Almost Timely News, September 3, 2023: The Future of Work in the Age of AI

Almost Timely News: The Future of Work in the Age of AI (2023-09-03) :: View in Browser

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Almost Timely News: The Future of Work in the Age of AI (2023-09-03)

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What’s On My Mind: The Future of Work in the Age of AI

This week, let’s talk about the future of work in the age of AI. It’s a topic that’s been on the minds of lots of folks, from questions about jobs to how AI will impact productivity, to the nature of work itself. To dig into this topic, we need a foundational understanding of the impact large language models and generative AI have.

Why is generative AI such a big deal? We’ve had AI in some form for decades. You’ve been using AI in some form for decades, from maps to get you to a destination on your smartphone to spam filters for your email. AI isn’t new in any way. Many of today’s theories and implementations of AI are based on theories and academic work done as far back as the 1950s.

What’s different about generative AI – in particular large language models – is the language part itself. Language is foundational to our civilization, to our species’ ability to communicate intelligently to each other in a highly compact, highly efficient manner. We understand language not just as words, but as entire concepts wrapped up in little storage containers called words.

Think about it for a second. How much information density is packed into a word we understand? If I say the word sunshine, how much is compressed into that single word? Images, perhaps moving pictures in your mind, a feeling on your skin – there’s so much nestled into the context of the word sunshine that it’s an incredibly efficient way to communicate a whole bunch of concepts at once.

Because language is so information-dense, any kind of system that leverages and uses language well will communicate a lot of information in a very compact format – and that’s what generative AI and large language models do so well.

Take the six word Hemingway poem as an example:

For sale, baby shoes, never worn.

There is so much to unpack in just that single sentence, and that makes language an insanely efficient knowledge compression mechanism. Even in cases when we’re not necessarily specific, language dramatically narrows down the field of information. If someone says “Chris is a jerk” unironically, that may not convey why Chris is a jerk, but it certainly removes a bunch of possibilities for the kind of person Chris might be, just with that single assertion.

Okay, but what does this have to do with AI? Large language models are built with those same inferences, those same probabilistic assertions, and as a result, they use language like we do. That’s the single most important concept to understand. Large language models use language like we do.

They learn language through context, just like we do.

They remember things based on association, just like we do.

They construct words and sentences in a predictive manner, just like we do.

That in turn means we can communicate with them in incredibly information-dense ways (prompts) that create high-density outputs, outputs that convey a lot of information.

So what does this have to do with the future of work? It fundamentally alters the equation of work itself, of the value created by the outputs of work – most of which for office jobs is language in one form or another. Think about how much you use language every single day, in every profession. Even jobs that are highly physical and non-verbal still use language in parts, from workers receiving instructions about what to do to reporting the results of work.

The classic case that highlights this quandary is education itself. Large language models, with good prompting, arguably will write a better term paper on nearly any non-novel topic than any student will. The papers will be coherent, will be grammatically correct, will be well-structured, and generally will accomplish the task of ingesting a lot of information and then summarizing it from a certain point of view.

Many schools and colleges have attempted to forbid the use of generative AI in schoolwork as a result. And this speaks to how fluent and capable the technology is; if the technology were as bad as critics claimed, then there would be no need to ban it.

So the question is, what is the value of the task of writing a term paper? By extension, what is the value of the task of writing a blog post, a whitepaper, an email, some marketing collateral, a call center script, an investors report… the list goes on of things that are language, that we use language to communicate, and that machines could arguably do better.

What is the value of work?

Think about this carefully. Before the internet, we had to remember things. Once search engines came along, we didn’t have to remember nearly as much because we could use a search engine to find the information we needed, at the time we needed it. Did that make us dumber? Less capable? Poorer workers? Of course not. It made us smarter, more capable, and better workers because we could accomplish the same tasks but faster and better.

Before smartphones, we had to work in prescribed locations, either in the convenience of an office or by lugging around a large piece of technology like a laptop computer to get work done. With smartphones and wireless networks, we can do more from wherever we are. Does that make us less skilled workers, less productive workers? Of course not. That would be a ridiculous assertion. Mobility enabled us to be far more productive workers.

In both technology examples, we are still generating the outputs of work – language, in many cases – but we are enabled to do so faster, better, and cheaper by giving us capabilities we did not have. And this is the key to understanding the role of AI in every scenario. Each wave of technology has brought us closer to the work, faster at the work. But we were still doing the work. AI abstracts that away at a much greater level because now it’s doing a chunk of the work. It’s doing the summary, the extract, the first draft, and we’re polishing it to ensure it meets our standards.

