This post is about replacing everything in your business piece by piece, especially copy and content.

You must have replaced a bike or car tire. Or maybe car windows, if they were smashed.

With a car, what can’t be replaced? You could replace the seats, the stereo, the steering wheel, the engine itself, and even the air-freshening mint-flavored rubix cubes that hang from the rear view mirror.

Let’s say you do all that – replace everything there is. And now you’re driving your completely replaced car.

Is it the same car? This is the Ship of Theseus paradox, basically.

It’s like primary-school biology class, where you learn that each cell in your body is eventually replaced by a brand new cell that never existed before. Ten-year-olds asking, “Am I still the same person … who am I?”

Who knows.

In brand messaging for B2B tech, there are two core “ships of theseus”.

One is your business website, the other is your body of content  – all the videos, articles, books, and social posts you make.

These roughly equate to copy vs content and like a smashed car window, both can and should be replaced.

Replacing your smashed window of a website

At any given time, about 80% of website copy is out-of-date. Give me the URLs of any 5 websites in my industry and I will show you exactly how this is true. Does that mean it should be replaced immediately? Not necessarily; that’s a multi-pronged business decision.

But replacement should always be on the table and to make this decision, there are a few key indicators to monitor:

• Significant new product or project is completed
• New business model and/or pricing model is introduced
• Key customer or employee leaves – or enters

There are more but you get the picture. If your copy and content is what you say off the phone, what you say on the phone should correspond to it.

BTW, this often means detaching yourself from copy you might like – letting go and letting it vanish.

Because what happens when you replace the copy on your home page? For all intents and purposes, it’s gone. For a high-traffic site, there might be archives on the web for a tiny percentage of it. There might be a search index cache. But it basically vanishes and that’s a good thing.

Everything about a business website can and should be in a constant state of replacement. Not for the sake of busywork, but for the sake of your website doing something – multiple things – better.

Keep in mind, the scope of our conversation here is as always B2B technology businesses. Don’t be distracted by the content practices of journalists, influencers, public figures, major brands, and newspapers. You don’t need to keep a record, that’s not your job.

Replacing your content marketing

Replacing your content has a different arc than replacing copy in your website (and presentation decks). It’s difficult to make content “vanish without a trace”. But often we’re not worried about vanishing it; displacing it is good enough.

One of the age old SEO rules is to prune. The questions to ask before pruning are:

• Is the content still relevant to your evolving audience?
• Does the content meet your evolving standards for quality?
• Does the content attract the right visitors?
• Do visitors engage with it the way you want?

Potential pruning action items are redirecting content, rewriting it, repurposing, or consolidating it – and perhaps just not linking to it from your website. This doesn’t necessarily mean it vanishes from the face of the earth and that’s Ok from a business standpoint.

A couple notes:

• The exact same principle is applied to social content accross all platforms, though the action items are limited by the social platform in question.
• Businesess that apply this principle to case studies or client success stories gain substantial leverage.

The rate of replacement in 2024

If you have ever built software from scratch, you will have noticed the same principle. The entire data and technology infrastructure will be probably be replaced multiple times in the lifecycle of a software product. What stays more consistent is the user experience – but even that evolves.

In fact, there are few areas of a business, if any, that cannot and should not be replaced. For example, the name. My business was called Message Maps the last time I wrote to this list; it’s now called Remap.

In the GenAI-era, is the rate of replacement faster than in bygone eras such as ancient Greece or the Trump administration.

The specific question for most businesses becomes, how frequently will every line of code, content, and copy that comprises their business’s website and content be replaced?

I want to expand on my latest dictionary entry – “Lead magnet”.

Caveat: as with every other definition, there’s an implication of quality. For example, when I define marketing, the scope of the definition is good marketing, not crappy marketing.

So I’m interesting in defining quality lead magnets not the crap you instantly toss in the trash – but let’s think about the latter.

A lead magnet should be very difficult to make

The standard lead magnet formula will sound familiar: it’s a cheatsheet, a 1-pager how-to, a template, a swipe list. It’s just good enough to pique curiosity.

You look at it for a few minutes and then, “meh”, you delete it. Then you get a dozen emails in automated sequence, then you’re on a list, and so on.

At some point within the last 5 years, this got very old.

The format may be fine, of course; a concise how-to manual, for example – nothing wrong with that. But the standard formula I refer to is marked by a conspicuous lack of effort.

If you created it in one day, let alone one sitting, it’s not a lead magnet. Don’t be fooled by course creators and marketing gurus who lead you down the “it’s easy path”.

A lead magnet is not for list-building

Part of the problem is a misconception as to the fundamental purpose of a lead magnet – that it’s a email list-building tool, a “list magnet”.

Maybe 10 years ago that was true, but that concept is very shaky now.

What’s the point of an email list anyway? for 99.99% of people in B2B solutions, an email list is a marketing channel that leads to sales of solutions. Granted: for a select few, the list itself is the product – Venkat, for example. But I’ve never seen such a person lure customers (ie. paid Subtack subscribers) with a traditional lead magnet.

Keep in mind, this is just my opinion. Someone could easily counter it; “of course it’s a good idea to build your list – why wouldn’t you use a lead magnet for that?”.

In my opinion, you wouldn’t because it’s a wasted opportunity to create value – more on that below but first let’s round out the list of what a lead magnet is not.

We’ll make the two premises above the starting point of this list, then add some even more important considerations:

What a lead magnet is not

• Something easy to make
• A list-building tool
• Something that you can’t sell on its own
• Something unrelated to your solution
• Something another business could create

The three points I tacked on are even more important than the first two.

Their connecting thread is this: a lead magnet is a product; your product.

It may not have the same scope as one of your paid products, but it must still have product-like impact and value creation potential.

When you create a lead magnet, think like a product manager, not a marketer.

Or like a drug dealer – does a drug dealer give away something the potential customer will simply throw away? No; they give something that is at least as good as the real thing, if not better. It’s only the quantity that’s limited.

BTW, there’s a finer point there; consider whether a lead magnet pertains to a single product – not to just any offer (product or service) your business provides.

So we design the lead magnet as a truncated version of whatever product it’s meant to inform people of,  interest people in, and sell people on.

In a sense, a lead magnet is an extension of the product itself.

It’s been a while since I’ve posted here as I have occupied myself with building call transcriptions into Message Maps and posting over at The Microdose of AI.

With that, I wanted to flag a post I wrote over there today in which I overview the 12 most important developments over late summer. Because I am trying to distill over a months’ worth of news and developments, it’s more of a macrodose than a micro one.

But I think it’ll be of interest to anyone doing product work, design, development, marketing, etc.

Putting together a news round-up gave me a chance to reflect  – we may be at peak GenAI hype, but that doesn’t mean we’re in a bubble.

Tie that together with where we’re at on the calendar, near the start of Blair Enns’ proverbial 100 day sprint (from Labor Day to Christmas), the fall’s opportunity is to get better at weaving AI into our daily work.

You might find some ideas for how to do that over at the Microdose of AI – The most important AI developments over late summer.

TLDR – Art of Message will now be a weekly email; the new Microdose of AI list will be daily.

A month or so ago, I wrote about the (clumsy) misuse of AI as a deterministic software tool by the (suddenly archaic?) plagiarism detection industry. I say ‘misuse’ because AI works better as an adaptive, non-deterministic interface; it bridges the gap between desires encoded in messy human language and deterministic software/data tools.

Then a week ago, OpenAI threw in the towel here – quietly removing their AI detection “tool” (which was actually just ChatGPT on a different web page, to be honest) because it basically didn’t work.

*    *    *

Like anything related to Generative AI, this new item has a lot of implications for product strategy, no matter who you are.

But apart from it being a questionable PR move by OpenAI to sweep this under the rug, it has fewer implications for messaging strategy.

On that note, a change of direction for this list: Art of Message will go from being a daily publication on product strategy and messaging to a weekly one – with the same focus.

So I will see you here next week with my usual thoughts on product messaging, marketing, and strategy.

Meanwhile, I have started another daily list that will be more technical and more news-oriented: Microdose of AI 😛.

Like Art of Message, Microdose of IA is a “sawdust project” for me. Because I’m designing, developing, deploying, marketing, and selling a software tool that uses AI (and advising on several others), I follow Gen AI news daily – and have done so for more than a year. By coupling that firehose-info-intake with actually building things with Generative AI, I have developed a sense of what’s important in AI news, what’s trivial, and what’s just hype.

That’s what I’m sharing on the new list: clarity and perspective on the most important Generative AI news, technology, research, and stories of the day.

If you know anyone interested in that, please let them know – thank you!

https://microdoseofai.com/

It actually surprises me when people describe AI as having IQ. For example,  the “tests” which found that Bing AI (GPT4) has an IQ of 114 – haha!!

Not sure whether that’s just marketing, or a joke, or what, but I think it’s the wrong frame. Generative has an AI has an IQ of ‘null’ or ‘not applicable’, if anything. Same for EQ.

A child with an IQ of 50 (relative to an adult) can’t defeat Google’s 70 million dollar chess AI, AlphaZero, but they can do something much more intelligent: decide whether or not the game is to be played in the first place.

A better frame for AI-based automation is maybe UQ, usefulness quotient.

The first “UQ technology” I experienced was the automatic door opener at the Safeway grocery store. It had zero IQ but it was massively useful millions of times a day.

Over time, though, it receded into the background of shopper consciousness, taken for granted. This is nice because if users notice something too much, it actually becomes a little bit less useful.

I want the same thing for software built using AI, automatic usefulness that users hardly notice after a few experiences with it.

