It's getting a little wild out there...

Intelligent technologies are here to stay, and our work will never be the same

ChatGPT with Dall-E enabled coughed this up when I told it I study how we’re adapting to intelligent technologies in the world of work - and that I was a huge fan of “Wild, Wild World of Animals” as a PBS kid.

I don’t need to tell you the world of work is changing. We all know it.

ChatGPT, Bing, Anthropic’s Claude, and Google’s Bard (and soon, Google’s Gemini) are free, and available to anyone with an internet connection. Image generation technologies like Midjourney and Dall-E are likewise turned on. And as I type this, OpenAI is rolling out its new multimodal ChatGPT (scroll down to the semi-viral “bike seat” video for maximum wow) - a system capable of receiving, processing, and producing an integrated blend of text, human-like speech, and images. And most of this capability is free, and relies on the chat-based interface that has become intuitive for billions of us, thanks to smartphones.

This is to say nothing of the long list of improving hardware out there - like processors (e.g., GPUs), sensors (e.g., cameras, microphones, accelerometers), actuators, motors, and batteries. In general, hardware is getting dramatically cheaper, better, and more reliable. This, coupled with steady improvements in planning and control software (increasingly related to the above) means we’re making robots - embodied computers that can sense, plan, and then act in the physical world - that can independently handle a far wider range of changing circumstances. For the whiz-bang end of this, take a look at my running list of practical humanoid robots. They’re not ready for primetime, but they’re getting *very* close:

All of this is being put to work… At work.

If you have internet access, you’re on the ride

We are dragging these technologies into the everyday work of millions of people. In some cases, this is in the form of organizations making strategic decisions: for example Price-Waterhouse Coopers (aka PwC), one of the world’s biggest consultancies, recently announced it would invest $1bn to “expand and scale AI capabilities.” But it’s also personal: hundreds of millions of us are going on the web and just trying these technologies out on whatever work problems suit our fancy. This is especially easy with web-connected generative AI because it’s impossible to tell whether someone’s used it if all you get is the output.

Aside from the advent of the internet, this is the only time we’ve lived through the advent of a general purpose technology. By definition, these are tools that are relevant to - and will eventually be insinuated into - most jobs around the world.

If even the conservative version of recent projections are correct, 2.6 billion of us could put generative AI to use in at least 10% of our jobs in ways that give us significant productivity boost. And a series of studies (here’s one recent one of consultants) show that those of us with low skill at at task stand to improve our productivity more than those who have high skill. Given these incentives and the eye-popping stats on usage (180m for ChatGPT alone) it’s a reasonable bet that millions of us are out there, putting genAI to work while organizations figure out how to do so systematically. And none of us quite knows what we’re doing - the clear picture of how to productively integrate general purpose technologies into organizations and systematic work processes only becomes available in hindsight. If the internet’s any lesson, the genAI integration will probably take a decade. Maybe more.

The wildness is about learning

Will jobs get lost as a result of this once-in-a-generation change? Sure. Will some get created? Sure. A big deal for those individuals affected.

I’ll write about that more later, but for now: job gains and losses are a tiny, tiny deal for our species compared to job change.

2.6 billion of us probably need to learn to do our jobs differently, starting more or less right now. Compare that to the hundreds of thousands of us who lose a job each year because of new forms of automation.

This is the Wild World of Work. Where we’re rebuilding the proverbial plane with new tools and techniques as we’re flying it. A generation of people lived through the arrival of electricity, then learned to do their jobs with electricity in the mix. After the shock (sorry, not sorry) and awe, new tools and techniques were developed, and we all had to learn them. Soon after, we figured out telephony. Then the transistor. In each case, a “raw” general purpose technology had to be adapted to be useful for a real problem, and that involved developing specific tools and related techniques, and then everyone - as in billions of us - had to learn to use them. Often the change wasn’t huge, and folks just seem to figure it out. After all, there weren’t really economy-scale retraining programs to accompany each of those general purpose technologies, and we did okay. Why not the same now?

