Around a year ago, I began a quiet pivot into artificial intelligence.

Granted, AI has featured in my work as a futurist, speaker, and digital transformation specialist for quite a while. But when OpenAI released ChatGPT in November 2022, I recognized the start of something big — a major shift akin to the advent of the worldwide web or the emergence of social media; two among a string of technology-driven transformations I’ve had the opportunity to guide leaders through over the years.

I dove in. I earned a graduate-level certificate in AI strategy from Cornell University’s SC Johnson College of Business. And another in AI ethics from the London School of Economics and Political Science. Fellow marketing veteran Geoff Livingston and I launched our podcast — No Brainer: An AI Podcast for Marketers — which has gone on to be ranked as one of the top marketing AI podcasts on the web. AI started to feature even more heavily in my keynotes. And I quietly launched an AI consulting practice, CognitivePath, through which I’ve been partnering with the Association of National Advertisers (ANA) to create original reports about marketing AI and delivering hands-on AI workshops for enterprise clients.

Today, I’m excited to announce the next step in my AI journey.

Geoff Livingston (who’s been consulting in AI through his own firm, Generative Buzz) and I are merging our practices to form a new, more focused CognitivePath. The new company — officially CognitivePath Research, Inc. — is the first and only research and advisory firm (aka industry analyst firm) squarely focused on the intersection between marketing and artificial intelligence.

Through our independent work over the past year, Geoff and I both recognized that marketing decision-makers are hungry for the kind of independent and objective guidance only an analyst firm can offer — unencumbered by the pressure to sell-in strategy, technology, implementation, management, or agency-style execution.

Smart marketing decisions (and actions) require CMOs and their teams to cut through the hype, get to the truth, and understand what rapidfire developments in the world of AI really mean to them, their business, their brands, and their customers. The big analyst firms are too broad. The AI pundits, too biased.

That’s where we come in — 100% focused on AI, evaluated entirely through a marketing lens, with a commitment to true objectivity.

We’re excited to launch with three core services — a robust advisory membership, practical hands-on workshops, and original research. And a few early clients, including an ongoing relationship with the ANA.

Our new Substack newsletter — fittingly, The CognitivePath — compiles our AI-related thoughtleadership, dating back to 2016 — and will be the best place to stay on top of our perspectives. A free subscription will put new posts in your email and grant you access to the entire archive. A paid subscription will unlock timely, ongoing, clients-only analysis of key developments in marketing AI — our first paid research offering and a fantastic low-cost point of entry for marketers looking to take the next step on their own path toward AI maturity.

Hear it straight from Geoff and me in a short launch video. And don’t hesitate to get in touch if there’s any way we can help!

First things first: I’m pumped to announce that No Brainer is now part of the Marketing Podcast Network, the premier network of top-quality audio programming by marketers, for marketers. We’re looking forward to growing alongside 40 or so of our favorite marketing thought leaders — and we’re glad you’re taking the ride with us.

And so, without further adieu…

In today’s power-packed episode, Geoff and I rant about “X” — Elon Musk’s entry into the generative AI race — just weeks after he signed the controversial “AI pause letter” that called for a six-month moratorium on training advanced large language models. What are his plans for his so-called TruthGPT? Will he build an LLM that’s trained on tweets? And could X become the “everything app” we never even knew we needed?

Then, we switch gears to talk about the implications of AI in ad world. Inspired by a great article at FastCompany.com, I share some real (and potential) ways that the big advertising agencies are integrating generative AI into their creative workflows. Geoff talks the truth about the long-term love affair between advertising and AI. And we ponder whether agencies will rise to the challenge and become indispensable AI advisors to big brands… or sink to the depths of pointless hype (as many did with Web3 and the metaverse in 2022.)

Be sure to visit the No Brainer website for the complete show notes, including links to the articles mentioned in this episode.

Today’s episode is packed with power as Geoff Livingston and I dive deep into AI ethics. With ChatGPT churning out fake news and Midjourney attempting to pump the brakes on deep fakes, it’s no wonder companies and countries are looking to rein in the use of generative AI.

We explore all of this and more, pondering whether humans are striking back against runaway AI productization. Geoff shares his thoughts on the seven essential elements for corporate responsible AI policy, we discuss some practical ways to keep AI from going off the rails (and taking your brand reputation with it) and tackle some tough questions that were submitted in advance by show listeners.

The show wraps with some thoughts on why it’s important to involve your employees in your AI ethics decisions, and even more important to understand what your consumers expect when it comes to how your brand uses AI in your marketing programs.

For full show notes including time stamps and links to all the sites, programs, and resources mentioned during today’s show, visit NoBrainerPodcast.com. You can find our episodes on Apple, Spotify, iHeart Radio, and just about any app you use to list to your favorite pods. Speaking of favorites: If you like what you hear, be sure to subscribe, rate, review, like, and recommend us to all your friends.

So prevalent that Chris Moran, the head of editorial innovation at online news site The Guardian, took to his own Opinion page to write about two inquiries from two researchers looking to verify reporting that ChatGPT had attributed to named reporters working for the publication. Pretty typical stuff for researchers and reporters. Except that neither article exists.

The same day Moran published his Opinion piece, news broke that ChatGPT had invented a sexual harassment scandal and named real George Washington University law professor Jonathan Turley as the perpetrator. Its source? An imaginary Washington Post article from 2018. In fact, when WaPo did its own investigation into the incident, reporters there found that Microsoft’s Bing — which also incorporates GPT technology — repeated the accusation.

Where a couple of odd calls to an editorial desk might be a nuisance (for both the desk and the researcher who likely assumed they were looking for nothing more than a check-the-box verification), false accusations of criminal activity are something far more dangerous.

“Hallucinations” like these are likely to become less prevalent over time, as the large language models that underly chatbots like ChatGPT, Bing, and Google’s Bard are trained on even larger data sets, refined through reinforcement learning, and fine-tuned based on the millions upon millions of prompts from and interactions with early users. But incorrect and illogical errors may remain a fixture of GPTs for a long time — possibly forever.

