The state of AI in 2026 can be confusing and anxiety-provoking – but there are ways to cope.
I make a good portion of my living out of all the background work I do to understand Generative AI – and sometimes I wish that was not the case.
When I got back to work on January 12, the folder in my email into which I filter Gen AI emails had over 100 items in it. I avoided the whole thing for a couple of days, and then thought: Dang it, just mark them ALL as read. (People who know me will know that I used a much ruder word than dang.)
I did that, and then went back over the first couple of weeks of January and looked at the people / newsletters I regard as “must reads”. (See links at bottom of post).
The initial sinking feeling as I looked at all those emails symbolises the general state of human interaction with the wave of change we are now surfing.
In essence: We are overwhelmed but feel obliged to keep up, and yet we end up putting things off. All of which adds a level of pressure to our lives as they play out in an increasingly uncertain world.
I do think though that when we focus on the things that are actually important, there are ways to relax just a little. Rather than trying to stay ahead, perhaps we just need to adopt that surfing mindset: adapt to the wave, hone our skills.
In that spirit, here follows my list of things I think are important to keep track of (or to do, or, of course, not to do) in the year ahead.
The industry hype
This is a not-to-do. I give you just one line of text I saw in my scan of all those email newsletters: “Inferact raised $150M seed at $800M valuation to commercialize vLLM.” (The Neuron, January 2026)
Erm – what? However: I contend that I don’t need to know any of those things right now, nor am I going to be drawn in by the mention of lots of money.
I am not alone in thinking that way. As AI trainer Heather Murray succinctly says about keeping up with AI news: “Don’t even try.”
Those shiny new apps and tools you keep seeing in your news feed? Heather again: “Scratch a little beneath the surface and you’ll find capabilities disappointing and security scarily non-existent. This stuff isn’t ready for use (yet).”
Her advice is my advice: Instead of going down these rabbit holes, spend time building your own AI literacy and working on core skills. Then try applying those skills to real workflows in small, consistent ways. (How to make that fun? Which AI tool to use? Start by playing)
As for all the articles about what the head of a humungous and deeply-in-debt AI company said at an outlandishly named conference? Take it all with a pinch of salt, and read it only if it really interests you. These people are all in it for the money, and don’t really have humanity’s interests at heart.
Tip: If you do want to keep abreast of what’s happening, try subscribing to one or two of the email newsletters I mention at the end of this email.
Issues I will be keeping a close eye on
There are some issues that I will always pay attention to. These are the places where the reality of generative AI (as opposed to the hype) intersects with our lives as human beings. A list, with links to the things I’ve read recently:
How AI intersects with the world of work:
There’s a study just been done that found this: “The gulf between senior executives’ and workers’ actual experience with generative AI is vast… Two-thirds of non-management staffers said they saved less than two hours a week or no time at all with AI. More than 40% of executives, in contrast, said the technology saved them more than eight hours of work a week.”
My first reaction is to say – well, duh! There’s always a gap between what senior executives think and what workers actually experience. But the gap in this case points to something bigger: the potential job losses that ensue when AI can (apparently) do things more cheaply or more efficiently or more quickly than human workers. That’s something we all need to be keeping an eye on
How AI intersects with environmental and climate change concerns
An article on the SmartBrief website theorises that as Gen Z move up in their careers paths and into management, there will be greater social and workplace pressure for the responsible use of AI (because of Gen Z’s collective social consciousness) . Maybe, maybe not – but the article points out that we can all be using AI in environmentally sound ways, and gives these two resources:
Prompt Responsibly – AI at Duke
The TL;DR on those articles: Get your thinking organised before you use a tool like ChatGPT (so you use fewer prompts to get things done) and try to limit your frivolous use of AI (one recipe is enough, you don’t need to get 5 variations). For myself, I am limiting my use of Gen AI in the making of images – they are resource-intensive and I’d rather find good photos by human beings.
The open source use of AI
An MIT Sloan article talks about this from a corporate viewpoint. A new paper found that users largely opt for closed, proprietary AI inference models, namely those from OpenAI, Anthropic, and Google. (Inference is the process that a trained machine learning model uses to draw conclusions from brand-new data, says Cloudflare). Yet open models achieve about 90% of the performance of closed models when they are released, but they can quickly close that gap – and the price of running inference is 87% less on open models.
What does that all mean? Philosophically, using open source models means you are not supporting Big Tech, which I think is an important strand of the debate. Read my previous article on this for more: Gen AI that isn’t big tech – an exploration | Safe Hands
My final “thing to watch”: AI and bias
This is no surprise but research from the Oxford Internet Institute at the University of Oxford, and the University of Kentucky has found that ChatGPT systematically favours wealthier, Western regions in response to questions ranging from “Where are people more beautiful?” to “Which country is safer?”. This sentence in particular leapt out at me: “A world map ranking ‘Where are people smarter?’ places almost all low-income countries, especially Africa, at the bottom.”
Outrageous. But we know this about Gen AI – which is why I list this as something to watch.
My Gen AI recommendation to try in the next week or two
The Reducer: I love this tool made by Nic Haralambous, a custom GPT/Gem that acts as a thinking partner that helps you be ruthless with your own ideas. Haralamous says the tool does two things: It forces you to focus on the outcome of your idea, and it pushes you to move from shipping a feature to unlocking value for your users.
I asked it what it thought of text for a services page on my website because I was worried about the user journey – and got back a much better way of doing things: simpler, more direct. The tool is really useful as a filter for work I have already done, but that I think needs a critical eye. I suspect it would be useful in a range of situations, but particularly when changing brands, or testing new content ideas (or even that idea you have for a new business).
The Reducer as a Custom GPT (the one I used, as recommended by Haralambous)
The Reducer as a Gem (and read my own post on how to build a Gem for yourself, tailored to your needs).
The newsletters I recommend getting
Listed here by the name of the writer(s), with their own descriptions of what you get:
Benedict Evans – What happened in tech that mattered, and what did it mean?
Ethan Mollick – Trying to understand the implications of AI for work, education, and life.
Jeremy Caplan – Wonder Tools helps you discover the most useful sites and apps
Alberto Romero – A blog about AI that’s actually about people
Corey Noles and Grant Harvey (The Neuron) – AI trends, tools, and how-to’s you need to know to stay ahead of the curve (note from me: this one is quite technical but also takes a balanced view of all things Gen AI).
And something that just landed in my inbox which looks like a useful resource:
The 25+ best free AI tools | Zapier – a massive roundup of the best free AI apps so you can put AI to work without adding another subscription to your budget.
Main picture: Hoang M Nguyen, Unsplash
Previous Sensible Guide articles to check out
What about the jobs? Artificial intelligence and social responsibility – a look at artificial intelligence companies and social responsibility.
Gen AI and climate change – what’s the story? – Gen AI and climate change from the ground up…
Overcoming Gen AI’s blind spots: Feminist prompting – In the world of generative AI, knowing what to ask an AI tool is crucial. But so is your thinking process. Enter feminist prompting…
If any of this is relevant to your work…
You might find one of these useful: