Mayuresh Bakshi

Just coming off of meetings with a couple dozen enterprise IT leaders discussing AI agents. Here are a few of the common themes that stand out: * Lots of conversation that you have to solve an operating model challenge to get the full benefits of AI. Most companies have orgs that have always operated in siloes; but agents are most effectively when they are tied to a process, which often cuts across these siloes. So the big question is how do you start to deploy centrally managed agents that can work across organizational boundaries. Who manages these agents? How do they get deployed and adopted? * Data fragmentation remains a major issue for most organizations. As long as data remains highly fragmented and not in standard formats, or data is not available to the right people and agents, enterprises are dealing with issues around being able to get answers from agents that are accurate or that conform to their business practices. This cuts across both systems with structured data (product metrics or revenue figures) and unstructured data (product roadmap or customer contracts). * Clear sense that companies need to figure out what their core data moats are going to be in the future. If everyone has access to roughly the same superintelligence from the various models, then the context that you feed the models becomes proprietary value in the future. Capturing this data and getting it into a format that agents can use becomes very important. * Everyone is trying to figure out the right metrics to manage to for AI adoption. General consensus that tokens are not the right metric per se, and people leaning more toward business outcomes (in an ideal world). For business outcomes (like more revenue or more shipped product), though, you have to get close to each individual workflow to figure out if it was successfully transformed with AI so it’s harder to manage top down. * Growing view that enterprises are going to live in a multi-model world. Lots of interest (though early in actual adoption) in layers that can route workloads to different models (frontside or open weights) for cost or performance reasons. Also enterprises are trying to figure out what things do you give to the models directly vs. what do you separate as horizontal systems and context so you can swap any system in and out. * Talent for driving AI adoption and implementation still remains a major issue and topic. Many view it as something you necessarily have to train for internally due to a shortage of talent being trained on this in the outside. As an aside, this feels like it remains a huge opportunity for those that get very good at deploying and management agents in an enterprise since most companies are looking for these skills. * The best use-cases for AI tend to be those that fundamentally change the work being done instead of just replacing an existing process and doing it more efficiently. Companies are working through their versions of this individually because it’s different per industry, but this often remains both the most exciting and higher upside uses of AI. Many more topics discussed recently, but overall it’s clear that there’s a ton of change going on with much more to come.

JUST IN: A new Chinese AI model from Zhipu AI reportedly matches Claude Mythos’ performance at finding security bugs.

It should be 100% obvious that there will soon be mythos level models on cyber security that are open and available to anyone. As a byproduct of this, alternative tech stacks will emerge that also drive more economic value and control away from the US’s tech stack. This is what should be considered when thinking through the gate keeping you want to have in AI. If advanced models will become open and available regardless, then by not allowing the release of models you’re neither more secure nor better off strategically. So much of the regulatory approach to AI has to assume China can’t catch up, when all current evidence suggests they can and are. And further, hard to imagine a higher priority than winning in AI for China; so you’re basically betting against their long term ingenuity, talent and motivation. Seems like a bad bet. So your options are either to create gates around your best models, which means you’re asymmetrically disadvantaging yourself, or you work to ensure you’re always at the frontier and driving the future architectures of AI.

Work on the things people can't take away. Titles get reorganized. Companies get acquired. Markets shift, layoffs happen, the role you trained five years for can disappear in a Tuesday morning meeting. But your judgment is yours. Your skills are yours. The relationships you've built, the things you've learned how to do under pressure — nobody can pull those back. Most career anxiety I hear is really anxiety about the wrong asset. People are guarding the title, the office, the org-chart position. Those are on loan. The lender can call them in. What's actually yours is whatever you've built inside yourself: the craft you've sharpened, the trust you've earned, the way you think about problems. So when you're deciding what to spend the next hour on — or the next year — that's the test worth running. Is this making me more of something nobody can take away? Or am I just defending borrowed ground? Spend your effort on the first one.