What happens if you accept that the true North Star is not a metric but an obvious and yet unquantifiable truth? What happens when you forget KPIs, analytics, conversions, goals, targets, and metrics? At least temporarily?

It gives you space to ask:

How do you provide overwhelming value relative to the cost of doing business with you?

I think that question is important enough to leave alone for now. Let me know if you have any thoughts; I will expand on new ways to do this next week.

Have a great weekend.

The best storyteller I ever met was my dad. Or at least he’s one of them – I’m biased of course!

As I was saying, good storytellers like him know when and whether to tell a story in the first place.

And like any reporter or standup comic, they don’t bury the lede or the punch line; they reveal the “so what” upfront.

A good storyteller also:

1. Knows their audience and senses the moment to gauge interest in their story
2. Gauges that interest with a hook – maybe a joke or funny observation, maybe an odd fact or quote.
3. Lets it go if there’s no interest
4. If there’s only slight or polite interest, tells the story end-to-end as one short statement
5. If there’s higher interest, tells the story in chapters or parts and – sometimes in great detail
6. Ends it neatly after any or all of those parts

I think you can tell a great story in one sentence. But great storytellers can tell a long, detailed story and make it just punchy as the one-sentence one.

This means breaking the story into small parts in your head and rearranging them chronologically if needed. And the rarest skill: the ability to summon a great number of facts, moments, and personal details and roll them all into detailed but concise passages of speech. It’s almost as if you think and speak in full, professionally-edited paragraphs.

Until it all comes together in a tidy ending, like a gymnast perfectly sticking a landing.

Yes, but how do you apply all this to business solutions storytelling?

If that question interests you, an example approach is the Brightr Story Framework, which makes a case study a story by highlighting the people and key moments of a business engagement.

When you open Instagram, Facebook has 10 million ads to show you. But it chooses just one, to begin with. How? Step by step. To vastly oversimplify for illustrative purposes, Facebook chains targeting filters together in a step-by-step sequence: gender, age, location, hobbies, bidding/budget, etc. The point I want to stress is that the filters don’t happen all at once; they’re sequential.

This is how code interpreter depicts it:

By the way, this is also how prompting an LLM works when you chain prompts together – a process of elimination is employed. Thus you can deliver more personalized content, analysis, or summarization.

In a way, chained filters depicted in the graphic are a form of personalization, but a crude one. I make 4 different ads and use the platform to show them to 4 different kinds of people. Pretty limited.

But over the last 10 years, there are ever more “dynamic” ads – dynamically personalized in real-time based on viewer data. The classic example is the shopping cart-data-as-ad: you put shoes in the cart on one site but don’t buy yet, then you see a massive ad for them on another. That’s called retargeting.

But these are also crude as a form of personalization, in one part because they rely heavily on ethically questionable data mining. But in another part because they offer limited inputs. Or they are just wrong.

You might have seen ads like Home prices dropping in [your city name]”, where the city named is an hour’s drive away.

Generative AI-enhanced ads, on the other hand, let advertisers pull in almost infinite inputs to dynamically personalize an ad to an individual.

It also allows the user to help create the ad they want to see, or customize, in real time using what we now call “prompting”.

If businesses can achieve that, they can leapfrog over the current regime of personal data mining and offer intriguing, noninvasive, and more personalized ads.

BTW, wherever I say ‘ad’, you can also swap in the word ‘content’.

A CEO should be careful about when to use storytelling – resist its allure.

“The really important issues of this world are ultimately decided by the story that grabs the most attention and is repeated most often”
Annette Simmons, Whoever Tells the Best Story Wins

Let’s file one this under “sad but true”. In the car ride of life, do you prefer a new story, or an often repeated one?

Simmons’ thinking echoes David Gergen, who once introduced a communications strategy called “Story of the Day” to his new boss, US President Ronald Reagan. That’s mass communications thinking and it’s dated.

Anyway, the best storyteller I ever met was … drumroll…

it depends. (Sorry)

It depends on:

• the storyteller’s background
• the audience
• the story itself
• how it’s told

You might say that the goal isn’t to be the best storyteller but the right storyteller.

And good timing doesn’t hurt 🤷‍♂️

There’s also something else: knowing when and whether to tell a story in the first place. BTW, maybe we overuse the word storytelling and its synonym, narrative?

In “Seduced by Story”, Peter Brooks sardonically points out that the “Starr Report”, the official investigation into the conduct of Bill Clinton preceding his impeachment, contained this headline above its findings:

“The Narrative.”

Wait, is it a congressional report related to a consequential legal matter – or is it a story? Also, shouldn’t judge and jury decide the story, not a prosecutor?

How about sometimes we offer something else – facts, opinions, research, code, flavor. And others can make that into a story if they wish.

The best storyteller I ever met (ie the best one for me) was – is – very judicious about when to tell a story. I’ll reveal his other storytelling qualities in the next one.

I got an interesting response to this post from friend of the list and actual data scientist (one of several on this list), who also understands how LLMs are designed and built on a nuanced level.

Because his is a more nuanced view than the ones presented in the paper referenced, I’m sharing parts of it, with his permission. Don your nerd glasses now, as we plunge you deep into the future nexus of generative AI and enterprise data science.

John responds:

What I get out of this is that even if some in a data science role take on responsibilities that look more like product management, the demand for data scientists will still be there.

Makes sense if you consider what it entails: statistical analysis, pattern recognition, inferential and predictive modeling, data transformation, pipelines, and architectures – plus the oversight of custom-built LLMs that help with all of the above. Also: domain expertise and the ability to communicate insight.

The generalist finally has the upper hand in the generative AI era – but the data scientist was a generalist to begin with.

The bottom layer of any business is the expensive problem. But you layer so much on top of that – people, business model, strategy, requirements & plans, tech stack, product design, messaging, pricing, and more.

There’s a layering process in nature too – here’s a tiny sliver of it:

“By producing sugars and proteins to entice animals to disperse their seed, the angiosperms multiplied the world’s supply of food energy, making possible the rise of large warm-blooded mammals. Without flowers, the reptiles, which had gotten along fine in a leafy, fruitless world, would probably still rule. Without flowers*, we would not be.”
― Michael Pollan, The Botany of Desire: A Plant’s-Eye View of the World
*by “flowers” he means fruiting/flowering plants, grasses, shrubs, trees, etc – angiosperms

From here you can choose your business metaphors – if “cloud” are the angiosperms, what comes next?

If LLMs are the angiosperms (and OpenAI is the current keystone species) here’s an inconclusive list of what comes next:

1. Products that sell LLM default behavior, text generation, like Jasper.ai
2. Products that sell something else, like analysis, summarization, categorization, and intelligent integration, like MyAskAI
3. New services models, such as the emerging “AI automation agencies” (more than any tech startup, this phenomenon reminds me of the dotcom boom)
4. Legacy software, like Notion or Brightr, that integrate AI
5. Ecosystem add-ons, like aptly-named PromptLayer.com
6. New LLM providers who research, design, build, deploy, host LLM services

That’s a lot of new stuff – new layers.

But that’s not surprising if you do the math on the Pollan quote – there are 1.5 trillion kilograms of humans, cows, pigs, and sheep on planet earth, all thanks to angiosperms.

Maybe I have achieved a one-day mind-meld with Azeem Azar, because he also noticed an interesting and timely research paper: What Should Data Science Education Do with Large Language Models?

I say timely because of OpenAI’s release code interpreter plugin for ChatGPT becoming widely available.

Sidenote 1: they’ll likely they’ll follow up with the equivalent functionality (probably superior, per Altman’s mission) via the API. But who knows when.

Sidenote 2: the paper’s title almost feels like an article you would see in a tech blog, not an aggregator of academic research papers. Is Arxiv.org the new Hacker News?

But it’s a much more thorough analysis than your average blog post, though, with coverage of every area in which LLMs are expected to impact data science, from education to professional practice . As to the latter, page 4 contains the passage that probably best sums up the paper:

“LLMs have the potential to revolutionize the data science pipeline by simplifying complex processes, automating code generation, and redefining the roles of data scientists. With the assistance of LLMs, data scientists can shift their focus towards higher-level tasks, such as designing questions and managing projects, effectively transitioning into roles similar to product managers.“

I don’t doubt this – one example of many of the opportunity for the laptop worker to subcontract the dirty work to AI, then align their daily work more closely to designing and delivering solutions.

How do you translate a high-level strategy into specific, hard-to-reverse trigger decisions – like hiring an executive, taking your sales and marketing team to a conference, or developing a new product offering?

By first expressing the strategy as messaging or even sales copy.

This isn’t just because it’s easier to undo words. It’s also because you better understand a  business or product strategy once you write it down and expose it to public scrutiny.

Messaging keeps business strategy honest; wherever you get one, get the other from the same place.

To make this concrete, I once had a predictive AI client whose software performed a very specific task: locate the perfect site, in terms of long-term profitability, to build a hospital or clinic. The idea was to do this with Big-Data AI rather than consulting horsepower; from 3 months to 3 hours. It practically sold itself.

I was surprised to learn that although much of the data was purchased, much was assembled by hand. Custom scraping of Google Maps business data, for example.

In fact, this business model brought the AI software firm into possession of a valuable data set: businesses that sell products and services to hospitals and clinics: hospital suppliers, a niche industry unto itself.

Thus a plan for new revenue was hatched: sell a hospital supplier lead list.

