Christopher S. Penn – Marketing AI Keynote Speaker

Almost Timely News: The Future of AI and Regular Life (2022-10-23)

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Christopher S Penn
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Almost Timely News

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Almost Timely News: The Future of AI and Regular Life (2022-10-23)
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What’s On My Mind:

Content warning/trigger warning: this newsletter isn’t going to be positive, cheerful, or uplifting. If you’re already stressed, I urge you to skip this issue. Go read the Trust Insights newsletter issue on self care or listen to Malinda’s new song or something besides read this. We’re going to be talking AI, economics, inevitably a bit of politics, and social issues.

🧐

Ok, now that everyone else is gone, let’s get started. This past week, a colleague asked me what I thought the future of AI is in the next few years, where I thought things like content generation are going.

First, on the optimistic side, the same technologies that power the ability for an AI model to take a text prompt like “dog wearing a pink tutu riding on a skateboard” and transforming that into art are capable of so, so much more. Without getting into the guts of these kinds of models, the underlying technology is agnostic as to the kind of content it’s working with.

Thus, with enough data, it’s equally possible for a prompt like this to eventually work: “make a short animated film about a penguin with poor eyesight.”

But again, that’s not thinking big enough. Content type is irrelevant to the underlying technology. This is also realistically possible:

“Here is a new virus’ RNA we haven’t seen before, create a candidate vaccine.”

Or,

“Here is the genetic code of an individual with a rare disease. Create 5 candidate drugs to treat it.”

Or,

“Create a genetically modified algae that consumes atmospheric CO2 at twice the rate of naturally occurring organisms and dies quickly.”

This is the upside of the latest generation of AI models. If we use them properly, we could engineer civilization-changing solutions for the better. What’s required are massive amounts of data to train on, domain expertise wedded to AI engineering skills, and a focus on desirable outcomes counterbalanced by an understanding of what could go wrong.

That’s the upside, and if we do it right, it’s a pretty big upside.

So let’s talk about the downside. You might want a beverage in hand.

The current generation of AI models and the immediate next generation, absent regulation and government interference, are going to cost millions of jobs. Yep. It’s going to be like that. There’s no gentle way to put it.

Here’s why. No AI software right now can do an entire job, because jobs are composed of a myriad of tasks. But some of those tasks are substantial investments of time, and individual tasks absolutely can be done by machines. For example, writing a blog post can be done by a machine with reasonable quality. Certainly, machines can create excellent first drafts.

Now suppose you have a team of 10 writers, a content creation team. Writing blog posts occupies about 40% of the team’s time in aggregate. If machines can create capable first drafts that only require minor editing, then suddenly your team has about 30-40% excess capacity.

What happens next? Well, one of two things. Either you find other tasks to fill that empty 40% capacity, or you can safely downsize the team by 40%. Instead of a team of 10, you can get the same amount of work done by a team of 6.

But not everyone writes blog posts, so most of us are safe, right? No. Anything that’s repetitively creative, machines can create okayish to good versions of. Machine-led art has already won art contests (and forced contests to specify that future entries must be human-led or human-only submissions).

So why do I think this will lead to millions of lost jobs? First, because the kinds of jobs that AI will impact are becoming far more numerous. Chunks of data science – my profession – are becoming more and more automated. Now creative jobs are on the line in every part of creativity – writing, photos, art, music composition. We’ve already seen the impacts of automation in manufacturing jobs.

And while it’s true that new jobs will be created, the scale factor isn’t in humanity’s favor. For example, I could probably paint one very mediocre painting in about a day. A tool like Stable Diffusion? I can write the necessary code and provide mechanistic prompts for it to generate 3,000 – 4,000 pieces of artwork overnight. Yes, someone like me in the role of a prompt engineer is still needed to operate the machinery, but I’ve replaced the raw output of 3,000 mediocre human artists (me) with 1 machine instance that can achieve the same levels of productivity.

Add to that the level of competence for machine-generated content continues to rise. Five years ago, machines could only work in templates, a bit like boring mad-libs. Today, they create coherent, readable text as well as amazing artwork, passable musical compositions, and other forms of creative output. Every year, the bar of competence rises higher for what machines can do versus humans.

This brings us to the second major point: our current economic systems in many different nations tend to reward efficiency and our main measure of success in free market economies is net profits.

If you, as a margin-minded executive or stakeholder, hear about 40% inefficiencies in your organization, what are you going to do? Wait the 6, 9, 12 months or more for people to reskill and upskill? Or are you going to make cuts to improve those margins and right-size the business? If you report to Wall Street or other similar investor mechanisms, you are being asked to optimize for net profits before the next quarterly earnings call.

Any publicly traded company is going to choose the latter for sure; most for-profit companies will choose the latter. It’s the rational choice if your goal is to maximize net profits. Why? People are expensive. Above and beyond the salary, you have other aspects of people – compensation in the form of benefits, healthcare, payroll taxes, etc. Obviously this varies from region to region, but there are no civil places where people are a most cost-effective option than machines for the same task. The only places where people are cheaper than machines are places where massive human rights violations are occurring.

Here’s what we’ve seen over the last two centuries as a general rule: once a task becomes the domain of machines at scale, it never goes back to being a human task at scale. No farm that produces at scale plows with a farmer and a mule. Instead, GPS-guided massive equipment does that, and the farmer is more a machine supervisor – and the hundreds of field workers that task might have employed in the past are no longer needed. No mass-manufactured automobile is assembled by hand; assembly lines today are more automated than ever. These industries are safer and more productive, but they employ far fewer people – and the same thing will happen to every task and industry AI touches.

Who will be affected first? Any job that’s made of largely repetitive tasks that AI can perform, for which there would be substantial cost savings – especially if your level of skill is below that of a machine’s. If a machine can generate 1,000 pay per click ads an hour and you can create 2, unless your 2 ads are brilliant, a machine is going to be doing that task very shortly – and you might not be.

What jobs will be affected least? Jobs where the tasks are so complex that it’s not worth automating them because the complexity is too high. Cleaning a hotel room has enough weird variability (hey, who glued the furniture to the ceiling?) that humans are better at it and will be for a while. Yes, you could teach an automaton like Boston Dynamics’ Atlas robot to do the job – but those are a quarter million dollars just for the hardware, not to mention the time to train it and their 20 minute runtime currently.

Jobs where human to human relationships are primary, like massage therapy, are jobs that customers probably will not want machines doing even if the machines are cheaper.

What should you be doing to protect yourself? First, if you are in a profession where your tasks are highly repetitive and creative in nature, like art, design, writing, etc. you should be constantly upskilling, constantly improving your craft to the point where machines struggle to match your unique style.

Second, your personal brand – your reputation and how you market yourself – must be a priority. Being known for something, being good at something, being distinct in your field will offer an added layer of protection that the average worker will not have. When people ask for you by name, you’ve got a strong personal brand. And that doesn’t have to be one kind of profession. We’ve all been to restaurants or coffee shops where there’s that one person we’d prefer to deal with – that person has built a personal brand that others value.

Third, on a societal level, every free market society needs to figure out safety mechanisms like universal basic income really, really soon. Like climate change, it’s got to be a priority now before it becomes an intractable problem later. Having thousands or millions of newly unemployed people in the workforce while a few leaders of business keep getting richer is a recipe for social unrest at the very least, and outright civil war at the worst. I’d give us a decade at the most to figure out UBI or some similar safety mechanism to allow people to live on the basics. Without that, it’s not going to be a pretty future.

AI is an incredibly powerful set of tools. Like all tools, it’s agnostic and amoral. In the right hands, we will do great things with it. In the wrong hands, we will do incredible harm with it. And given the penchants and propensities of the people we tend to elect to office (in many nations) and the people we elevate in public society like business oligarchs (again, in many nations), the latter outcome is probably more likely than the former.

What do you think? Am I being too pessimistic? Am I missing the boat on something obvious? Hit reply and tell me why.

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I do actually read the replies.

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ICYMI: In Case You Missed it

Besides the new Google Analytics 4 course I’m relentlessly promoting (sorry not sorry), I would recommend the piece on conference swag and what it says about your brand.

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What I’m Reading: Your Stuff

Let’s look at the most interesting content from around the web on topics you care about, some of which you might have even written.

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Advertisement: Google Analytics 4 for Marketers

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Dealer’s Choice : Random Stuff

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Events I’ll Be At

Here’s where I’m speaking and attending. Say hi if you’re at an event also:

  • Heapcon, November 2022, Belgrade, Serbia
  • SMPS, November 2022, Las Vegas

Events marked with a physical location may become virtual if conditions and safety warrant it.

If you’re an event organizer, let me help your event shine. Visit my speaking page for more details.

Can’t be at an event? Stop by my private Slack group instead, Analytics for Marketers.

How to Stay in Touch

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Required Disclosures

Events with links have purchased sponsorships in this newsletter and as a result, I receive direct financial compensation for promoting them.

Advertisements in this newsletter have paid to be promoted, and as a result, I receive direct financial compensation for promoting them.

My company, Trust Insights, maintains business partnerships with companies including, but not limited to, IBM, Cisco Systems, Amazon, Talkwalker, MarketingProfs, MarketMuse, Agorapulse, Hubspot, Informa, Demandbase, The Marketing AI Institute, and others. While links shared from partners are not explicit endorsements, nor do they directly financially benefit Trust Insights, a commercial relationship exists for which Trust Insights may receive indirect financial benefit, and thus I may receive indirect financial benefit from them as well.

Thank You!

Thanks for subscribing and reading this far. I appreciate it. As always, thank you for your support, your attention, and your kindness.

See you next week,

Christopher S. Penn

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