I should pause here to point out that, inflammatory headline aside (damn you, tempting clickbait!), ChatGPT does not, in fact, “lie” any more than it tells the “truth.” It does neither. It has no understanding of the words it strings together. It has no moral compass, no motives, does no reasoning, and never pauses to reflect. Again, it merely strings together words in a manner that often turns out to be true, but sometimes doesn’t. ChatGPT doesn’t know the difference. But you, dear human, do…

And that’s where you come in. What’s a user to do?

The lesson for any consumer or business end user who employs ChatGPT (or any of the many applications built on top of OpenAI’s GPTs) as a research or writing assistant is a simple one: Verify. Verify. Verify.

If you thought prompt engineering would be an important job skill in the age of AI, wait until I tell you how important fact-checking will be. It’s important to keep in mind that an AI like ChatGPT hasn’t been trained to tell the truth or get things right; It has (essentially) been trained to respond to user queries with information that sounds plausibly accurate, to make connections between words without understanding the meaning behind those words as a means of generating believably human-like text.

If I sound a bit like a broken record, so be it. It’s far too easy to anthropomorphize this technology and assume it has more agency over its actions than it does. And this is precisely where any human user is bound to get in trouble. Ultimately, you are both the arbiter of the truth you tell and the person who is accountable for it — whether or not you incorporate AI into your writing workflow.

There are 100 million+ people using ChatGPT today. And that’s a lot. But bear in mind that this or Google’s GPT model will be coming to your Microsoft Office and Google Workspace apps – that’s BILLIONS of users with access to what is arguably the most powerful — and still highly flawed! — generative AI models in the world. And then there’s GPT-powered search, which Microsoft already offers as part of Bing inside its Edge browser and Google is still testing before rolling it out to the general population.

So even if you’re not a ChatGPT power user today, you’ll be using technology like this (perhaps without even thinking twice about it, as is the case with so many AI applications today) before you know it.

If you’re a generative AI decisionmaker at a media company, marketing agency, brand, or really any company of any kind, the lessons are: Cautious will beat crazy. Editorial (or content) standards are more important than ever. Clear, written guidelines (even policies) and effective communication, roll-out, and enforcement of those guidelines will keep you from looking like idiots (at best) and landing in hot water (at worst).

With Adobe being the latest move by an incumbent technology company, we ponder how the market may shake out between Big Tech and a crowded AI startup scene, leading into a deeper conversation about why marketers need to move beyond AI tools and tactics to make strategic decisions about the right AI stack and the best AI use cases that deliver real benefits for their organization.

Next, I talk about ChatGPT PlugIns, which I see as OpenAI’s ‘App Store Moment.’ Find out why, then listen as we discuss the implications for the future of search marketing, content marketing, the media industry, and brands. Will every brand need to build a custom Plugin to remain relevant to consumers?

Finally, Geoff returns to the importance of strategic thinking for this week’s Brainer (key takeaway) and my No Brainer (tip or trick) highlights some LLMs (beyond ChatGPT) that you can try today.

Episodes are embedded below. Get the full show notes including all the episode links on the No Brainer Podcast site. And check us out, subscribe, and drop a rating or review on Apple Podcasts, Spotify, or anywhere else you enjoy podcasts.

To that end, if you haven’t seen OpenAI’s report (published in conjunction with researchers at the University of Pennsylvania) about the impact of generative AI on the workforce, it’s worth checking it out. I’ll warn you though, it’s not sunshine and rainbows.

AI Will Impact Nearly Every Occupation

In short, the team behind the report concluded that AI (specifically GPTs and GPT-powered applications) will impact most occupations to some extent. Most significantly, there are entire fields of work that have “100% exposure” to automation by AI. Jobs like writing, designing, web development, journalism, tax preparation, financial analysis, clinical data management, and climate change policy analysis(!) all fall into this category. Does this mean that 100% of these jobs go away? Not necessarily, but 100% of those jobs are impacted according to this report.

A range of other occupations are heavily impacted, even if not to that same degree: Writers, authors, and lyricists. PR specialists. Proofreaders. Translators. Survey researchers. Court reporters. Blockchain engineers. Animal Scientists. It’s an eye-opening list that houses more than a few surprises.

According to the study, higher-paying jobs and those that require formal education or credentials are more susceptible than blue-collar work and manual labor. This isn’t surprising given that the research looks specifically at the impact of GPTs, not at AI overall or even robotics. So, for white-collar knowledge workers, this one hits closer to home.

To be precise, if I’m reading the report correctly, the measure of “impact” is that a human aided by AI can do a given work task in half the time without a loss of quality, vs a human alone. You could certainly interpret this as good news. GPTs could deliver a meaningful improvement in productivity — and that’s good, right?

Some argue that we are in the midst of a productivity crisis that could have dire consequences. On the other hand, the emphasis on personal productivity (at its worst, exemplified by hustle culture) can lead to burnout and exacerbate health issues. So, others argue that AI-driven efficiencies may turn work into a hyperproductive hellscape in which all of us must lean into an AI toolset to boost output.

Regardless of where you stand on this, one point is clear: You can no longer compete in the future of work if you aren’t AI-fluent. And that alone is argument enough to learn ways to leverage GPTs in your work today.

Back to the report — and I’m reading between the lines, to some extent — one clear implication is that most jobs (and the ones that have 100% exposure, most of all) will be irreversibly changed. Again, this could be good, as AI takes on more of the routine, rote, repeatable (and frankly, robotic) tasks that make up the drudgery in what we do. I think there’s an often unspoken risk that GPT-driven efficiencies automate away far more than this, though — eating away at the work most of us would consider strategic, creative, nuanced, and rewarding. The work that is core to what we do and — for many people — core to who we are. In that case, “learn to prompt” sounds glib. Besides, does the world need that many prompt engineers?