But it wasn’t until we started to write about this offer – by creating ads and a landing page – that the strategy crystalized: rather than try to act like a typical lead prospecting database, act like anything but.  This informed the design and delivery of the product itself.

The high-level strategy for the new product was: embrace your strengths and the competition’s weaknesses. But we didn’t know what that really meant until we wrote sales copy.

A recent academic research paper came to the same conclusion as a similar paper from June 28th concerning academic plagiarism in age of large language models: software cannot reliably detect AI-generated content.

From the abstract:

The research covers 12 publicly available tools and two commercial systems (Turnitin and Plagiarism Check) that are widely used in the academic setting. The researchers conclude that the available detection tools are neither accurate nor reliable and have a main bias towards classifying the output as human-written rather than detecting AI-generated text.

That mostly summarizes the answers to the 5 research questions identified on page 3 of the paper (eg. RQ1: can detection tools for AI-generated text reliably detect human-written text?).

One important question isn’t on that list:

Can humans detect AI-generated text reliably?

Nor is its follow-up:

Can humans detect AI-generated text reliably when skillfully using AI tools?

The last question is the most important one – and the only one that has a chance of being a yes.

BTW, the study looked at 14 detection tools (listed on page 11), such as GPTZero, with 3.5M in funding, and of course Turnitin, acquired at 1.8B in 2019. (Sidenote: the study left out the least brittle plagiarism detection tool).

That’s a lot of money being spent on some missed points. For one, would we even need these detection tools if we didn’t still assume that quantity of writing was an effective learning approach – or a reliable way to measure how well a student has learned something? And that’s probably the bigger question.

But here’s where I’m focused: some assume that generative AI tools are here to automate complex solution soup-to-nuts; they’re not. They are here as aids to skillful, thinking mammals with big neo-cortexes. You whisper to the tools in the context of comprehensive approaches to complex problems.

“Your mission, should you use to accept it, is to increase the client’s rate of making trigger decisions. Except when it isn’t”
Venkatesh Rao, Art of Gig

This post is sort of a follow-up to last Friday’s post, 21 ways generative AI will transform marketing – I think I overlooked an important one.

I didn’t realize this until I today when pulled out the Kindle version of Art of Gig on the metro back from a TOA23 event. As usual, I skipped right to the addendum, “100 Rules for Consulting”, and my eyes landed on the quote above.

For context, by ‘trigger decisions’ Rao means important, strategic decisions that trigger a sequence of other smaller decisions and actions.

I had written in ’21 ways’ that:

18. Marketing strategy will be produced, altered, and personalized closer-to-instantly than ever before, with the aforementioned Gi/Go still in effect. 

And then:

19. For that same reason, and others above, marketing and sales content will also be produced orders of magnitude faster; thus it will be more timely, which sort of makes it more personalized

This still holds – more accurate and more rapidly produced strategy means more timely content. But that’s a narrow view.

The broader view is this: like the impact of a skilled strategy consultant, well-designed generative AI will increase the rate of trigger decisions – not just trigger decisions in marketing, but in all areas of the business.

Here’s the fact: it’s not in the DSM.

That doesn’t mean it doesn’t exist, though; it just means it’s hard to diagnose as a distinct mental disorder.

Impostor syndrome exists, instead, as a psychological experience – patterns of thoughts, feelings and behaviors where the through-line is anxiety, even pre-emptive anxiety.

“If I frame it that way, people will think I’m _______”.

Yes, this is normal and yes, it means – congratulations – you’re not a sociopath. But let’s be real, it also cuts your revenue, ultimately.

And impostor syndrome can be a group experience too – people at companies feed one another narratives, or just self-deprecating jokes, that validate doubts. This makes it easy to:

• take refuge in the “features” of your product, rather than go hard on unique value proposition
• emphasize how you’re the same as competitors; you belong, really
• indulge in aloof, indirect messaging
• say too much or try to hard to prove your worth

Actually, this is why firms end up hiring an external strategist – for the latter, making a strong claim about how the company is different is emotionally uncomplicated.

Message Maps has the same effect. Given enough information in discovery, it crafts a positioning strategy that leapfrogs right over your company’s impostor syndrome and stakes your flag in the ground. Which is essential for a tool that rapidly creates sales and marketing messaging to help grow revenue.

Purely as a matter of wordsmithing, you have to give “The Big Con” a 10/10 for its title. As a book title it has an allure to it that almost feels like a promise.

The promise is that you’ll get some blend of James Elroy detective-fiction and investigative tech business journalism by Azeem Azar. The Big Con isn’t as gripping as James Elroy but comes pretty close to being investigative journalism. It presents a ton of data that shows a messy entanglement of large consulting firms (McKinsey & Company, Boston Consulting Group, Bain & Company, PwC, Deloitte, KPMG, EY – and dozens of smaller replicants) with our governments and our economies. This has an inevitable outcome: the crushing of innovation.

It’s war on entrepreneurialism.

But you can’t throw the baby out with the bathwater – there are many brilliant and innovative consultants out there at firms like these.

So maybe “the Big Con” is more abstract than specific consulting firms or even the consulting industry as a whole  – maybe it’s also a conceptual problem.

Maybe what’s most pernicious is the idea itself that you need vast resources, time, meetings, even pedigrees, etc., to “strategize”.

Again, what does strategy really mean? Formally, strategy means a set of ideas that inspire a change to a position of advantage over a significant period of time. Informally, strategy means, to “be smart about things”.

And the way to be smart about things is to spend time asking good questions and putting effort into answering them. But that doesn’t need to take many months, meetings, or MBAs – message maps is based on the theory that you can find strategy with a couple of hours of dedicated focus.

Advice is to make you think and inspire you; certainty and wisdom, though, you have to figure out for yourself. Take the quote below:

“It took a lot of thinking through, a lot of taste, and lot of iterations, and a lot of feedback cycles, to realize ‘ok, we need to write this like we’re writing to our mom or our friend’, because that’s going to make sure the language comes off so clean”
-Patrick Campbell, Profitwell

Someone like Patrick had probably already been told to, “write like you’re writing to a friend”. Or he’d read it in a book or article. That’s not uncommon writing advice.

When you give (or write down) advice though, you’re not actually transferring any wisdom at all, contrary to the sales pitch of many consulting firms.

You might capture someone’s imagination though. All you’re really transfering when you give advice to someone is inspiration,  to get them to try doing something – with the hope that through taking action, that person might eventually acquire wisdom for themself.

It worked for Patrick, BTW; his Profitwell marketing emails have always been so clear that I often used to mistakenly assume he’d written me a quick note, personally (like the host of the episode I pulled that quote from). When in reality, he probably has 60,000 people on his list.

Travelling back in time a bit..

1. Pierre Omidyar was a web developer before starting eBay.
2. Stewart Butterfield owned a web agency before starting Flickr (then later Slack).
3. Jason Fried owned a web design agency before creating Basecamp.
4. Rand Fishkin owned a web design company before starting Moz (then later SparkToro).
5. Drew Houston was a web developer before starting Dropbox.
6. Brian Chesky had a design studio before starting AirBNB.
7. Mat Mullenweg was a freelance web developer before he founded WordPress.

In their era, the web was was the platform – creating business value with web design & development was the opportunity and the imperative.

Since then, we’ve had search, async JS, cloud, open source CMS, UX/design thinking, social, mobile, big data, blockchain, and predictive analytics AI – these were powerful tools for services firms like the above to deliver value to clients.

Yet none were as important as the web itself.

At the same time, we’ve also seen the profitability of web design and development slowly decline over the prior decades, as most businesses acclimate and submit to awful, low-cost websites.

Maybe the “services era” for the web agency is long gone?

I’m not sure yet whether generative AI is as important as the web, but it’s definitely more important than anything that’s come along since.

But maybe we’re still in the services era of generative AI? Or barely in it.

The question for everyone in services is: how can you use gen AI to improve business outcomes for your clients? Making it a line item in actual proposals is a good place to start.

If the past predicts the future, many great AI products will come from people who once provided it as a service.

How do you get strategy-on-demand?

Quick sidebar – in front of the A&O Berlin next to the street, there’s a metal box, just taller than the average tall person, with a screen attached to it.  It’s a pizzabot. Here’s the workflow:

• On the screen, you pick your pizza
• You swipe your card
• the pizza is then assembled and baked, “from scratch”
• the pizza is placed into a pizza box
• The pizzabox is ejected from the metal pizzabot on a tray.

And it’s not too bad!

It’s similar to print-on-demand, where the product exists digitally but you don’t waste the resources and energy incarnating it until there’s a purchase order.

Even closer to true on-demand in terms of timing are education-on-demand such as Masterclass.com or entertainment-on-demand such as Hulu.

But with people-based services-on-demand, TaskRabbit, Handy, Fiverr, the gratifyingly immediacy isn’t there.

That goes for strategy-on-demand too, which usually comes from consulting retainers, perhaps acquired through Toptal or BTG. This kind of service feels rapid compared to a “full” consulting engagement but it’s not really on-demand.

Generative AI works on the immediacy part; ChatGPT, for example, can get you strategic advice in seconds (whether it’s worthwhile – that’s another story).

But it doesn’t actually think, let alone think strategically, let alone produce a complete strategy for a specific purpose. If you need strategy-on-demand, that can only come from inside your head. So question is how to use gen AI to change what’s in your head – on-demand. That’s what Message Maps works on.

This list applies to marketing as a profession, an organizational unit, a practice, a sector of the economy, and as a business need from an entrepreneurial perspective.