Schools that ban the use of AI are like schools that ban the use of smartphones. They’re doing their students an incredible disservice by handicapping them, by forcing them to learn to work in less efficient, less effective ways, and when those students – particularly higher education students – enter the workforce, they will be behind their peers who have had years of practice with the best tools available.

Imagine you went to culinary school and your instructors forbade you the use of any electrical appliances. You had to do everything by hand – all the chopping, slicing, etc. You enter the workforce and while you conceptually know what a blender is and what it does, you’re not skilled with its use. You are inherently less employable than someone with the same time in education but more skilled with the tools of the trade.

AI is a tool of the trade for every profession. That’s the crux of the issue. Generative AI and large language models are premier tools of the trade for every profession that uses language – which is pretty much every profession. I can’t think of a single profession where no one communicates with language.

But that still doesn’t answer the question about what the value of work is, does it? If a machine can write a term paper or a blog post, and do a better job than we can, what is the value of work? The answer is that our value is in the asking. The machines can produce the answers, but they produce answers commensurate with the skillfulness of the question. If I prompt, “write a blog post outline about B2B marketing”, that’s not a particularly skillful prompt. The answer, the output will not be particularly skillful either.

If I prompt, “You are an expert B2B marketer. You know lead generation, demand generation, scalability, marketing, market share, customer acquisition, customer retention. Your first task is to write a blog post about B2B marketing. The post should focus on the evolution of B2B marketing from analog to digital, from transaction to experiential, and from selling to helping. Be sure to cross reference key societal changes such as the Internet, the smartphone, and the dawn of generative AI and their influences on B2B marketing. Be sure to prescribe solutions for B2B marketers to remain effective in an era of constant change and deep uncertainty. Be sure to focus on lead acquisition as a key outcome in B2B marketing. Write in a professional, warm tone of voice. Avoid business jargon. Avoid business cliches and tropes. Avoid adverbs and passive voice. Write the post outline.”

That is a much more skillful prompt. It’s a better question, and the answer the machine generates will inevitably be better. Try out both to see what the results are.

Better questions lead to better answers. Better prompts lead to better outputs. Better ideas create better realities. That is the value of work, and that is the value we provide. A term paper that is just a regurgitation of existing information teaches very little except rote memorization. A machine can and should write that paper. But a term paper assignment that asks for deep synthesis, for novel thought, for making difficult or imperceptible connections is going to be a much more interesting read, whether written by human or machine.

The people who are fighting AI on the grounds that it can’t be original or creative fundamentally misunderstand that AI is as creative as the person operating it. The institutions who want to prevent its usage – schools, workplaces, governments – also fundamentally misunderstand the role of AI in work is to replicate and extend our capabilities with language. Those who embrace the technology will dramatically outperform those who don’t, in the same way that those who embraced automobiles dramatically outperformed those still riding horses.

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ICYMI: In Case You Missed it

Besides the newly-refreshed Google Analytics 4 course I’m relentlessly promoting (sorry not sorry), I recommend the episode we did on generative AI and gender biases. It was absolutely stunning just how the biases show up in side-by-side tests. It’s worth a watch.

Skill Up With Classes

These are just a few of the classes I have available over at the Trust Insights website that you can take.



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Get Back to Work

Folks who post jobs in the free Analytics for Marketers Slack community may have those jobs shared here, too. If you’re looking for work, check out these recent open positions, and check out the Slack group for the comprehensive list.

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.

Social Media Marketing

Media and Content

SEO, Google, and Paid Media

Advertisement: Business Cameos

If you’re familiar with the Cameo system – where people hire well-known folks for short video clips – then you’ll totally get Thinkers One. Created by my friend Mitch Joel, Thinkers One lets you connect with the biggest thinkers for short videos on topics you care about. I’ve got a whole slew of Thinkers One Cameo-style topics for video clips you can use at internal company meetings, events, or even just for yourself. Want me to tell your boss that you need to be paying attention to generative AI right now?

πŸ“Ί Pop on by my Thinkers One page today and grab a video now.

Tools, Machine Learning, and AI

Analytics, Stats, and Data Science

All Things IBM

Dealer’s Choice : Random Stuff

How to Stay in Touch

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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:

  • ISBM, Chicago, September 2023
  • Content Marketing World, DC, September 2023
  • Marketing Analytics and Data Science, DC, September 2023
  • Content Jam, Chicago, October 2023
  • MarketingProfs B2B Forum, Boston, October 2023
  • Social Media Marketing World, San Diego, February 2024

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.

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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