…and we’re breaking the best school we’ve got

If history is any guide, organizations and institutions like schools and universities will figure out what to teach us about intelligent technologies after a decade - if we’re lucky.

So it’s up to us, and we’re relying on the taken-for-granted process of vicarious learning that’s unfolded between experts and novices for thousands of years.

ChatGPT (with Dall-E) suggested this should go before the beginning of this section. That felt a bit premature. What do you think? Anyway, it did get the idea: “This image serves as a metaphor for the transfer of knowledge across generations.”

In the rough-and-tumble of our everyday work, we’ll just try to figure these out. We’ll get a little help from someone who knows a bit more, often in exchange for helping them on something. We’ve known since at least the late 80s that this is how learning on the job happens - it’s informal and just-in-time, given a task to do. And post-books, TV and the internet, this doesn’t need to be with a coworker, with someone physically present, or even a live interaction: thousands of folks already share ChatGPT tips and tricks on this subreddit, for example.

The beauty of this process is that just by working, the novice builds skill, and both parties build a trusting bond. Of course it doesn’t always work, but it’s worked well enough for us to build and transfer skill since before recorded history. This is the best way to explain how we perfected multi-part stone tools like axes and spears. It’s certainly needed to explain the nested expertise to make the bow - our first stored energy weapon - about 160,000 years ago. That’s right about when we invented language. This process runs deep. It’s in the bones of every culture. It’s Normal. Good. Effective. Reliable. In a very real way it’s the bedrock of our civilization.

If you’re here because you already know my work, you know the hidden, troubling punchline that I shared on the Ted stage: we’re putting intelligent technologies to work in ways that hurt this vicarious, informal, on the job learning process. Intelligent technologies (ChatGPT is a great example) allow a single person to be much more “self serve” at work - they can get more done, faster, with less help. We take that deal. Productivity is hard to turn down.

But that means that the next person down the line - a novice trying to build skill and get ahead - is less necessary to get the job done, and doesn’t get as much (or any) chance to learn on the job. They also don’t get to build trust and respect with that expert colleague, which hurts both parties well beyond skill.

This is a massive problem for all of us. We’ll have to learn better, faster than we could before to adapting to work involving intelligent technologies - and we’re hamstringing our go-to method for that learning right when we need it most.

So, really, it’s a wild, wild world of work out there

It’s wild on the surface. Generative AI and advanced robotics inspire awe, fear, curiosity, anger - the whole gamut. And figuring out how to get tasks done with them is at once cool and tough.

But it’s wild down deep, too. We’re taking short-term deals with these technologies and selling our future down the river. On the skills front, we’re hollowing out our professions, occupations, organizations, and careers without quite realizing it.

Now is the time for the opposite. Now is the time to enhance human ability, just as we find good ways to put all this intelligent technologies to use.

We've known for decades that things usually go poorly when we first handle new technologies. Workers end up on the short end of the stick, organizations spend way more money and time trying to figure the technology out, and society misses out on huge opportunities and can pay other dramatic intangible costs.

But it's not always true - somewhere, someone is finding a much better way forward. They're just rare, and their solutions are incomplete. My research shows this is true with intelligent technologies, just as it has been since the bow.

I often refer to my work with this metaphor. Of course, ChatGPT/Dall-E produced photorealistic images of a “needle in a haystack” but this one really caught my fancy - we are not dealing with normal needles OR haystacks these days. And if you look in the right places, positive needles are MUCH easier to find.

I am focused on finding these positive needles in the negative haystack, and testing out which ones could be useful for us all. Beyond publishing papers, it’s now clear to me that I need to share these with the broader world, and to engage the diverse community of concerned researchers, practitioners, policymakers, and workers that have different-but-equal stakes in bending the arc of human ability in a healthier direction.

So here we go: let’s explore this wild world of work. I look forward to learning together.