1. Marketing will become less of a science and more of an art; analytics will become less valuable because attribution will become less feasible
2. Google will lose its de facto monopoly on search, though partly in exchange for elevating Bing
3. The overall value of search will decline, though that’ll translate to an increase in the value of social media
4. LLM-index optimization will be become a thing – specificity will be the hack, as opposed to keyword cloud content
5. Thus the value of SEO as both an area of expertise and a growth tool will shrink
6. Also, as documented previously on Art of Message, sites focused on transactional content will lose traffic, whereas those offering human connection won’t
7. Also, gen AI will diminish the value of mediocre or even “pretty good” content, because there will be so much of it in supply
8. As a converse result, the value of idiosyncratic content that takes skill and ingenuity will rise
9. Consumers of marketing will develop a razor-sharp radar for AI-driven content, but be intuitively forgiving of content better suited to being AI-created, like text created by support chat bots
10. Producing marketing content will be a skilful blend of human creativity and AI; for example, a person who’s obviously a better writer than ChatGPT may write website copy but also use AI to generate things like detailed product FAQs
11. To state the obvious, developing a mastery of multiple kinds of generative AI  will become table stakes for marketing as a professional practice
12. Marketing teams and agencies will shrink in size but not to 1; ideation and decision-making is a team sport
13. Video will become more important than ever, because producing it will become more possible than ever
14. Content will be more customizable and personalized than ever before but garbage-in/garbage-out still applies; sure, you can prompt your own personalized sequel to your favorite book – but only if you input the entire text of the first book, having read and absorbed that book and thoroughly understood, therefore, exactly what you really want from its sequel
15. Advertising will become radically personalized (and this could be creepy, yes – but doesn’t have to be)
16. Leveraging technology in marketing, namely automation and integration, will become even more important- because it will become more feasible
17. Because of many of the above, social and video advertising will see a burst in creativity and value capture
18. Marketing strategy will be produced, altered, and personalized closer-to-instantly than ever before, with the aforementioned Gi/Go principle still in effect. See (Message Maps)
19. For that same reason, and others above, marketing and sales content will also be produced orders of magnitude faster; thus it will be more timely, which sort of makes it more personalized
20. The lean digital agency model will outperform the management/strategy consulting firm model
21. Generalists will finally win out over specialized experts

“If you’ve got the time… we’ve got the beer”

-Miller Lite

There’s a give-and-take in consulting between extracting every possible piece of information and insight possible from a client, yet not completely annoying them while doing so.

It’s the same with Message Maps. It asks for a lot of your time relative to other online tools.

In the Pre-Alpha release, you took a very intensive strategic discovery interview.

In this Alpha release, that’s been refined somewhat and should be slightly easier to use. The navigation flow around it should also make more sense. Not perfect yet, though, and there are a couple of concrete improvements in the works.

Some errors related to download or printing out your discovery interview report have also been fixed.

But the biggest update to Alpha is that it unveils the first glance at the major piece of the ‘Maps’ roadmap: brand analysis and strategy.

Users who complete most of their interview (80% of the questions or more) and “Submit” their interview as complete will now be able to get a strategy, starting with a positioning strategy statement, the first component of the brand strategy. You can find the button activated on the dashboard and on your interview report.

The 2nd piece of the brand strategy, the detailed brand analysis, won’t be available for another two weeks when we release the first Alpha update. I’m honestly pretty excited and can’t wait to show you (:

But the positioning strategy is here now – if you’ve got the time (to really dive deep on your discovery interview), we’ve got the positioning strategy statement for you.

BTW – to everyone who tried this thing so far, thank you!! You’re invaluable to me!

Definition of Art: “That which provokes significant and lasting emotional, spiritual, and intellectual surprise.”^1

^1 “Art,” in Blue Elephant Dictionary, 2023, accessed June 27, 2023, https://www.rowanprice.com/dictionary/#art.

People like surprise but not too much.

This is according to researchers from the Frankfurt School of Finance & Management. They published a study: “Creating Effective Marketing Messages Through Moderately Surprising Syntax” (syntax being a fancy word for word structure).

BTW, this is not a new idea. What’s new is creating a mathematical model that can measure surprising word/sentence structure in marketing copy. Then to apply that measurement to a large corpus.

Note that the study is confined to syntax (structure). But surprising semantics (meaning) also works.

The semantically unsurprising slogan for an energy drink would be:

Red Bull gives you tons of energy.

The semantically surprising slogan is the one we all know. Though it’s only moderately surprising, because wings are an allusion to flying, which is a common metaphor for the feeling of having lots of energy.

Red Bull gives you jetpack-brain, on the other hand, might be too surprising a meaning.

Syntactic plus semantic suprise could look like this:

Jet-pack brain,

you,

on Red Bull.

You get the picture. Let your messaging fall in the sweet spot between too predictable and too weird.

But now for the real surprise..

Why did I look this study up to begin with? Because I have seen people posting about it on LinkedIn for months now. So I got moderately curious whether it was peer-reviewed scientific research (it is). And when I did some poking around, I found several dozen LinkedIn and blog posts referencing it.

Yet it looks from the study publication page as if the supplemental materials document, the only substantive part of it that’s free, has only been downloaded 4 times, and one of those was me.

Surprise, surprise (:

Examples of keystone species include African elephants, beavers, sea otters, and Zapier.

That’s a little broad, so let’s carve out a subset – animals.

What do elephants, sea otters, and beavers have in common? First, they all spend significant time in forested areas, if you count kelp habitats as such.

Second, they diversify. Through physical transformation, they change the ecological landscape in ways that create and support new life.

The African Elephant

An elephant herd eats and tramples out swathes of grassland amid forests, opens up water sources by puncturing lake beds, and ports genetic seed data from one sector to another.

It’s an ecosystem integration engineer.

Now let’s widen the lense back out, until technology platforms are in view.

Zapier

Zapier also engineers its ecosystem – our economy itself.

Over the past decade, none of its rivals has spawned so much new life.

2.2 million companies use Zapier. And for many, it’s essential infrastructure. For dabblers and non-technical entrepreneurs, consultants, and clever employees, it’s the first experience automating the integration of apps.

Zapier makes simple integration hacks economically viable. For example, this simple Airtable-to-Gmail zap: someone fills out a webform, and boom, you get a draft email ready to send as a follow-up (like after they appear on your podcast).

For the entire 2010s, you did well to at least play with Zapier if not build it into your solutions, as Calendly, Lucidchart, Orchard, and others have done.

That’s still true.

OpenAI

But a bigger elephant than Zapier has come along.

OpenAI’s API by itself is the agent of almost all economic activity related to text-based generative AI and will ultimately create even more economic life. Like Zapier, it’s a keystone species.

And OpenAI has another parallel to Zapier – the best way to understand it is to build something with it.

Startup people love to trot out the original Airbnb pitch deck (easy to Google) but my new favorite is from Mistral.ai, sort of like Europe’s answer to OpenAI.

Aside from data sovereignty for EU firms, they want to innovate on the LaaM,  language model as a service. For example, AI sustainability innovation could come through small lean models that run on your normal laptop and have utility as assistants.

There are more interesting details, too.

But why am I telling you all this?

Because this “deck” is an impressive example of strategic messaging. It’s really just a word doc, too, and it has no graphics, design, infographics, or charts.

If it “cheats” (ie. uses something other than rhetorical skill to persuade) in any way, it’s by mentioning the list of wealthy and successful co-founders, such a ex-Meta/Llama types.

But you sort of have to do that to make an investment pitch deck work.

It’s also got a:

• good story
• a villain (OpenAI/Microsoft)
• a big idea that’s inspiring
• a set of entangled supporting ideas
• a long-term plan for a position of advantage

In other words, it’s strategic. And without a single hero image.

In fact, nor is there a prototype behind it or even a line of code.

Yet just yesterday Mistral raised over 100 million, based on that text.

They really do need that money to lock in AI compute infrastructure too.

Will it work? No idea.

But here’s the takeaway: if the strategy and messaging is on point, sometimes it’s better to skip the decorations.

It’s not just personalization. That’s the #1 rule is that IS observed.

And it’s not just keeping it short; that’s #2.

And it’s not just communicating something worthwhile.

Those are the more commonly observed rules of outbound marketing. Observing them elevates you above the fray, to the upper 5%.

But how do you elevate above the upper 5%?

It’s very simple – the first time you contact someone, observe the rules above, but with a  twist: never mention or refer to yourself or your product or services in any way.

Keep it about them. If that gets a response, the proceed. Otherwise move on.

Just like at a social event.

Most pricing pages state the money you pay but ignore the time you pay. When’s the last time you saw, “Starter tier: $49/month plus 6-8 hours a month of your time” ?

Perhaps this comes from our simplistic view of transactions. In this view, buying something means no more time spent than it takes to pull out your wallet or credit card.

It makes sense – we don’t want to scare away potential buyers by presenting the total cost. Which can be considerably different than the total dollar amount.

Take a professional conference, for example. You elect to pay the conference fee, plus the premium add-ons, then the conference website tells you: “Total price: $999“.

But isn’t this “total” price misleading?

Attending a conference might cost you time spent:

• researching speakers and attendees, accommodations
• travelling in cars, taxis, planes, and by foot
• finding restaurants and cafes, gyms
• looking at weather
• chatting with a chatbot on the conference website
• scheduling
• making online payments
• and more

Excluding any time at the actual conference itself, you might need 12 hours of time to make it work. Let’s say your hourly rate is $xxx/hr – now what’s the “price you pay“?

When you hire a professional service, it’s a similar story: there’s research, communication, planning, preparation, vetting, payment-making, meetings, meetings, meetings. The hidden price of time is even true of software.

