Do You Manage a Team?

Hello,

This is Simon with the latest edition of The Weekly. In these updates, I share key AI related stories from this week's news, list upcoming events, and share any longer form articles posted on the website.

We had a laugh this week about our new 'team members'. We use Claude and an internal tool called Jake, and we refer to them constantly: "I'll get Jake to do that" or "I'll ask Claude." It genuinely feels like these are the names of colleagues.

These new team members are undeniably efficient — handling tasks I either don't have time for or the capability to do myself. I'm sure you recognise the scenario. But what I'm also noticing is that we're building more and more workflows around these tools. We're creating a growing number of agents that carry out tasks on our behalf. Right now, it's all manageable, but what happens as that number keeps growing?

My boss has oversight of our team. He guides and directs us, pushing us in certain directions and pulling us back from others. But who's doing that for all of our new 'colleagues'?

From what I can see, this could go a few ways:

Given that AI is helping us build leaner, more efficient teams, I think the answer will be a combination of one and three. We'll develop more sophisticated systems to manage AI — an overseer that hands off smaller tasks to focused agents and coordinates a network of tools. But alongside that, each person will build their own AI manager. People who were previously individual contributors will, in the future, be managing their own team — not of humans, but of AI agents and systems capable of handling vast amounts of work, governed and directed by their own experience and judgement.

For that to happen, individuals and teams will need significant training and enablement. Outside of a few small pockets, I don't see many businesses being anywhere close to working like this today.

What's your view? Have you found yourself managing lots of different AI tools and agents? Is there any coordination happening, or is everything still operating separately?

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Oracle announced new Fusion Agentic Applications for customer experience on April 9 and followed that with an expansion of its agentic AI platform into corporate banking on April 14. In both cases, Oracle is pitching coordinated AI agents that can automate and orchestrate outcome-based work inside the software companies already use, rather than asking staff to handle everything manually through standalone assistants.

Why it matters: The next phase of AI adoption may happen less through separate chat interfaces and more through finance, service, sales, and operations systems

The Wall Street Journal reported this week that US investor-owned utilities now plan to spend $1.4 trillion over five years to modernise the grid and support rising electricity demand, with AI data centres a major driver. That lands alongside Bloomberg reporting that US AI data-centre expansion is being slowed by shortages of transformers, switchgear, and batteries, and Reuters reporting that US power demand is set to hit record highs in 2026 and 2027 as AI-related electricity use rises.

Why it matters: AI is no longer just a software story. Energy supply, utility investment, grid constraints, and physical equipment bottlenecks are becoming strategic business issues that can affect cost and rollout speed.

Anthropic launched Project Glasswing on April 7, giving selected partners early access to Claude Mythos Preview for defensive security work. The company says the model is capable enough in coding and vulnerability discovery that it is working with major partners including AWS, Google, Microsoft, Apple, Nvidia, Cisco, JPMorganChase, and the Linux Foundation.

Why it matters: This is a useful reminder that AI adoption is not only a productivity story. As models become more capable, cyber resilience, software supply-chain risk, and internal controls move closer to the centre of AI strategy.

Thanks for reading, and see you next Thursday.

Simon,

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