Yes, AI Will Take Your Job

The other implication is that many workers will be displaced. Yes, I’m saying it: A robot will take your job. OK, maybe not your job specifically, and certainly not every job. But I believe that in the near term, many jobs are at risk and many workers will be displaced.

Frankly, it’s not so much about whether GPT-borne productivity will eliminate this particular job or that particular job. It’s that organizations that put efficiency and profitability above everything else will invest in GPT-enabled technologies to justify and build structures that require fewer human workers to serve in the functions that are easier (if not better) handled through automation.

But let’s assume this is neither good nor bad. It just is.

We’ll adapt, as we (almost) always have. Much like hunters became farmers, and farmers found work in factories, then factory workers found knowledge work desk jobs, knowledge workers will now… Well, I’m not quite sure. And lest we forget, great swaths of the world’s population have struggled to make these shifts, many people have been left behind (whether by choice or circumstance), the digital divide is wide, and wealth has accrued overwhelmingly to the few (even if, arguably, the global standard of living has risen overall).

But OK. We’ll adapt. But can enough people adapt quickly enough to avoid near-term technological unemployment? I’m not so sure.

I know it’s in vogue to argue that AI will create more jobs than it destroys. Heck, I’ve been using that line for years. Over the long term, this may be true. But in the nearer term, I don’t see how there isn’t significant displacement — and even widespread deskilling — in the workforce. Again, by choice but more likely by circumstance, many will get left behind. The digital divide will get wider still.

Unlike prior revolutions in the nature of work, the mainstream adoption of GPTs is far too fast to allow for the kind of education and upskilling necessary, at the scale and on the timeline we’re looking at. Anyway, who exactly is on tap to do all that upskilling? Employers? Educators? Governments? YouTube scam-boys hyping get-rich-quick schemes? More importantly, for most “regular” people — the people who don’t spend their days sweating this stuff — this isn’t even on the radar.

(If you’re reading this, you’re inside the bubble. It might seem as if LLMs are practically the only thing anyone talks about anymore. Just a few weeks ago I keynoted the annual offsite for the customer service division of a global manufacturer. When I asked how many people had heard of ChatGPT, four people out of nearly 400 raised their hands. Zero had actually tried it. Let that sink in for a moment…)

Almost Nobody is Prepared for AI

Already, this — along with the fact that the for-profit large language models are black boxes, and that the companies deploying them aren’t doing nearly enough to mitigate their potential harms — has led technology pundits, ethical AI academics, and even tech reporters to suggest slowing things down. In truth, this is unlikely to happen. The arms race and gold rush have already begun.

If you go with the measure of impact used in the OpenAI labor report (completing the work in half the time), one AI-augmented worker can do the job of two. While the company that empowers this across its entire workforce would be extremely productive and extremely successful, the emphasis here is on extreme. That company would be an edge case in its industry (likely in any industry). I’d expect that a more likely case will lead to a substantial reduction in the human labor force as the practical impacts of workplace AI are felt. And yes, entire professions could indeed be blinked out of existence.

Take just one “100% exposure” job: the graphic designer. This past week, Adobe announced its own proprietary LLM (Firefly) trained on properly licensed rights-free content, and its plan to integrate GPT-enabled functionality throughout its Creative Cloud suite of tools. As a frame of reference, these tools are the de facto professional standard for corporate creators. It is this kind of innovation that might halve the time it takes for a designer to complete her work.

But tech analyst David Mattin points out that the impact may go much deeper than this. Provide non-design employees elsewhere in the organization with access to the same toolset and you equip the entire organization with an “infinite in-house visual creator.” His verdict? “RIP design and illustration as we knew it.” As AI democratizes creativity, it utterly devalues it as a dedicated career path. Even if Mattin’s take is too extreme in its assertion, it’s not beyond the realm of possibility.

Maybe long term, it’ll be fine. We’ll catch up. We’ll adapt. We’ll redefine work. According to one school of thought, we may be heading toward a post-work future. Media theorist Doug Rushkoff, for example, has long argued that the notion of working for a living will be seen as a blip on the human timeline, and the idea that a person had to have a job in order to earn access to food, shelter, education, and healthcare will be seen as a relic of times gone by.

In a 7-page letter, Bill Gates argues that there will always be plenty of work to go around. And that AI’s encroachment into knowledge work might cause more of us to seek work as educators and caregivers. Important work, to be sure, but work that requires specialized training (which takes time), is high-stress, unattractive to many, and generally (unfortunately) under-valued and under-compensated. Still, his vision is hopeful — one where humans increasingly work not with their heads but with their hearts.

Depending on your point of view, this might be heaven. Or it might be hell.

Meanwhile, we have the practical implications of AI’s impact on work — and what our lack of readiness means for workers. Constellation Research analyst (and my former Dachis Group colleague) Dion Hinchcliffe suggests a timeline in which AI affects hiring this year, displacement sets in by 2024, and is deemed a “serious” issue by 2026 — yet broadscale reskilling lags another two years (2028) and novel solutions to the labor problem and the emergence of new (presumably more equitable) economic models could be a decade or more away.

Needless to say, this is a problem and we’ve been caught flat-footed. But this isn’t to say that we can’t get back on track. Just that it will take time. And it’s the gap in time between when we feel the impact (as early as — well — now) and when we have enough widespread supports to welcome enough people into an AI-powered future or work that creates the near-term challenge.

And, if you look at the details in Dion’s timeline, even this assumes the use of “Super AI” to help tackle the unemployment problem. Super AI… Not Artificial General Intelligence (AGI), which even many leading AI researchers argue may be decades away or outright unachievable, but the next level above that: artificial super-intelligence (unless Dion is using the term more loosely).

Wither the Firm?

But wait, there’s more.

Jobs provide one lens for viewing the impact of AI. But in a LinkedIn thread about the OpenAI report Peter Evans, chief strategy officer at an e-commerce consultancy, notes “we should also consider firms, since this is how most jobs are organized. When industries are hit by disruption, some firms adapt and grow, while others perish along with the jobs they supported. When we look at the list of jobs that are 100% exposed and see tax and accounting so high on the list, one wonders which, if any, of the Big Four professional services firms (Deloitte, EY, PwC, and KPMG) will remain in 2030-35? If they do, how many people will they employ and what services will they provide?”

It’s a good point. And through this lens, Dion Hinchcliffe’s novel solutions and new economic models can’t come too soon.

As important as it is to consider the future of the firm, it’s more important to consider the responsibility of the firm — and more specifically, the responsibility of the leaders in the firm. All of this is only an issue if the people in charge of who gets to work and in what roles allow themselves to be seduced by AI-driven efficiency above all. In other words, given the inevitability of the human-AI workforce (it’s already here) it remains incumbent on leaders to ensure a human-forward workplace.

What Can Leaders Do to Prepare for AI?

Don’t panic. Do act.

As the impact of AI on work becomes increasingly apparent, business leaders must take action to prepare themselves, their companies, and their workers for the future. Here are some things you can do, starting right now:

• Be proactive in understanding the potential impact of AI on your industry and business, and identify areas where AI can be leveraged to improve productivity and outcomes. Consider all of this through the lens of your human workforce — What changes? What remains the same? Who feels emboldened? Who might feel threatened? What roles are at risk? Which roles will reap rewards? How can you build and sustain a work environment that is human-friendly and human-first, even as it becomes more digitally enabled by the day?

• Offer targeted reskilling and upskilling programs that address the specific skills and competencies that will be in demand in the new world of work. This could include training in areas such as data analytics, artificial intelligence, human-machine collaboration, and other emerging technologies. You can work with educational institutions and training providers to develop programs that meet the needs of their workforce and align with their business goals.

• Redefine roles and responsibilities to take advantage of the unique strengths of both humans and machines. For example, workers could be trained to work alongside AI systems to improve productivity and efficiency, or to focus on higher-level tasks that require creativity, critical thinking, and problem-solving. This could involve a shift from traditional job roles to more flexible, project-based work arrangements that allow workers to develop new skills and adapt to changing business needs.

• Foster a culture of continuous learning and development, where workers are encouraged to take ownership of their own professional growth and development. Business leaders can provide resources such as online training courses, coaching and mentoring programs, and networking opportunities to help workers stay up-to-date with the latest trends and technologies in their field. This can help build a more resilient and adaptable workforce that is better prepared for the challenges and opportunities of the future.

• Consider new business models that leverage the unique capabilities of AI to create new products, services, and revenue streams — and that also place value on uniquely human competencies.

• Collaborate with government and other stakeholders to address the societal and economic impacts of AI, such as job displacement and income inequality.

While the challenges posed by AI on work are significant, it’s important to remember that this is not the first time that technological innovation has disrupted the workforce. With careful planning and thoughtful leadership, it’s possible to create a future in which humans and machines work together to create a better, more equitable society.

Let’s approach this future with open eyes and a commitment to building a better world for all. And hey, let’s be careful out there.

OpenAI just announced the availability of plugins inside ChatGPT. Well, sort of — they’re rolling them out to paid ChatGPT Plus users first, using a waitlist, then gradually making them available more widely over time.

What Are ChatGPT Plugins?

Plugins are extensions that install and run inside ChatGPT, allowing the large language model to access and work with proprietary data sets from the plugin developers or access real-time information to provide more relevant responses. The first plugins have been created by Expedia, FiscalNote, Instacart, KAYAK, Klarna, Milo, OpenTable, Shopify, Slack, Speak, Wolfram, and Zapier. Short videos on OpenAI’s site demonstrate specific use cases (booking a restaurant reservation through OpenTable, selecting a recipe, and populating an Instacart shopping list) that seem to bridge access to information with actions based on that information.

Like so:

In addition to these third-party integrations, OpenAI is rolling out its own browser plugin that knows when to access the live internet for current information — overcoming an ongoing challenge and common complaint given that the underlying GPT models are only trained through September 2021.

Here’s how that works (no sound):

Why Are ChatGPT Plugins Interesting?

To this point, announcements from partners like Hubspot, Salesforce, Kahn Academy, and so on have emphasized the integration of ChatGPT or the underlying GPT models into the partners’ own platforms. Plugins flip this model on its head — to be clear, this is an “and,” rather than an “or” — by bringing new use cases into the ChatGPT conversational interface.

Now, with third-party integrations available via ChatGPT’s Plugin Store, OpenAI seems to have taken a page out of Apple’s playbook. Thinking longer term, this leads me to wonder: If the future of human-machine interactions will be conversational, is OpenAI moving to turn ChatGPT into the new operating system? As the ecosystem of plugin developers grows, giving end users access to more and more access and functionality from inside the conversational UI, does ChatGPT become essentially a new browser or a 21st-century take on the old-school web portal? Does this make ChatGPT itself a more suitable search engine that, curiously enough, could compete head-to-head with Microsoft’s GPT-4-powered Bing?

Is the race on for brands and media companies to build their own plugins — in much the same way they built websites and mobile apps (or for that matter Second Life islands and more recent metaverse activations) — to embed their data sets and unique value inside ChatGPT?

Practically speaking, putting the chatbot in the third-party services or putting the third-party services in the chatbot may amount to essentially the same thing: making a given service accessible via a conversational UI. That said, the ability for a user to access, engage, and switch between multiple services from within a single “box” represents a potentially significant upgrade to the user experience.

The Battle of the Chatboxes

Ultimately, we may be looking at the first volley in a battle to control the chatbox as the interface layer between consumers and companies. And as we’ve seen before, the company that controls the interface layer between consumers and companies (whether we’re talking about Facebook’s social network, Amazon’s store, Uber’s marketplace, Apple’s iOS, or — now – OpenAI’s ChatGPT) often reaps an outsized share of the rewards.

While this may be a boon for OpenAI (or really, any Big AI company that is quick to seize on this opportunity), there’s a certain level of risk to brands when a third party has so much control over the customer experience. Facebook’s shifting relationship with brands and media may prove to be a telling parallel.

Time will tell how this all turns out. For now, though, my sense is that the introduction of plugins to ChatGPT marks a significant step forward in the evolution of conversational interfaces and the integration of human-machine interactions. As more and more third-party integrations become available through the ChatGPT Plugin Store, ChatGPT could become the go-to platform for companies looking to embed their data sets and deliver unique value inside a conversational UI. As the ecosystem of plugin developers grows, it will be fascinating to see what new use cases emerge and how businesses capitalize on this new frontier.

Then along comes ChatGPT, followed in rapid succession by the release of OpenAI’s more powerful GPT-4 model, and all of a sudden AI is brand new again. Marketers have been quick to heed the hype, shifting their attention and even their budgets away from recent darlings like NFTs, Web3, and the metaverse. Indeed, 2023 has been the Year of the GPT — with big brands like Coca-Cola embracing early (if, in my opinion at least, uninspired) experimentation; martech mainstays like HubSpot, Salesforce, and Adobe turning LLM-driven tools into a must-have feature; and just about everyone else going ga-ga over prompt engineering and the ways in which GPTs can crank out content at superhuman speeds.

Marketers do love a good call-t0-action. And if generative AI had a CTA, it would be: get up to speed or get left behind.

Indeed, I’d tell my clients the same thing. Experiment. Learn. Get your hands dirty. Try it (even if it’s too soon to trust it)

But if that’s all you do, you’re missing the point — and you’ll miss the boat. Or worse, you’ll board the wrong boat, pursuing generative AI as yet another shiny object on this month’s marketing innovation punch list.

In the worst case, this kind of narrow consideration misses the mark — leading you to invest energy into doing things that benefit neither your company nor your customers. The world is not waiting for you to 10x your output of low-quality, look-a-like content, no matter how easy ChatGPT makes it to do so.

Instead, experiment and explore with your strategy in mind.

Consider what marketing challenges a powerful generative toolset can help you address, and which objectives it can help you achieve. How can generative AI help you market better (not just cheaper or faster), differentiate you from your competitors, or connect your company with its customers in a deeper and more meaningful way? How can AI narrow or close any gaps that may exist in your consumer experience, or provide you with new ways of delivering value at critical points across the customer lifecycle?

Don’t make the mistake of turning generative AI into a solution in search of a problem. First, define the problem. And then figure out if generative AI is the right solution. Where it is, you’ll open the door to smarter, stronger, more strategic experiments that might actually deliver real value for you, your company, and your customers.

These are exciting days, indeed. But as is generally the case when it comes to marketing’s shiniest objects, I’d like to believe that level heads will prevail.

As you venture deeper into the realm of AI-driven marketing, I hope you’ll commit to prioritizing the needs and experiences of your customers. Generative AI, exemplified by GPT-4 or brought to life in whatever marketing technologies you have at hand, presents an opportunity to revolutionize the ways in which you connect with your audience and create meaningful, lasting relationships. It’s your responsibility to harness this transformative technology with intention and foresight, crafting innovative strategies that elevate your marketing efforts beyond mere quantity and into the realm of genuine, customer-centric impact.

Embrace the potential of generative AI, not as a fleeting fad, but as a powerful ally in your pursuit of delivering real value to your business, your brand, and the people you serve.

Or check out this episode, embedded below.

A couple of months ago, my buddy Geoff Livingston and I started sharing our thoughts about everything going on with AI (spurred on by the release of ChatGPT) and what it means for marketers. Two things occurred to us. First, it feels a lot like the kind of transformation the marketing industry went through at the advent of digital, then again with mobile and social. And second, other marketers might benefit from learning with us as we go.

And so, we’ve teamed up to launch a new podcast — No Brainer: An AI Podcast for Marketers — in which we’ll aim to address the news, the hype, and most importantly the marketing implications of everything that’s going on in the business of artificial. Plus you’ll get a double dose of our personalities (good, bad, and otherwise), so we hope it’ll be as entertaining as it is informative.

This series is mainly for marketers. If that’s not you, no worries — in my writing and work, I’ll continue to explore AI (along with the full range of exponential technologies) through a broader lens, considering the commercial, economic, social, cultural, and personal implications of change.

Our pilot episode is live. You can watch the video show on YouTube, or listen to the audio version on Spotify, Apple Podcasts, Google Podcasts, or pretty much anywhere you get your pods. Check it out and subscribe.

You can, of course, also check out our pilot episode right here.

The November 2022 launch of ChatGPT by OpenAI signaled a major milestone in the awareness and adoption of AI. Usage grew at record speed, achieving in mere weeks the kind of scale that it took previous record holders like Tik Tok and Instagram months or years to attain. Claims that conversational AI could challenge Google’s dominance in search caused a ‘code red’ at the tech giant, who quickly announced their plans to incorporate their own AI-powered bot (Bard) into their search engine. But in the sudden and swift generative AI arms race, even this wasn’t enough to ward off Satya Nadella’s claim that Bing is back (baby!) as Microsoft upped its stake in OpenAI by a cool $10 million and began a suspiciously quick rollout of a new chat-driven Bing experience to anyone willing to install the developer edition of the company’s Edge web browser.

From Great Heights

Early ChatGPT users were blown away. Business and tech journalists pumped out hype-fueled puff pieces. Pundits grew giddy with excitement. The future had indeed arrived! Knowledge work would never be the same. Humans would lose our lock on creativity. Professions would be imperiled. Entire industries would vanish practically overnight as “prompt engineers” learned to coax passable articles, high school essays, business plan outlines, and snippets of computer code out of the AI model’s vast storehouse of information.

ChatGPT may or may not have passed the Turing Test. It did pass the United States Medical Licensing Exam, if only barely, and nearly passed the legal bar exam. Buckle up — the world and work will never be the same.

That’s a whole lot of disruptive energy sparked by the hasty release of a half-baked project that the team at OpenAI nearly shelved, and OpenAI chief executive Sam Altman openly admitted was “incredibly limited” and even a “horrible product” — essentially, more toy than tool.

Indeed, it didn’t take long for those limits to become obvious to anyone who cared to consider them.

How Low Can You Go?

Trained on information but devoid of knowledge and experience, ChatGPT — like any conversational bot built on top of a large language model — is hardly the sage (or — ahem — bard) eager proponents made it out to be. It’s a stochastic parrot — stringing together snippets of language based on probabilities, devoid of understanding (hence, devoid of meaning), and prone to unwitting but not unimportant errors, falsehoods, and worse. Helpful in certain well-constrained cases but not the machine-learning miracle worker it was first made out to be. Certainly, not much (if at all) better than the kinds of AI writing assistants, generally trained on the same data, that have been available for years.

The very same media outlets that fueled the hype now decry ChatGPT as a “robot con artist” that is making “suckers” out of us all, a “notorious bullshitter” that can’t be trusted, a “deal with the devil” that will cost us dearly. I mean, hey, one innocent error in Google’s promotional video for Bard wiped $100 billion off Alphabet’s valuation. Break through ChatGPT’s carefully maintained guardrails and you might find yourself chatting with “DAN,” the bot’s dark side alter-ego who isn’t as likely to play it safe. Meanwhile, the more waitlisters gain access to the new Bing, the more obvious it becomes that the underlying OpenAI model (a version of GPT-3.5 that is more advanced than the one used by ChatGPT) is not only less-than-useful as a search engine but outright weird, inappropriate, and unhinged.

None of this should be a surprise to anyone who remembers Meta’s Galactica or Microsoft’s Tay, both of which went hardcore robo-fascist within days (even hours) of their release.

Still, oh what a difference a day makes!

If ChatGPT broke records for its speed of adoption, it may also hold the record for the fastest plunge from the tippy-top of the Peak of Inflated Expectations to the bottom of the Trough of Disillusionment.

The Future? Or Fvck It?

As anyone who has spent a fair amount of time around emergent technologies knows, the answer is not so cut-and-dry.

Prodigy. Netscape Navigator. Blogger. Twitter. Instagram. Tik Tok. Some were undeniably awful, at least at first. (Twitter has the distinction of surviving its early innovator arrows only to become increasingly awful now.) But each also represents a “where were you when” moment in the evolution of not just technology itself but the way humans interact with technology, information, and each other.

Early users of each remember the sense that something important had just changed forever — and over time, as these tools (or the new capabilities each represented) gained traction and earned mainstream appeal, it became clear that something had changed indeed. And not just something — arguably everything. Even if it took some time to understand just how significant the change would be. And even if not every change was positive or quite as promised.

And this is perhaps why, at a moment when conversational AI prompts us to “ask anything,” it’s equally important to question everything.

It’s why I’m equally excited by what AI promises and skeptical about what it delivers. Why I’m equally eager to heed (but hopefully not feed) the hype and to cast a critical eye. If this is a pivotal moment in the history of technology, the economy, and humanity, it’s also moment that benefits from a balanced perspective.

And all of this is why I’m not particularly discouraged that an AI Winter has come so soon. Because our climb up the slower, more steady Slope of Enlightenment begins soon too. There will be more bumps along the way, and not every early innovator (or digital age stalwart, for that matter) will make it all the way to the Plateau of Productivity.

But I’m excited to see where this journey leads us all.

(My brother from a cyborg mother, Geoff Livingston, has shared similar thoughts on Medium, and I’ve written about this before too.)

The odds are that you’ve seen the buzz about ChatGPT, a large language model trained by OpenAI to generate human-like text based on the input it receives. It’s a variant of GPT-3 (Generative Pretrained Transformer 3), the machine learning model that is considered to be one of the most advanced technologies for generating convincing, high-quality written content.

ChatGPT was specifically designed for conversational text generation, meaning it’s good at generating responses to questions or requests on a wide variety of topics based on the data with which it was trained. Meaning, it’s essentially a super-helpful chatbot. But a chatbot that is far more advanced than any you’ve likely interacted with online.

Its conversational interface makes ChatGPT easy (and fun) to use. Given that OpenAI has released a free version, ChatGPT has attracted more than a million users in just a few days (often exceeding the service’s capacity). And this has resulted in nearly as many opinions about exactly what this technology means for human professions as diverse as writing and coding, for companies (like Google), and for entire industries — like education, media, or marketing.

Granted, these are the kinds of things that some of us have been pondering for years: whether technology will ultimately replace humans or empower humans to achieve more, do more, and be more. And of course, the answer is yes to both to one extent or another (as has always been the case…)

But ChatGPT’s recent release and the fact that so many (business) people are playing with it has made the great debate around AI top of mind for many more people — furthered by the fact that it’s just so damn convincing, even if it’s not particularly good.

Hey, even OpenAI co-founder Sam Altman has said so!

ChatGPT is incredibly limited, but good enough at some things to create a misleading impression of greatness.

it’s a mistake to be relying on it for anything important right now. it’s a preview of progress; we have lots of work to do on robustness and truthfulness.

— Sam Altman (@sama) December 11, 2022

Over the past ten or so days, I’ve asked ChatGPT to write articles, headlines, social media posts, and even my own bio. I’ve had it outline a digital strategy for a client. I’ve challenged it to rewrite many of these in the style of Shakespeare, the Bible, Kurt Vonnegut, Stephen King, and the poet Billy Collins. In almost all cases (unsurprisingly, it totally botched the strategy, instead offering a set of steps I might use to develop the strategy on my own), ChatGPT performed surprisingly well, especially when compared to the kind of (frankly so-so) output I’ve gotten from a number of other AI writing assistants I’ve tried.