Notion.so, for example – I don’t care what your use case is (except perhaps note-taking), if you don’t spend at least 4 hours in research, practice, and study, Notion won’t work.

Like when you pay for a conference online but then spend 0 time doing 0 other things.

As software sellers, there’s an opportunity to (a) gain trust and (b) filter out bad-fit customers by being clearer on pricing.

Another opportunity for another post: add time, not subtract it – like a dishwasher.

I like ‘Marketing Against the Grain’, co-hosted by Kieran and Kipp. They’re CMOs and content marketing experts but in recent months, their takes on generative AI have been particularly on point.

A few weeks ago, Kieran made a point about the increasing value of human connection, clipped here.

You might catch Kipp’s rejoinder:

• Human connection
• Point of view
• Opinion

Sites that lack the above, ones based on information transaction, will be disrupted by ChatGPT and other generative AI-enhanced apps because they are not stuffed fill of ads, content spam, and promotion spam, like Google search.

Stackoverflow.com traffic, for example, is down 20%. It makes sense – who wants to suffer abuse from an acerbic nerd on Stackoverflow when you could get the same information from a cheerful albeit annoyingly-verbose robot on ChatGPT?

Meanwhile destinations featuring human connection, point of view, and opinion are better positioned.  They cite Reddit, for example. (And Reddit-esque humanity plus LLM in the sidebar is a winning recipe).

The same trend will play out on video – as they point out elsewhere in the episode, the value of video in marketing is set to spike in the coming years, because of its potential to deliver a lot of verifiable humanity.

Highly robotic LinkedIn Learning Videos about JS frameworks, databases, and AI will cede ground to idiosyncratic channels like Fireship.

Not because LinkedIn doesn’t have the right information but because Fireship has the human point of view that now feels warmer than ever.

The other day, A Smart Bear wrote about leverage thru embracing differentiated, long-term strengths; “do you” was the idea. If you like that line of thinking, see the book called Good Strategy / Bad Strategy.

The upshot is embracing your business model in your messaging. Imagine that your business falls somewhere on this matrix below.. wherever it falls, strengths and weaknesses come with it.

               Types of B2B Business Models


             slow     decision-speed    fast

 deep        |    Type A    |    Type B    |

insight      ---------------+---------------

shallow      |    Type X    |    Type Y    |

But wait, who wouldn’t want to be Type B: fast on decisions and deep on insight?

And who in their right mind would choose Type X, slow on decisions and shallow on insight??

A lot of businesses actually; let’s take a quick tour of the matrix.

Type A has a lot going for it:

• Develops tailored and innovative solutions
• Long-term profitability based on deep relationships

But… slow decisions can mean missed opportunities, misunderstanding of new trends – plus high cost research and analysis work.

Examples: R&D teams, strategy consulting.

Type B can respond more quickly to opportunities and stack profits quickly – but they also incur the most risks, for their clients and their own firms.

Examples:  agile software shop, a real-time analytics product for equities traders.

Type X  might not be such a bad deal if you like predictable revenue, an easier selling cycle and lower overall risk, in exchange for lower growth potential.

Examples:  a standardized CRM, a bookkeeping package.

And Type Y has revenue flexibility and solution agility – the only issue is the solution might fall flat and do little for customers.

Examples: a rapid prototyping service, curated dataset-for-sale.

I’m guessing you know which quadrant you fall in – if so, you know your strengths and weaknesses. If so, how to embrace that in your messaging?

“We could have called it ‘strawberry intelligence.'”
– Gong CEO Amit Bendov

What Amit meant by that statement, per Andy Raskin, is that rather than “invent” the category of “revenue intelligence“, as Gong did, they might just as well have invented the category of “strawberry intelligence“.

The idea being that Gong’s enormous success didn’t have to do with making up a “____ intelligence” category.

So the category name didn’t move any needles, according to Raskin and Bendov.

Instead, apparently, what people cared about was the Gong company/product story – not their cool “revenue intelligence” category.

Story strategy, you might call it. Like with Storybrand.

Maybe that’s true of Gong but I wouldn’t take it as gospel for any firm.

The thing is, there are good stories and there are boring stories. BTW, here we’re using ‘story’ to mean an account of how a company’s leadership discovered and moved toward their positioning and started creating win-win value in their market.

Anyway, my point is that story strategy only works some of the time.

And I do think the category name “revenue intelligence” made a difference for Gong.

Here’s what I think happened – in the first place, someone in marketing at IBM came up with “business intelligence” back in the 1980’s. And tech companies have been running with it ever since, including Gong.

Why? Because business intelligence or revenue intelligence, or whatever, is an easy to grasp idea that has a nice ring to it – and can be easily riffed on in your sales an marketing materials.

It doesn’t have to be logical, it just has to be non-illogical – and catchy.

For 20 years, conventional wisdom on the web has been: more words, longer posts.

In the late 2010’s, Brian Dean explored this through large-scale SEO research. In analyzing blog content and search engine results Brian formalized what SEO experts had long guessed: Google rewards content for length – 1500 to 2000 words, to be specific.

For now, this is still the case. The net effect is agonizing wordy-ness. Just like in school: if you force people to write X number of words, you bore and annoy readers. ChatGPT has mastered the art; people hate it. It’s industrial-age behavior.

To clarify the obvious: just because an article is long doesn’t mean it’s bad. What’s more, long articles can add value in a way short ones can’t.

For example, the average essay in The New Yorker is 3,000 words; many are 6,000

And long blog post essays about a business, tech, or societal trend can be more, not less, useful for their length, when there’s space to weave together multiple ideas, and provide examples or discuss data.

As a specific example: Andreeson’s Why AI Will Save the World. It’s long enough that it has a chapter structure that serves as a table of contents and internal navigation. (The presence of a ‘chapter’ approach was another Brian Dean SEO finding actually.)

But these examples of high-quality long-form content are exceptional and require great effort.

Neither Google nor anyone else required or incentived Marc Andreeson, a billionaire venture capitalist, to write such a long post. He made it as long as it needed to be to make his point and supporting points.

And he can also keep it short, just check his Twitter. He does so 999 times of out a 1000. That’s a pretty good ratio.

Here’s the thing: keep it short almost all the time, unless you have an extraordinary reason not to – and are willing to expend great effort.

“Ever since I was a child, I’ve dreamed of understanding what animals are saying. How wonderful that is now a real possibility.”
― Dr. Jane Goodall

Quick question – but first some context; bear with me.

Animal research org Earth Species Project is using neural network AI, of the same sort that I use here to create personalized, conversational discovery interviews.

What is ‘ESP’ they up to? They’re learning how to document, decode, and ultimately understand animal language, or communication, let’s call it.

This means we can speak with them.

Maybe this means I can conduct use Message Maps to conduct a discovery interview with a Baleen whale. Maybe it means we can provide animals with their own AI tools, so they can leverage their intelligence and finally overcome the problem of not having opposable thumbs.

That’s interesting sci-fi but back to the question: if we can level the species playing field, or at least talk to more of them, can we also use generative AI to level the playing field for other people?

We wanted that same outcome out of social media and mobile apps, but the results have been mixed at best. Maybe we can do better this time?

*    *    *

PS. Yesterday I announced a “pre-release” of Message Maps, in which you can use one part of the tool in exchange for feedback – there’s the official release notes https://github.com/roprice/messagemaps-community/releases/tag/v0.1.0-pre-alpha-Ausangate

PPS. Speaking of AI and animals, there are more camelids on the mountain of Ausangate than there are open source LLMs bearing their homonyms

Lately on this list and elsewhere I’ve been speaking of:

• the value of personalized follow up questions
• garbage-in/garbage-out in generative AI
• trying to “see” the buyer more deeply
• chatgpt in our apps, not the other way around

And generative AI as the user interface for … pretty much everything.

It’s all connected to message maps, much of it directly.

Which I’m pre-releasing part of today!

I’m not releasing the full tool today but I am sharing its AI-assisted discovery interview – the part that mines your mind for overlooked diamonds in the rough. When burnished and set on the right showcase, such diamonds capture attention.

If you’d like to try to uncover one, reply and let me know – I’ll send an invite code to let you try it out free and set you loose to ‘mine your mind’.

And offer some feedback for me of course (:

The course where I learned the most about business was a studio drawing class in the Arts and Architecture department of my university.

In the first lesson, we set down our pencils, folded our arms for 10 minutes, and looked at – wait for it – a small white styrofoam cube.

There was a smaller white styrofoam sphere perched on top of it. And on closer inspection a small triangular-shaped object behind it. After a while, I saw that the cube had a faint zig-zag pattern in its surface. I also noticed that the sphere had a seam and that there was a slight notch in it just above where it met the surface of the cube.

And so on – the details were always there but seeing them was work.

Later when we began to draw live models, it was the same effect times 1000, because if the goal is to represent what you see faithfully, trying your best no matter how badly you inevitably failed, then you needed more. More detail to improve your capture. And there’s always more. A chair, a floor, a wall, a window, a horizon..

Thus, ultra high-resolution giga-pixel photos, such as Hubble photos of space, offer less to see than the typical room in the typical house.

It’s the same effect as a discovery interview – you’re trying to paint a picture. The more detail you want, the more you try to capture, the more questions you ask, the more cues you observe.

It’s easy to apply this to consulting but it applies to any business owner trying to capture a portrait of the buyer in some moment in space and time, such as when they’re auto-filling a payment form with credit card details from a password manager.

There’s always more to see.

.. is asking each other the right questions.

I have a worked on a list of questions over the years. At times they were informal and half-remembered, at times they were in a notepad, or Google Forms (bleh).

Maybe 100 questions have cycled through. Most are transactional or straightforward but some rise to the level of “strategy trigger questions”.

For example:

• What do you know about your customers that hardly anybody else knows?
• How do you think things should be done differently in your area?
• What do you and your customers both agree on about your industry or market?

By now these questions and I are on close terms. Sometimes they even come out in casual conversation.. ” what do you know about your dog that no one else does?”

But ultimately, questions like these are one-trick ponies and not worth as much as the art of question-asking.

By themselves such questions get you maybe one small volley of insight. They’re “starter” questions because they’re only really valuable if they ignite an exchange. Like kindling vs logs.

The exchange affords the chance to ask a better question, one that is personalized specifically to what someone has just said.

Asin in the 5 Whys approach, which works pretty well, albeit moreso with simplistic problem discovery:

• What’s the problem?
• I can’t find a good designer?
• Why can’t you find a good designer?
• Because..

Etc.

It’s not unlike Gestalt Therapy; focus on what surfaces.

Holding that focus and asking the right follow-up questions is where the value is: if you do it well, you can find excellent messages to put on your website.

A “read-in” is jargon for the preparation a TV, radio, (or podcast) show host makes before having a guest on. The more they read-in on their guest, the better their questions and conversation – and the more they avoid over-asked questions. In short the better the show.

If the TV host does the read-in the day before the show..

– and the elite copywriters reads 7 times more material than he thinks he needs before writing a word

– and Teddy Roosevelt reads an entire book about each white house guest, before their stay

– and the strategy consultant organizes discovery interview topics into themes, cross-verifies them, SWOTs, gap-analyzes, problem-defines, and visualizes, before making a single recommendation

– and the chef performs an hour of mise-en-place chopping before turning on a single burner

– and the NBA player watches 3 hours of tape, before a playoff game

– and the hathic yogi warms joints, gently stretches, surya namaskars, pranayama-breathes, before assuming any asana postures.

If all that is true, what does automated, self-service product to do prepare for its user – before trying to help them?

Or is that work done by the time the product is released?

Yesterday, we looked at Sam Altman’s “no-product-market-fit” comments on ChatGPT plugins.

Let me de-jargonize that discussion: his idea was that ChatGPT users don’t use plugins because they don’t like them. Whereas my suspicion is that people do like plugins, but if they don’t use them, it’s because they make it harder to use ChatGPT.

Maybe there’s a design solution to that problem.

But there’s a bigger takeaway: OpenAI wants to be a platform for software makers – not a software maker itself.

It’s entirely possible or likely that people at OpenAI know very well they could figure out how to make plugins much more useful. But the bigger point is that maybe this doesn’t fit the overall corporate strategy.

He actually made this point directly: OpenAI will avoid competing with their customers — other than with ChatGPT”.

He coupled assertion with this observation: “a lot of people thought they wanted their apps to be inside ChatGPT but what they really wanted was ChatGPT in their apps”

The latter part, at least, is true. And how will OpenAI further that goal?

He offered a product roadmap for 2023:

• cheaper and faster GPT-4
• longer context windows (even up to a million, though that seems doubtful)
• easier fine-tuning API, per community feedback
• stateful API (conversation memory)

With the possible exception of faster GPT-4, these are meaningless to most ChatGPT users. They’re designed to help developers make better generative AI products.

Of course, there are no guarantees. And if developers decide what they really want is a plugin-economy app store, I’m sure OpenAI would accommodate them.

But it looks like standalone generative AI products won’t face direct competition from OpenAI anytime soon – they want to be the platform.

… is UX: they interfere with the basic experience of using ChatGPT.

In an interview with Raza Habib of Humanloop archived here, Sam Altman says (paraphrased): “The usage of plugins, other than browsing, suggests that they don’t have product-market-fit yet”

But I think there could be PMF. The blockage is UX – ironically for a product which is otherwise super simple to use.

Here’s the bigger list of issues with plugins:

• Most of them don’t work as advertised – or just don’t work at all
• Most of them take too long to return results
• Many of them require you to do many things outside of the ChatGPT UI, which is annoying
• Many of them duplicate what ChatGPT can do natively – and do so in a worse way
• But the big problem: they disrupt the ChatGPT UX

To be clear, I think the concept is great and there are several plugins I like. For example:

• ChatWithGit
• ChatWithWebsite
• daigr.am
• Scraper
• AskYourPDF
• Metaphor (<– this is one is great, BTW)

But even these disrupt the iterative flow of conversation.

It’s like when you’re in an enjoyable, animated conversation and the other person says: “hang on, I have to look that up on my phone”.

Then two minutes of silence later: “shit I can’t find it”.

Prompting is part priming the model but it’s also priming your own mind – the current plugin UX cuts both short.

Many businesses love data analysis, comprised of the big, little, and even tiny actions customers take. This is great for making cool-looking reports.

But now we can layer in words analysis, thanks to generative AI. It’s something like sentiment analysis but bigger and more fluid.

Take for example the words that often come before actions: complaints. I hear you can find them on the Internet.

We hate, despite, loathe, are sick of, feel sickened by, or annoyed by. We feel agony and pain, we laugh with disgust of disbelief, and we regret that our time, our day, or even our life was wasted by such a stupid company. While our trust was abused, our intelligence insulted, and our money was taken.

Like with this Kaggle-dataset customer-support chat: “Just wanted to warn people not to waste your time with Delta’s “Best Fare Guarantee” that they supposedly offer. I went through all the steps, had a perfectly valid claim, on the same day, and was denied. This was their response…”

In marketing, you scan for genuine, emotionally-charged complaints like the above and echo them back near verbatim:

Sick of perfectly valid claims being denied – and your time wasted?

Product-idea Sidebar: a product that uses your account to log into the Slack or Discord channels of competitors, to proverbially scrape the bitterness of their customers. (Ethically questionable, I admit).

Speaking of products, we love UI behavior analysis – analytics and product metrics like CTA, CPCs, page scroll depth, usage time, day-of-month churn trends. And I admit, there’s valuable insight there.

But there’s also value in words analysis – a task that any AI-enabled product can now perform as well as a marketing consultant, with the right training.

When you use LLMs to code, especially if you’re a sh*tty coder like myself, you might feel beset upon by the Garbage-In/Garbage-Out principle. My experience here is mostly in:

• Copilot/Starcoder
• ChatGPT with GPT3.5/4
• OpenAI API tools usually on GPT3.5
• Claude+/Claude-100k

BTW, coding is where LLMs most impress. I say that having made 15,000 prompts a month since last fall.

But I feel that my LLM-coding experience is unusual – partly because I’m so low-level; it’s hard to evaluate the code and advice LLM gives me. In contrast, I can detect value from LLMs as they predict, categorize, critique, ideate, graph, write, etc.

But also, I’m not the “ideal customer profile” – I’m a generalist full-stack designer/developer, not an actual professional developer. I just want my tool to work, be simple to use, and be secure – I care nothing about things like efficienc, scalability, or other best practices that don’t directly benefit my current users.

But the LLMs are OBSESSED with software development best practices.

Why? Because the authority-nerd-majority prevails over the content on which major LLMs develop foundational knowledge. (For example, 11 million C4 tokens are from StackOverflow.) And as professionals, of course these people obsess over efficiency and scalability – and “separation of concerns” (eye-rolling hard right now). And so LLM coding assistants inherit that bias.

But  just because a bias for professional software development best practices is painful for me, and wastes my life, doesn’t mean it’s objectively bad. Engineers do need to make sure buildings don’t collapse and personal data is secure.

I’m just using the wrong tool for the job.. albeit the best available.

LLM products have 4 places, IMO, where they can take in “garbage”:

1. Pre-training, as on the C4 cited above
2. Training/fine-tuning
3. Hardcoded prompting (from product owner)
4. Non-hardcoded prompting (from product user)

But here’s the thing – one man’s garbage is another man’s treasure: the challenge will be affordably creating generative AI that works for you and enough others like you.

According to this Pew study from last week:

• 18% of U.S. adults have heard a lot about ChatGPT
• 39% have heard a little
• 42% have heard nothing at all

I’m tempted to leave it at that, because for me contemplating that stark reality is a thought exercise.

But if you extrapolate a bit, you arrive here:  just 4% of US adults find ChatGPT useful.

So what’s going on here – how can a technology that the vast majority of people find non-useful be more revolutionary than anything ever, except maybe the technology that got our knuckles off the ground for good (rock tools)?

I think the answer is that it can be that revolutionary useful but in good time, like with the web. Generative AI and machine learning in general may even reshape our economy and society, maybe even our genetic evolution.

But it’s another case of the future being here but unevenly distributed.

Yes, generative AI rips machine learning out of the graspy talons of the programmer and puts it in the lap of the knowledge worker – but that’s still a pretty small segment of society.

Maybe the 4% number is also another case of social media feeds drowning out understanding.

For most in product is, one way to proceed here is to ask: how do I leverage LLMs without relying on the web app called ChatGPT?

The explicit definition of strategy that I shared earlier is brittle. Sure, it applies to most of my use cases, but definitions like this will eventually break if you apply them to enough situations. You shape them to meet your needs.

But connotative definitions are hard to control.

The connotation of strategy is basically something like “smart“.  It’s not that you can always interchange the two words, but there’s a strong association.

