Rob May | AI & Cybersecurity

⭕️ Most AI adoption fails not because the tech doesn’t work, but because there’s no clear business case.

Harnessing AI helps you create policies, training, ROI measures, and governance so AI delivers value instead of chaos.

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Do you know what these privacy notifications mean?
A green dot means an app is using your camera. An orange dot means it's using your microphone. A blue arrow means something is using your location. They sit at the top of your screen, they're easy to miss, and they're one of the simplest privacy tools you've got.
If you spot one when you're not expecting it, swipe down from the top right of your screen to open Control Centre and see which app is responsible. Sometimes the answer is perfectly sensible, like maps while you're navigating. Sometimes it's a torch app that has no business knowing where you are.
I think of this as part of the human firewall. Cyber security isn't only about software and policies, it's about people noticing when something doesn't look right, and a small coloured dot is well worth noticing.
When did you last check which apps can see, hear and locate you?

⭕This is a fascinating interview with @sama @OpenAI at @Stanford, my single biggest takeaway is this:

Push things that already show promise to scales no one has tried before. Empirically, the most interesting and valuable outcomes Sam has seen (in AI, at Y Combinator, and in startups) come from emergent properties at scale, effects that simply do not exist at smaller sizes and that the consensus usually underestimates or actively discourages.

He has no clean theory for why this happens. He just notes that it keeps happening, that most people under-do it (because things break in unpredictable ways and smart people give reasons not to), and that the upside has repeatedly been large.

I think everything else in the talk orbits this idea:

Affordable tokens now let small teams do what used to require large engineering orgs.

ChatGPT emerged by noticing what users were already doing with the API and scaling that behaviour.

Coding was the longer-term “control the computer” bet.

Intelligence is becoming a new utility, but the real constraint is cheap, abundant delivery at scale.

Betting against further scaling (of models or of products) has repeatedly looked premature.

In short: find something that works a little, then scale it harder than feels reasonable. The non-obvious, high-leverage results tend to appear there!

What do you think of this?
youtu.be/8yeoMnKQdv8?is…

The skills that top employers are looking for.

I was at the University of Birmingham yesterday, and one slide has stuck with me. It's the World Economic Forum's Future of Jobs Report 2025, showing the skills top employers want now and the ones rising fastest to 2030.

The top three rising skills are AI and big data, networks and cybersecurity, and technological literacy, which won't surprise anyone who works in my world. What caught my eye is what sits beside them. Creative thinking, resilience and agility, curiosity and lifelong learning, leadership and social influence are all in the rising list too, and resilience and creative thinking appear in both columns. Analytical thinking is the number one core skill today, yet it slips to ninth on the rise list, which tells me it's becoming the baseline, not the differentiator.

I think that's the real message for anyone worrying about AI and jobs. Technical skills get you in the door, but the human ones decide how far you go, because anyone can now ask an AI for an analysis. Knowing which question to ask, having the curiosity to challenge the answer and the leadership to act on it is where the value sits.
It also fits what I see every week with business leaders. The ones struggling with AI rarely have a tool problem, they have a habits, culture and learning problem.

So here's my challenge. If you looked at your own team against the rising list, how much are you investing in the technical skills, and how much in the human ones?