Now, don’t get me wrong: It never rated a 10 out of 10, rarely even a 5 out of 10. Its work (and words) lacked nuance and personality. It excels with facts while struggling with opinion, although even ostensibly factual information contained obvious errors, (as OpenAI notes on their site) is prone to bias, and doesn’t incorporate anything that has happened since the system was last trained in 2021. 

Overall though, ChatGPT comes across as surprisingly human in its ability to string together information into something that loosely resembles the written expression of human thought. This fact alone makes anyone arguing that they don’t feel even a bit at risk in their work seem like Luddites, especially when you consider how far this type of AI has come in these earliest of days.

This isn’t to say a dose of healthy skepticism is not warranted. Because on the flip side, we have the system’s obvious drawbacks (noted above) and the reality that any large learning model might put the world’s information at an AI’s virtual fingertips but that this type of artificial intelligence does not, cannot, and quite probably never will possess true expertise or have any actual understanding of the material with which it works. These facts make the breathless raves, pie-eyed wonder, and premature prognostications come across as unadulterated hype.

After all, ChatGPT is indeed weak AI. I don’t mean this as a pejorative. ChatGPT is weak in the technical sense — trained in and focused on a specific, narrow area. And weak AI is already all around us. It’s in our Google Maps and Uber app, in Siri and Spotify shuffle, and in the infernal social network algorithms that are as likely to connect us with our community as they are to feed the political rage machine. 

Yet none of these use cases feel magical, even if they once did. ChatGPT does (feel magical, that is), possibly because it engenders a sense of play. This distinction lies in its interface more so than in its intelligence, or in the ‘fingers on the keyboard’ nature of the interaction and the genie-like ‘your wish is my command’ interpretation of every prompt. And — in contrast to the ephemeral nature of the responses from a Siri or Alexa-like voice assistant — the tangible nature of its output. You wanted an article, you got one. You asked for a summary, it’s right here in black-and-white bullets (just like you specified).

You might make a similar argument about generative image generators like DALL-E 2 (also from OpenAI) or Midjourney, both of which are also enjoying their magical moments right now. And you’d be right, to a large extent — they’re certainly part of the same pattern. But ChatGPT seems to hit closer to home for more people, partly because writing is such a routine task for so many and partly because the conversational interface hides the technical quirks of the process behind a layer of human-like chit-chat that feels far more natural. 

And so, I wonder:

Is the release of ChatGPT a tipping point moment in artificial intelligence? Will it result in many more millions of mainstream a-ha moments about what it means to engage with and be augmented by intelligent machines? Are we getting just a peek through the door to unlimited possibilities for the future of work and the future of life? Of the rapidly changing relationship between humans and machines?

In some ways, it reminds me of the first time I fired up a web browser. It was awful — from the screeeeeee of the modem to the slow-motion load of the clunkiest website you could ever imagine — but still, you got the feeling the world had changed forever.

In some ways, it reminds me of the first time I tried Twitter (c’mon Elon, don’t f— it up, man!), of that seachange moment when the web went real-time even if hardly anyone outside of social media wonks was there at the time. Or even the first time I logged into Second Life or, so many years later, first strapped on an Oculus headset as part of a conference demo.

ChatGPT is surprisingly good.

And also pretty awful.

And it just might be giving us a glimpse of the future of work and the future of the world.

The deeper I dive into web3, the more ways I find to experiment with how I get my work out into the world.

To that end, I’ve been playing around with a new(ish) web3 publishing platform called Mirror. In simplest terms, Mirror moves the basics of the online authorship model introduced by Medium or Substack onto the Ethereum blockchain. This allows writers (and other creators, although for me, Mirror is uniquely suited to wordsmiths where other NFT platforms favor visual artists or musicians) to post their work, have it validated as a distinct digital asset (essentially a web3 copyright), and then mint editions of that work as NFTs.

Right now, you can visit my Mirror profile to read and invest in six of the essays that appear in my book NEVER NORMAL: Uncommon Ideas for Leaders Who Won’t Settle For the Status Quo. This includes the title essay that to date has only been available in the book.

Each of the six essays is available as a limited edition NFT, with versions ranging from a mere 0.01 ETH (approximately $28 USD at today’s price of Ethereum) for one of 500 versions of ‘common’ rarity to 1 ETH ($2,800 USD in round numbers) for one of just five ‘legendary’ versions. The tiered structure allows each supporter to come aboard at a price that’s comfortable to them, while ensuring the kind of scarcity that makes NFTs in general valuable in the eyes of other collectors.

(Not financial advice and — as the web3 kids like to say — do your own research.)

Sound interesting? Open to supporting my writing and my work? 

Visit me on Mirror. Read (for free) and choose to mint any of my six essays. Pick your level of investment, and purchase with your Ethereum wallet (Metamask, Coinbase Wallet, etc.)

I’m curious to see how this will work. And I’m realistic enough to realize it may not work at all! And that’s OK. But if it does work, I’ll likely upload the remaining 10 NEVER NORMAL essays, essentially repurposing the entire book as a series of collectible NFTs. And either way, I’ll be gaining some valuable learning about alternative models for monetizing my content without the backing of a big book publisher or the logistical hassles of producing an indie title on my own.

The audio podcast is available on iTunes, Spotify, and other listening platforms (links here). And if you’re up for the full video experience, you can watch the action right here.

  

Adaptability ranks as one of the — if not THE — most in-demand skills for workers and by employers. McKinsey calls adaptability the one skill everyone needs more of.

Over the course of the past few years, I’ve been banging the same drum: First recognizing the vital importance of adaptability as a core competency and organization-wide foundation for a successful transformation. Next, compiling a set of market-proven principles for adaptability into the Adapt Manifesto. And then digging even deeper into the competencies that comprise adaptability and earning a certification in AQ (adaptability quotient) assessments from AQai.

But for many, the question remains:

What is adaptability?