• Strategy consulting = giving smart advice
• Strategic planning = making a smart plan
• Strategic hiring = being smart about hiring
• Strategic investments = making smart investments
• Strategic planning software = smart planning software

This is why it’s dangerous to rely too much on the word – firstly, it’s over-used. Secondly, it basically amounts to you claiming to be smarter, or have smarter software, or a smarter approach. Or whatever.

That can come off as boastful, even glob, and it fails to compensates for not providing specific reasons why your solution will be worth more than it costs.

Ironically then, using the word that connotes smart might not be that smart.

On the other hand, if you can offer specific narratives, and at least hold an explicit, meaningful definition of what strategy/strategic means in your use case, inserting it into your messaging can help you sell.

Marketing to your team and  marketing to yourself have this in common with marketing to investors: tell stories which reveal that your product creates more value than it cost.

Salesforce CEO Marc Benioff has always been great at all three.

For example, early on he convinced himself that software should be delivered 24/7 over the Internet when most thought this was crazy.

He’s also good at marketing to the team. For example, one story he tells is about an “Ohana” (Hawaiian for family) made up of the Salesforce’s hundreds of thousands of employees, partners, developers, customers, etc – everyone has their role in the family and knows its traditions. This conceit has survived multiple layoff rounds over the years; there’s still a certification in it.

Investors are too disinterested to be part of the Salesforce Ohana but Benioff markets well to them anyway.

On a recent quarterly earnings call, he announced the release of Einstein GPT and teased the imminent releases of Slack GPT and Tableau GPT.

He packaged the product roadmapping with concrete profits and a grandiose “prediction”.

The coming wave of generative AI will be more revolutionary than any technology innovation that’s come before in our lifetime, or maybe any lifetime. Like Netscape Navigator, which opened the door to a greater Internet, a new door has opened with generative AI, and it is reshaping our world in ways that we’ve never imagined.

Actually, that’s not a prediction; it’s an implication that Benioff wants current and would be investors to ponder: Salesforce fully grasps, is passionate about, and will capitalize one the newest wave of technology.

Don’t know if it’s true or not, but altogether it’s a good story – and there’s a similar version for the kitchen-table investor.

Let’s say you become adept at “marketing to yourself”. Meaning telling yourself the right story. Who is next guy” to market to?

Interesting point made by a reader: how important it is to have people on your team that represent other ‘yous’ to market to”.

I couldn’t agree more – that person on your team is the “next guy” to tell the story to.

As for who makes up your team, well that’s another story. Suffice it to say, we all have a team of some kind, even the soloists. And there’s a way to tell that story to everyone on the team. It’s been like this with us people for at least 4 million years.

It’s easier when the team member built or marketed the thing with you, or was somehow intricate to it.

I experienced that on consulting teams over and over again. You tell the stories of why your solution makes more sense. You practice, critique, prune – and then the narrative hits harder when it shows up in the outside world.

For example, witness the CEO of data-visualization app Tableau ​skillfully discussing data literacy​:

“Data literacy is becoming a basic job skill of the 21st century – so we are really interested in helping people learn how to use data…

Tableau is a canvas for thinking and often when you have a canvas for thinking it is often like reading a good book. It’s not that the book finishes with a how-to list for how to live your life, that’s not the point.

The point was the thoughts that occurred to you while you were reading..

To get that level of storytelling fluency, marketing to each other helps.

Who is the most important person to market to?

You.. well, part of you at least.

This is closely related to “write for yourself”, “make what you like”, “cook what you love to eat”, or “scratch your own itch”.

Marketing is not the same as making but it should proceed from the same impulse

One of the interesting things about making a product is that if you don’t like it, you have a Big Problem.

Especially when you’re doing it by hand. If I invest 45 minutes cooking dinner, I better like it. If invest 450 hours building a product, I better love it.

I want it to speak for me, reflect me, and generally “be delicious”. And I think it will be. I think it’ll offer the right person more value than it costs, in time and money.

So why would I hesitate in giving it to that person? Why would any of us hesitate?

Speaking for myself,  because part of me wants it to be valuable and “perfect” – something it can’t possibly be.

Fortunately, another part of me wants it to be valuable and imperfect, even if lots of people observe its imperfections.

Marketing to the most important person, you, sort of equates to choosing to:

• talk to the part of you that’s ok with the inevitable imperfection of what you’re making
• tell the right story to that person – why what you do/make is more valuable than what it costs
• put the right amount of daily energy and frequency into the above efforts

If you get good at that, you could be great at marketing to the next guy.

Who doesn’t love progressive disclosure  in our long ass multi-step forms, long essays, and Audible books – one easy to understand section at a time. As well as “progress indication” – so we know where we’re at.

Turbotax does this superbly well with its multi-step forms – we aspire to that standard over at Message Maps.

There’s a lost art to breaking something down into lots of palatable pieces – people were better at it in the 1800s judging from the chapter structure of Moby Dick (185 chapters) and many others.

And not just “chapter 1”, “chapter 2” – no, there unique and descriptive titles for each chapter. Just like a well designed LLM prompt, you don’t waste words; each is a chance to provide more context.

Seth Godin’s book This Is Marketing brings back the old style – it has 235 chapters and sections (and they’re on Audible too – which is rare).

The addendum to this post, below, is the chapters themselves of this fascinating book – enjoy.

(And tell me if you know who the last chapter is about).

This Is Marketing

How Tall Is Your Sunflower?

It’s Not Going to Market Itself

Marketing Isn’t Just Selling Soap

The Market Decides

How to Know if You Have a Marketing Problem

The Answer to a Movie

Marketing Your Work Is a Complaint on The Way to Better

Chapter One: Not Mass, Not Spam, Not Shameful . . .

The Compass Points Toward Trust

Marketing Is Not a Battle, and It’s Not a War, or Even a Contest

The Magic of Ads Is a Trap that Keeps Us from Building a Useful Story

On Getting the Word Out (Precisely the Wrong Question)

Shameless Marketers Brought Shame to the Rest of Us

The Lock and the Key

Marketing Doesn’t Have to Be Selfish

Case Study: Penguin Magic

You’re Not a Cigar-Smoking Fat Cat

It’s Time

Chapter Two: The Marketer Learns to See

Marketing in Five Steps

This Is Marketing: An Executive Summary

Things Marketers Know

Chapter Three: Marketing Changes People Through Stories, Connections, and Experience

Case Study: Vision Spring—Selling Glasses to People Who Need Them

Consider the SUV

That Riff About the Quarter-Inch Drill Bit

People Don’t Want What You Make

Stories, Connections, and Experiences

Market-Driven: Who’s Driving the Bus?

The Myth of Rational Choice

Chapter Four: The Smallest Viable Market

What Change Are You Trying to Make?

What Promise Are You Making?

Who Are You Seeking to Change?

Worldviews and Personas

Forcing a Focus

Specific Is a Kind of Bravery

Shun the Nonbelievers!

Where Does Love Lie?

“Winner Take All” Rarely Is

A Simple One-Word Transformation

Coloring the Ocean Purple

“It’s Not for You”

The Comedian’s Dilemma

The Simple Marketing Promise

Case Study: The Open Heart Project

Chapter Five: In Search of “Better”

Empathy Is at The Heart of Marketing

A Million-Dollar Bargain

Thinking About “Better”

Better Isn’t up To You

The Marketing of Dog Food

Early Adopters Are Not Adapters: They Crave the New

An Aside About the Reptile People Who Are Secretly Running Things

Humility and Curiosity

Case Study: Be More Chill—More than One Way to Make a Hit

What’s a Car For?

Too Many Choices

Positioning as a Service

Choose Your Axes, Choose Your Future

So Many Choices

People Are Waiting for You

Your Freedom

The Freedom of Better

One Last Thing About Sonder

Chapter Six: Beyond Commodities

Problem First

Does It Work?

The Commodity Suckout

“You Can Choose Anyone, and We’re Anyone”

When You Know What You Stand For, You Don’t Need to Compete

But Your Story Is a Hook

Case Study: Stack Overflow Is Better

Better Is up to the Users, Not up to You

And We Serve Coffee

The Authentic, Vulnerable Hero

Service

Authenticity versus Emotional Labor

Who’s Talking?

Chapter Seven: The Canvas of Dreams and Desires

What Do People Want?

Innovative Marketers Invent New Solutions that Work with Old Emotions

Nobody Needs Your Product

No One Is Happy to Call a Real Estate Broker

Where’s the Angry Bear?

What Do You Want?

Always Be Testing

Scrapbooking

If You Had to Charge Ten Times as Much

Irresistible Is Rarely Easy or Rational

Chapter Eight: More of the Who: Seeking the Smallest Viable Market

The Virtuous Cycle and Network Effects

The Most Effective Remarkability Comes from Design

And Then a Miracle Happens

A Thousand True Fans

But What About Hamilton?

What Would Jerry Do?

Taylor Swift Is Not Your Role Model

All Critics Are Right (All Critics Are Wrong)

Why Don’t People Choose You?

Chapter Nine: People Like Us Do Things Like This

Deep Change Is Difficult, and Worth It

People Like Us (Do Things Like This)

Case Study: The Blue Ribbons

The Internal Narrative

Defining “Us”

Which Us?

It Shouldn’t Be Called “the Culture”

Just Enough Art

Case Study: Gay Marriage in Ireland

Elite and/or Exclusive

Case Study: Robin Hood Foundation

The Standing Ovation

Roots and Shoots

Chapter Ten: Trust and Tension Create Forward Motion

Pattern Match/Pattern Interrupt

Tension Can Change Patterns

What Are You Breaking?

