AI is a now a significant contributor to the broader trend of enshittification. The market is full of AI-powered products, features and code that are costlier to use and run and often counter productive for the end users.

Most executives are reaching for ways to become an AI-first organisation, but many of them are missing the productivity power up that’s hidden in plain sight – asynchronous collaboration. Not only does asynchronous collaboration free up unproductive time for workers, it helps create the digital exhaust of knowledge that LLMs can feed on.

Taste and discernment are more important than ever, in the age of AI.

The unpredictable nature of generative AI can often feel as addictive as gambling. Addictive services often get enshittified, and can also be candidates for a classic bait-and-switch.

As the circular funding economy of AI becomes more transparent, innovation slows and costs increase, it may help us all to temper our AI enthusiasm. There are many indications that the current state of generative AI is neither essential nor as transformative as the frontier AI companies will have us believe. If anything we could make the case that generative AI is a net negative for most stakeholders.

Modern workplaces increasingly reward visibility (“restaurateur” roles) over deep work (“chef” roles), especially during turbulent times. If AI-driven disruption and organisational thrashing favour those who are present and highly visible, what should deep workers do?

The larger the company, the harder it is for them to express dissent, especially under authoritarian regimes. If employees expect their C-suite to be their protest comrades, they’re likely to be disappointed. Our employers cannot be vehicles for our activism.

I’m “AI bipolar” - an enthusiast and a sceptic. Here are some of the reasons I blow hot and cold with AI.

Enterprise AI pilots have a 95% rate of failure. Could it be that people have experiment fatigue and that companies are taking a piecemeal approach to AI innovation?

AI has many productivity benefits, but those benefits may only accrue towards corporations who now have new tools to exploit workers.

AI is coming for many jobs. Bullshit jobs will be the first to go. Those of us with a real job must cultivate a few characteristics to be AI-first in the way we work and stay relevant in the job market.

We often derive satisfaction from difficult activities, even if they aren’t pleasurable. Does this effort paradox hold any learning for us all?

In teams, no one is, or should be, indispensable.

Before companies seize productivity advantages through agentic AI, they’ll have to contend with a messy period of agent sprawl, poor design choices and inefficient workflows. When the dust settles, success won’t just be about the ease of agent building. The companies that succeed will most likely prioritise thoughtful design and problem solving, and find clever ways to balance bottom-up innovation with lightweight governance.

Don’t give into AI FOMO. Board the train at your preferred station.

AI transformation is more about behaviour change than technological change. Job insecurity, misaligned incentives, incompatible work environments and fragmented work systems can be impediments to this new way of working.

AI is rarely perfect or production-ready. Working with AI means embracing a perpetual beta mindset. And perpetual betas always need humans in the loop.

The days of manually curating and organising company knowledge will soon be behind us. Knowledge managers can’t operate as librarians anymore. They must, instead, elevate their AI literacy, implement an AI-first KM stack and deploy themselves as consultants to implement knowledge-enabled workflows.

The Chinnaswamy stampede highlights the daily risks the Indian salaried class faces. While there is no silver bullet solution to these problems, remote work can provide Indian knowledge workers some relief from these stresses.

It takes a special discipline to push back against novelty and embrace routine, boring solutions. That discipline, however, can lead to surprising efficiency.