation. My hope is tech companies broker a "New Deal" between artificial intelligence and humanity. Silicon Valley understands the abundance AI can create. What it hasn't answered is how that abundance gets shared. That answer could define what kind of civilization we become. Right now, technology companies hold the power while most people are falling further behind. We need a new kind of tech — dignity tech, stability tech — built to DISTRIBUTE that abundance instead of concentrate it to a few technologists at the top. We need a “New Deal” between Silicon Valley and humanity to help people provide everyday people their basic needs. Food. Clothing. Shelter. Energy. Broadband. At scale. vanjones.substack.com/p/ai-n…