Tension Is Not the Same as Fear

Marketers Create Tension, and Forward Motion Relieves that Tension

Are You Ready to Create Tension?

How the Status Quo Got that Way

Chapter Eleven: Status, Dominance, and Affiliation

Baxter Hates Truman

It’s Not Irrational; Status Makes It the Right Choice

Status Roles: The Godfather and the Undertaker

Status Lets Us

Case Study: Lions and Maasai Warriors

The Status Dynamic Is Always at Work

Status Is Not the Same as Wealth

Six Things About Status

Frank Sinatra Had More than A Cold

Learning to See Status

Different Stories for Different People

Affiliation and Dominion Are Different Ways to Measure Status

Learning from Pro Wrestling

The Alternative to Dominion Is Affiliation

Fashion Is Usually About Affiliation

Sending Dominance Signals

Sending Affiliation Signals

Affiliation or Dominance Is up to the Customer, Not You

Chapter Twelve: A Better Business Plan

Where Are You Going? What’s Holding You Back?

Perhaps You’ve Seen the Shift

A Glib Reverse Engineering of Your Mission Statement Isn’t Helpful

Chapter Thirteen: Semiotics, Symbols, and Vernacular

Can You Hear Me Now?

What Does This Remind You Of?

Hiring a Professional

Imagine that World . . .

Why Is Nigerian Spam so Sloppy?

The Flags on SUVs Are Called Flares

The Flag Is Not for Everyone

The Same and the Different

Case Study: Where’s Keith?

We Add the Flags with Intent

Are Brands for Cattle?

Does Your Logo Matter?

Chapter Fourteen: Treat Different People Differently

In Search of the Neophiliacs

Enrollment

What Do People Want?

The Superuser

The Truth About Customer Contribution

What’s the Purpose of This Interaction?

Chapter Fifteen: Reaching the Right People

Goals, Strategy, and Tactics

Advertising Is a Special Case, an Optional Engine for Growth

More than Ever, but Less than Ever

What Does Attention Cost? What Is It Worth?

Brand Marketing Makes Magic; Direct Marketing Makes the Phone Ring

A Simple Guide to Online Direct Marketing

A Simple Guide to Brand Marketing

Frequency

Search Engine Optimization and the Salt Mines

Chapter Sixteen: Price Is a Story

Pricing Is a Marketing Tool, Not Simply a Way to Get Money

Different Prices (Different People)

“Cheap” Is Another Way to Say “Scared”

And What About Free?

Trust and Risk, Trust and Expense

Be Generous with Change and Brave with Your Business

Case Study: No Tipping at USHG

Chapter Seventeen: Permission and Remarkability in a Virtuous Cycle

Permission Is Anticipated, Personal, and Relevant

Earn Your Own Permission and Own It

Tuma Basa and RapCaviar

Showing Up with Generosity

Transform Your Project by Being Remarkable

Offensive/Juvenile/Urgent/Selfish Is Not the Same Thing as Purple

Suspending Fight Club Rules

Designing for Evangelism

Chapter Eighteen: Trust Is as Scarce as Attention

What’s Fake?

What’s Trusted, Who’s Trusted?

The Trust of Action

Famous to the Tribe

Public Relations and Publicity

Chapter Nineteen: The Funnel

Trust Isn’t Static

You Can Fix Your Funnel

Funnel Math: Casey Neistat

The Sustainable Direct Marketing Funnel

An Aside on Funnel Math

The Truth About Your Funnel

Life on the Long Tail

The April Fools’ Passover Birthday Easter Shirt

There’s a Way Out

Bridging the Chasm

Where’s Your Bridge?

Surviving the Chasm

You Might Not Find the Bridge

Case Study: Facebook and Crossing the Biggest Chasm

Crossing the Local Chasm

Clean Water in a Local Village

An Aside About B2B Marketing

Chapter Twenty: Organizing and Leading a Tribe

It’s Not Your Tribe

The Power of Now, Not Later

Manipulation Is the Tribe Killer

Shared Interests, Shared Goals, Shared Language

It Will Fade if You Let It

Take a Room in Town

Chapter Twenty-One: Some Case Studies Using the Method

How Do I Get an Agent?

Tesla Broke the Other Cars First

The NRA as a Role Model

Getting the Boss to Say Yes

Chapter Twenty-Two: Marketing Works, and Now It’s Your Turn

The Tyranny of Perfect

The Possibility of Better

The Magic of Good Enough

Help!

Chapter Twenty-Three: Marketing to the Most Important Person

Let’s imagine that there are 5 ways to grow a business.

• PLG – Product-led growth
• MLG – Marketing-led growth
• SLG – Sales-led growth
• CSLG – Customer-success led growth
• ELG – Engineering-led growth

Which is best? I’ll cut right to the punchline: instead of picking one, you probably want a blend.

The wealthy of Silicon Valley love PLG and ELG. It lets them leverage access to capital, access to amazing product talent, a love of product metrics, and patience. You focus on nothing but building an amazing product, iterate for years, patiently waiting for worth of mouth adoption.

In reality most VC-funded startups grow on SLG and MLG – but they at least they like the idea of product-led growth.

I like it too – I appreciate focusing on the product.

PLG and ELG are the approaches to product growth that will be most enhanced by generative AI, which allow for much more rapid product improvement.

But the two approaches above are expensive and not realistic for most B2B solutions providers.

Those kinds of businesses traditionally relied on SLG, which requires skillful, intelligent, and socially brave people to sell, one-on-one, day after day.

This is the most underappreciated of the three approaches. Like the similarly personality-powered CSLG, which focuses on extreme customer happiness after the sale, this focuses on one-to-one human relationships. Thus both will be slightly less transformed by generative AI than the other approaches.

SLG is the most derided- maybe that because people are secretly too scared to sell. So they default to the go-to: marketing-led growth. Social media, content marketing, digital advertising, SEO. This is the one that will be most disrupted by generative AI, especially SEO.

Anyway – why the rundown? What’s my point?

Most people need to escape the mental trap of assuming that marketing-led growth is the only path forward. There are lots of options!

In sales and marketing related to technology, with solutions based on features, products, frameworks, plugins, approaches, concepts, APIs, datasets and platforms, a naming or labeling pattern has emerged.

Specifically, it’s easier to sell solutions if:

• the name contains an easy-to-grasp “instant idea” that you can riff on easily – and can later attach to more profound, strategic thinking
• their names themselves have a ring to them; plus, they often sound good when shortened

Sometimes more of one than the other but that’s the basic albeit ridiculous formula.

There’s an allure to them that feels like a promise.

Case in point – when my firm designed and sold custom solutions based on Drupal and Salesforce, I remember introducing a module to a client called “Organic Groups” to counter the problem of Facebook Groups luring away customer from their own platform. The name Organic Groups:

(a) suggested the  pleasantly natural evolution of affinity groups
(b) hinted at organic lead generation (“organic growth”, “organic traffic”)
(c) was nice to say on calls; it was easy to shorten to ‘Groups’ too.

In reality, Organic Groups was fatally flawed and a waste of time and money 8 times out of 10 – but its name held a promise that kept it alive for a bit.

Same with sales and marketing concepts like “Unique Selling Proposition”.

It’s a crisp idea based on a premise that you can easily bullshit on in a sales call – that buyers remember just one thing. And if you shorten it down to 3 letters, USP rolls off the tongue quite nicely – like IBM, NBA, MVP, etc.

The fact is though, that the person who coined USP, Bill Bernback, never produced any evidence that buyers “remember just one thing”. He just made it up.

The real challenge is coining a term with the properties discussed – but for something that actually delivers on its promise.

When you’re building a new product, feature or message, “best practices” are often “worst practices”.

It’s different when you’re building an aircraft carrier. At least nowadays it is.

Originally, when the British and US Navies first started building aircraft carriers in the 1920s and 30s, there was constant experimentation. They were even given autonomy to experiment – early attempts at holacracy.

But by the 1960’s, the system was fairly codified and plan-driven.

The first chapter of the PMP book, the sort of “best practices” manual for project management, claimed that the US Navy “invented” project management, by which they meant massively complex, multi-year plan-driven project management.

I highly doubt this claim – I mean, the pyramids of Egypt were built 4,500 year ago.

But the concept of best practices, wedded to project management, has become popular in US business culture and beyond.

For example, it’s central to the design of ChatGPT, to the frustration of users who don’t need constant best-practices-nagging – which make it tedious to extract useful information.

Best practices are also applied to marketing. For example:

• abiding by a “content marketing calendar”
• always putting periods after headlines (which make people stop reading, for many reasons)
• never putting periods after headlines

Look, if you have a sinecure at a massive corporation, then best practices like these might work for you. Because your firm gets business through maintaining its grip on media, government, distribution, etc. Look at Google’s supposedly fruitful 20% policy – it’s produced nothing for 20 years, except for perhaps psychological benefits. And for most people in companies big and small, best practices make sense to cling to. For example, Jacob Nielsen has codified some very useful UX best practices.

But for people making new things based on new ideas, it pays not to accept best practices without serious scrutiny.

In messaging, boring is something stationary and circular; if it’s not moving an idea or a message from one person to another, or not moving someone to act, it’s boring.

And unfortunately boring is not a very profitable quality.

But boring has another shade of meaning that’s related to personal characteristics.

Just as being interesting is generally related to being interested, being boring is generally related to being bored.

If there’s a takeaway here, it’s to find a way to be very interested, and seldom bored, by whatever it is your messaging applies to.

That’s probably a more sound approach than just trying to write better.