The AI advice nobody's giving you: do less, not more.

Hello,

Hi, I'm Simon, and this is Plain AI, your weekly look at what's actually happening with AI at work, minus the hype. Each week I cover the stories that matter and what's coming up.

One of the things that keeps me interested in the world of AI is the rapid development, and the stories we see unfolding in a matter of weeks rather than played out over years. However, the flip side to this is that at times it can all feel totally overwhelming. From stories about AI models escaping their cages and running around the internet, to a constant barrage of new AI tools being released, it can feel impossible to understand what's happening and keep up. We also have our own employers pushing us to use it and drive efficiencies in our work, but at times with very little actual guidance or education. Amongst this backdrop, it would be very understandable to simply bury your head in the sand and hope it all goes away, so that we can revert to writing a document just with our own thoughts and skills. However, I hate to break it to you, but the progression of AI isn't going anywhere, and whilst we might hope to ignore it at times, there's a very real risk that we put ourselves in the slow lane and see our careers stall — and I'm sure none of us want that.

In this scenario, I have a recommendation, and that's to use AI, but keep things simple. For most of us, we don't really need to concern ourselves about AI systems breaking free, nor do we need to keep up with all of the latest scientific advances in each new model. Instead, I'd just take a step back and understand what tools you have available at work, then make a short list of how they can help you in your day-to-day role. Find two or three tasks where AI can help you — perhaps summarising long documents, or pulling out action items from a call transcript. Understand those few use cases and become an expert in them; finesse your process so that it's second nature. Also take a moment to understand the time it saves you, note down any insights you might not have realised before using AI, and generally understand the value it brings you. If it helps, create a document for yourself and write all of this down. This serves two purposes: firstly, it helps you understand what AI tools you're using and how they help you; and secondly, it gives you something to share with your management if they ask how you're using AI to be more efficient at work. Unfortunately, this is something I hear being asked of people more and more, typically because senior leadership teams are under scrutiny about running efficient teams and making cost savings.

I'll finish with this: don't try to learn everything about AI, but educate yourself on a few aspects where it helps you personally, and become an expert in that.

Everyone’s hunting for the next Unicorn.

The type of “category disruptor” that grows fast and turns early believers into big winners.

59,000+ investors think that Mode Mobile could be one of those rare finds.

Americans spend 4 ½ hours on their phones daily, and Mode Mobile is monetizing that screentime. With $1B+ earned by over 490M customers and 32,481% revenue growth, Mode’s EarnPhone is turning smartphones into income generating assets.

Their previous raises sold out, and the company is now offering pre-IPO shares at $0.52/share with up to 20% bonus, exclusive to early investors.

Being early is everything, and this window is still open.

>> Review the offer before it closes

^{*Please read the offering circular and related risks at invest.modemobile.com.}

^{Mode Mobile recently received their ticker reservation with Nasdaq ($MODE), indicating an intent to IPO in the next 24 months. An intent to IPO is no guarantee that an actual IPO will occur.}

^{The Deloitte rankings are based on submitted applications and public company database research, with winners selected based on their fiscal-year revenue growth percentage over a three-year period.}

A new Global Talent Barometer survey found that regular AI usage among workers jumped 13 percentage points to reach 45% of the workforce — but in the same period, confidence in using workplace technology fell by 18%. The report, cited by Barchart, also identifies a rising "job hugging" phenomenon: 43% of workers say they fear automation will replace their role within two years, making them less likely to flag inefficiencies or suggest process changes that could reduce their own headcount.

Why it matters: High AI adoption rates are masking a quieter problem: workers are going through the motions with AI tools without trusting them, which limits genuine productivity gains. For managers trying to embed AI into their teams, this is a warning that deployment and cultural change are not the same thing.

A wave of new research is crystallising around what analysts are calling the "AI productivity paradox." Despite 91% of businesses now reporting some AI use, more than 80% say they have seen no measurable bottom-line impact, according to data compiled by Value Add VC and corroborated by an Atlanta Federal Reserve working paper. A separate CEO survey found 56% say they have gotten "nothing out of" AI investments, and only 12% report AI has both grown revenues and reduced costs simultaneously. Individual workers report genuine time savings — averaging 5.4% of weekly hours — but those gains are not aggregating into firm-level results.

Why it matters: This is the question CFOs will be asking in every boardroom this autumn: where did the ROI go? The gap between task-level gains and enterprise-level impact points to an integration and change management problem, not a technology problem. Organisations that can't answer the "so what?" for AI investment are heading into budget season in a difficult position.

The global industrial sector is undergoing a rapid AI-driven consolidation. Schneider Electric agreed to acquire Norwegian industrial AI platform Cognite for $3.1 billion, while Emerson completed a $7.2 billion takeover of AspenTech — both deals targeting AI capabilities for factories, energy systems, and supply chains. According to Forbes, CB Insights tracked 266 AI M&A deals in Q1 2026 alone, a 90% year-on-year increase. The thesis behind the deals is consistent: industrial incumbents cannot afford to depend on third-party AI platforms when competitive advantage is shifting to whoever controls the data layer.

Why it matters: The AI land grab is no longer just a software industry story. Heavy industry is making multi-billion-dollar bets that the companies which control industrial AI data infrastructure will dictate the economics of manufacturing for the next decade.

Thanks for reading, and see you next Thursday.

Simon,

Was this email shared with you? If so subscribe here to get your own edition every Thursday.

Enjoying Plain AI? Share it and get a free resource!

If you find this newsletter useful, why not share it with a couple of friends or colleagues who would also benefit? As a special thank you, when two people subscribe using your unique referral link, I’ll send you my "Exclusive Guide: Supercharge Your Work with NotebookLM." It’s a practical, no-nonsense guide to help you turn information overload into your secret weapon.

You can also follow me on social media: