In our world of black and white, it’s difficult to be a tech skeptic without being labeled a Luddite.  The Holy Grail is progress, so the thinking goes, and any pesky question asking threatens to stifle innovation.  Do you want us to lose to the Chinese!?!?

It’s ok, I’ve been doing this a long time.  Not so long ago, my nickname among CNBC bookers was “Big Data Hater.”  You remember the age of Big Data, don’t you?  If you don’t, it was yet another marketing moniker that took over the tech world for a few years, stoking stock valuations everywhere it went. A mini dot-com boom, if you will.  Big Data, unfortunately, often became synonymous with Bad Data, which always gives bad results, no matter how much data you shove into the GIGO machine. Today, we call these Large Language Models, which sound much more sophisticated, but suffer equally from the same garbage problem.

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This is part 2 of a three-part miniseries on Artificial Intelligence.
Read part 1: Disarm AI, yes, but the Pope was just getting started 

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Back then, I would protest with a glint in my eye — how can someone hate data? That’s like hating atoms! Some of my best friends are data!

I don’t hate data. Or tech.  What I hate is thoughtless “progress” without discernment about side effects and collateral damage. And I really hate when the progress … is promised, down the road — soon! — while the roadkill piles up today.  What’s the roadkill of this never-ending tech bubble cycle? Pension funds that are crushed when the bubble bursts. Workers who are laid off in the name of cost savings needed to offset investments.  Kids who end up with addiction machines in their pockets because there’s no other legitimate business model for social media. Adults who’ve sacrificed every shred of human privacy so they can be stalked by ads for items they purchased last week. And so on.

Yes, the consequences are real, and they are here — even if the innovations are…just around the corner.

I’m not arguing that AI isn’t real. Already, it’s freed an entire generation from writing trite, jibberish-laden emails back and forth at work.  AI can turn meetings that should have been an email into a summary of said email.  That could be real progress — but let me know when those meetings are actually canceled.

Can AI do a great job of writing a meeting summary for people who weren’t really paying attention anyway? Yes, absolutely. Can it pull out that one critical moment in the meeting which most attendees missed…which might very well be what was left unsaid?  Ha! (You’ll read about this in part three of this miniseries)

AI is great at writing code, getting rid of some of the grunt work of the digital age.  It helps people with blank page syndrome get a start on papers and presentations.  And it’ll do a fine job of summarizing large amounts of material for people in a hurry. A great application I read about recently involved practicing physicians who have scant time to read all the latest medical research. It can do these things today.

As for tomorrow — there seems good reason to believe AI will be great at finding needles in research haystacks, which could very well lead to amazing medical advances.  I will be the first to cheer on this work. I’m sure I’ll need it someday.

But tech titans have a decades-long pattern of racing forward with innovations, intermediate consequences be damned. Of doing things simply because we can, not because we should — in fact, not even asking if we should.  And, specific to my main work right now, of creating tools that are easy to abuse and darn near impossible to stop.

I am not a Luddite. I think tech does more good than bad. But I think in a playoff series, “good” wins in the 7th game, and probably in overtime.  It’s often a close call.  We can’t ignore the bad things that AI will do because it might slow progress a smidge. The best thing we can do is air every single one of these side effects and work to eliminate them. That’s how penetration testing has always been done. That’s the ethos of open source software. More than ever, we need to approach the coming age of AI that way.

That’s why I was so happy to learn recently about the Artificial Intelligence Incidents Database. It is what it sounds like — a list of mishaps caused by, or enabled by, AI.  I recently interviewed one of its leaders, Harvard fellow  Sean McGregor, for The Perfect Scam, a podcast I host for AARP.  McGregor is the kind of plain-speaking genius we desperately need right now.  We talked for an episode about a family who was targeted by an AI-generated photo of the family dog depicting him on an operating table, riddled with injuries from a car accident. (That was incident 1,478 in the incident database). Naturally, our conversation covered far more.

McGregor made this point: Early on, the database was full of (funny?) incidents about AI failing to work properly. But increasingly, the database is loading up on tales of fraud committed by criminals using AI.  That might be the bigger problem, he suggests — the so-called dual use problem — as AI gets better at what it does, it gets better for the bad guys.  I left our chat thinking my sarcasm about AI’s clumsy failures might very well be misplaced.

Whatever you do, don’t call someone a Luddite because they’re worried about the future. We do get to decide what kind of future we want; we don’t have to just accept what Elon Musk gives us. In fact, I’d argue, that’s a poor choice.

Tristan Harris from the Center for Humane Technology appeared on CNN this week and made a very sharp point about incentives.  In the end, AI is going to become whatever the incentives nudge it to become. Right now, the only incentive on the table is shareholder value. That means AI will principally be used to eliminate labor costs.  The End.  But we have the chance to design other incentives right now. To reduce human suffering. To build more housing.  To make mass transit far more efficient. Heck, to enable human happiness.  Whoever told you that our society’s only goal is profit sold you a very shallow future. We can, we must, do better. An honest, real-time look at AI’s failings is going to be a big part of that.