Intro

When reports like Adecco’s Global Workforce of the Future survey find that the average saving for workers using AI is 1 hour a day, we should question this.

* What did those workers do with their time savings?

* Should that time savings benefit the employer or the employee?

* Can we trust such a hard-to-measure stat?

Our latest episode tackles this and other disruptions happening to the creative and production processes.

Matthew Krissel is the Co-Founder of the Built Environment Futures Council and a Principal at Perkins&Will. For over two decades, he has led transformative architectural projects across North America and internationally.

We discussed how AI is disrupting architecture and lessons for digital product teams. He really struck powerful points many times during our conversation about questioning the role of time and permanence in a world when we want more, faster.

Other points covered in the conversation:

* Commoditizing design makes production easier, enabling societies to tackle challenges like housing shortfalls

* Commoditizing design devalues other vital processes, like community engagement, respectful place-making, and longevity of projects

* Over-indexing AI’s potential as a workflow optimizer, while under-indexing the potential to reimagine how complex projects are planned and operationalized

Listen on Spotify | Listen on Apple Podcasts

In this newsletter, I’d like to tackle the concept of time saving and what it means from the perspective of crafting an AI strategy.

Here was the most important quote from the episode:

 So just because something took half the time it did before, what happened is we just did more. So we just filled the time.

Is there something higher and better use? I suspect that somewhere along the line the designs got better. Also I suspect that somewhere along there was diminishing returns. We were just doing more because we could not that it was actually yielding anything better.

 Are you gonna focus on fewer, but better increase your quality? Are you going to spend more time on business development or some entrepreneurial side hustle? Just go home early? What you decide to do as we start to gain productivity time is going to shape a lot of where this is all happening.

Newsletter recommendation: Scott Belsky

Essential insights and lessons from Scott Belsky that anyone building with AI must read. His newsletter is fantastic and a must-subscribe because of his unique cross-section of expertise across creativity, product, and innovation.

His books have also always been pivotal reads to advance your craft. Hopefully, we can do some of the same with our Design of AI podcast and newsletter.

Who should benefit most from your ability to learn AI: You or your employer?

The challenge to creatives and builders is to decide who should benefit from these transformative technologies if you’re self-taught:

* Should you gift your employer the benefits if you’ve taught yourself ways of getting 25% more work accomplished in a day?

* Should you gift yourself the benefits of your increased productivity and work on side projects, or spend more time with your family?

Historically speaking, employers were responsible for the means and training of production. They paid for novel technologies —desktops, SaaS, big data— and were responsible for training you on how to use them.

AI is different because employers are often lagging behind employees in embracing and educating on how to use the technology effectively. It is very easy to argue that the 200 hours you’ve spent learning AI outside of work hours should exclusively benefit you.

AI Time Savings: Benefits & Risks

Technologies have consistently saved us time, but the resulting effects have been questionable. The internet and mobile phones connected the world, while also leading to increased poor health outcomes due to more time sitting. We also spend more time alone than ever.

Further back, the Industrial Revolution raised the quality of life for everyone. Still, the commoditization of work led to industrialists exploiting child labour and putting everyone into deplorable working conditions that polluted communities. The time the workforce saved most benefited employers, with employees giving up their ways of life in favour of steady incomes. Most relocated to cities, got cut off from their families, and learned the pain of commuting for the first time.

When it comes to AI, the benefits we hope for centre on automation and augmentation. The hope is that we will benefit from less shitty work (automated away) and that we can our new capabilities (augmented by AI) will enable us all become wealthy entrepreneurs.

Sure, this may be true for the top 0.01% of AI users who learn how to run a typically 10-person business by themselves. For the rest of us our work may in fact get a lot shittier. At least that’s what the authors of the upcoming book, The AI Con, believe.

The authors (and upcoming Design of AI guests), Alex Hanna and Emily M. Bender tell a tale of how AI’s risks have been severely hidden under the rug. In their book, they document many examples of the technology performing so poorly at tasks that products were shut down within weeks.

Maybe the future of businesses will look a lot like Amazon: A business offering endless products of questionable quality and provenance with no humans in sight except those working the worst possible jobs in sorting information, like something out of Severance.

In this scenario, the majority of humans will be employed as mall cops of the technology, swooping in when a problem happens that slips between the programming and policies.

At this point, AI hypers would argue that even if the enshittification of work is inevitable, AI will open up new and better types of jobs. Only time will tell.

How does AI change our relationship with time?

When buying productivity-boosting hardware and software, the expectation has always been that the results are undeniable. Going from handwriting to using a typewriter was immensely faster. The same is true when buying a new Saas platform that makes managing projects infinitely easier.

Now, with GenAI-powered products, the ROI is unpredictable. The vast majority of capabilities deliver the illusion of rapid progress.

Think of image and video generation —the immediate results are shockingly impressive. But getting results to be production-ready requires mastery of probabilistic software and/or resetting your expectations. It all means that the operator —you— ultimately plays a bigger role in the ROI of using this technology than with previous ones.

So-called Vibe coding is a major testament to the time savings that AI can create. Anyone can now build a website and app without writing a line of code.

Vibe coding platforms —like Cursor, Lovable, Replit, and many more— are fantastically easy to use… until they’re absolutely painful to use. The stunning early rewards turn into confusingly broken components all over.

Again, results depend on the operator’s ability to debug using an entirely new interface paradigm (conversational). This continues the technology’s remarkable inversion of the value paradigm, where workers define the quality of outputs.

Looking ahead, mastery of data will triumph over mastery of interfaces. This favours employers who unlock the power of their first-party data and build solutions that augment and automate the expertise of their employees.

Always worth reading, strategist and tech critic Tom Goodwin posted an intriguing analysis on LinkedIn this week. At the core of the guiding philosophy regarding AI-assistance is that the more complex the task, the less qualified AI is to work on the task unassisted.

Check our previous podcast episode and newsletter for more details on how to unlock the power of your data.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit designofai.substack.com