While many have offered their own definitions over the years (although very much in the spotlight right now, adaptability is hardly a new concept), I find the concept is often confused for agility, resilience, or another related but different competency. So, I wanted to offer up my own definition. To me:

Adaptability is the ability to respond proactively (even preemptively), quickly, and appropriately to change or uncertainty as they happen in the environment in which you operate, so that you can thrive in that changing/changed environment.

If adaptability is an essential skill for work and life in the Never Normal, the good news is that it is not a fixed or finite resource. AQ comprises a set of competencies that are learned and can be grown over time. Just like a muscle, the more you exercise it the stronger it gets. In a world that will change more in the next 10 years than it has in the past 100, and in which disruption will come from all directions, understanding, measuring, building, and exercising your adaptability is essential to your ability to thrive.

So, how can you increase your own AQ?

First, be sure to check out the Adapt Manifesto. The seven values and 10 principles provide a framework for thinking about change in the modern organization and a set of specific mindsets and behaviors you can adopt for yourself and endorse for your team. None of them are ‘hard’ to do — yes, they require focus and effort (and maybe a bit of courage) but all practical and achievable by design. (Be sure to sign the Manifesto to show your support.)

Second, consider taking an AQ assessment. While the Adapt Manifesto provides an action plan of sorts for bringing adaptability into your work, team, and organization, a personal or team assessment will provide a rich understanding of your current base state for adaptability and a solid sense of how your areas of strength and opportunities for development shape the way you respond to change.

Together, an AQ assessment and the Adapt Manifesto can provide a basis for a know-then-do course of action. Interested in learning more? Get in touch.

In the face of pervasive, ongoing, accelerating disruption, commit to acting on intelligence but in the absence of absolute certainty or predictable outcomes; don’t let perfection be the enemy of good.

This idea has become one of the main takeaways in my recent keynote speeches because of its broad applicability to businesses of all types and the people who lead them. And since we first introduced the Adapt Manifesto in the way-back world of 2019, I’ve had the opportunity to dig deeper into what it means to embrace ambiguity and why it is such an important competency for adaptable leaders and adaptive organizations.

In this short video, culled from a recent keynote “at” (virtual, of course) Dow Chemical Latin America, I share some of my latest thoughts.

The good news is that so many of the skills (including this one) that make up adaptability (or AQ) are learnable. Granted, this doesn’t mean they’re easy. Leaders need to work at these skills and it’s hard, uncomfortable work for many. But it’s certainly vital that leaders for sure, and really everybody, builds the competencies that will enable them to be respond quickly and appropriately in the face of sudden change.

If you’re interested in understanding your own adaptability quotient and learning how you can make the most of your own capacity for change, future-proof your career or drive transformation in your organization, get in touch with me to discuss my AQ assessments, coaching, and advisory programs.

Watch to hear more about the coming waves of change that will make always-on adaptability — not mere digital-readiness — an absolute business imperative.

Last week I had the pleasure of keynoting B2B Marketing Exchange: Next-Level ABM — the first edition of the popular B2BMX conference to focus specifically on best practices in account-based marketing. This keynote was a bit different from my typical talk. For starters, it drew heavily from my 2010 book microMARKETING and tied the framework from that book to current ABM principles and practices. It was also a rare speech that gave me the opportunity to talk a bit about the work we’ve been doing at our content marketing agency.

I did, of course, manage to weave in some of my usual material about the Never Normal and how marketers can help their customers better navigate the challenge of relentless change. This short video clip captures that part of my speech.

Looking for a more complete summary of my message to marketers? Demand Gen Report, the host of B2BMX published a solid overview of my talk. Here’s what they had to say:

The evening keynote led by Greg Verdino explored the shrinking of mass marketing initiatives and the influx of small-scale, 1:1 ABM programs.

Verdino focused on the importance of forging intimate account relationships. He said marketers are slowly moving away from reaching as many accounts as possible in favor of building stronger relationships with targets through real-time engagement in a smaller setting. This allows marketers to foster mutually beneficial relationships with accounts that drive lifetime value for customers and lead to brand advocacy.

“Mattering a lot to a few of the right people is worth far more than mattering just a little to everyone else,” said Verdino. “Forget about reaching as many people as possible — the future of marketing was, and still is, about building and maintaining deep, meaningful relationships between your company and its customers.”

Verdino emphasized the importance of involving key accounts in the ABM process, and that having a “Customer Council” is the best way to hold the accounts’ attention while building brand credibility. This involves the accounts in the brand’s ABM process, which allow marketers to take in feedback and shape future engagement to appeal to the account’s own processes.

“Brands earn attention when they give attention,” Verdino explained. “Businesses become more interesting to buyers when brands are more interested in those buyers, which helps you move away from interruption marketing to more interactive marketing. This is an opportunity to create participation, partnerships and dialogue through collaboration and co-creation.”

Verdino explained that while many modern marketers are leveraging personalization in their ABM programs, they often fail to engage the buying group or person that makes the purchasing decisions.

Digital marketing, however, has made it easier for marketers to learn about the interests and pain points of various individuals, and Verdino believes that marketers have an opportunity to connect with the individuals behind the accounts. Some tactical examples he explored include:

• Customizing Content For Individuals: Research reports, surveys and E-books can be marketed and customized for an individual’s pain points and areas of interest, which increases engagement with relevant data, research and case studies;
• Recalling First-Person Experiences: Sales reps in charge of accounts can speak to their own personal experiences on a pain point or process in 1:1 meetings with individual buyers, allowing the individual to build rapport with the brand they are engaging with and accelerate the buying process; and
• Leveraging Social Media Data: Marketers can determine an individual buyer’s interests through their social media activity, paving the way for creative outreach based on shared interests or pain points between the individual and the brand.

“The boundaries of the personal and the professional have eroded due to the pandemic,” said Verdino. “Marketing to an account without engaging each individual within that account in a genuine human way falls short of the promise of personal.”