Post Control Marketing

When I struggle with something changing in the world I often turn to books. I’m an avid reader with diverse interests.

Right now, I’m looking at my bookshelf to try and figure out what I perceive as a significant shift in the discussion around AI. To understand my view on AI, it may be helpful to revisit where I’ve been.

My view on AI hasn’t always been the same. It has shifted over the years, but I’ve always tried to navigate my way towards the middle ground.

My 3 1/2-year journey with AI.

In December 2022, I first used ChatGPT. AI wrote a scary good blog article on social media marketing (my expertise) in less than a minute — something that would take me hours.

By December 2024, I spent two years writing about AI without using AI in my writing process ending every post with “100% Human Created.”

By Fall 2024, my LinkedIn feed felt full of criticisms of how woefully behind professors were. We weren’t doing enough to prepare students for the future. In 2024 LinkedIn added the “Enhance with AI” button encouraging users to use AI in writing their posts.

I decided to better understand AI I needed to use it in the research and writing process of my blog posts – a thing I do frequently. How can I help professionals and students navigate AI without firsthand experience discovering where it can be helpful and where it may be harmful?

In Spring 2025, I first used AI more directly to write about AI. I wanted direct experience with the new reasoning models to see how they “think” and to help me better understand AI agents and how they may impact our jobs and our students’ jobs.

That began my journey this past year in using AI to assist in the writing process. It’s been an experiment and I’m learning where AI can genuinely be helpful and where it can be harmful for experienced professionals and for students first learning their profession. I began ending every blog post with various forms of “AI assisted in this post…”

Beyond that first experiment in 2022, I haven’t used AI to write an entire post. My background is an advertising copywriter and I write as a professor. The last thing I want is AI to replace writers and the writing process. Yet, I’m also a realist and know employers will increasingly seek professionals and students who know how to use AI properly to improve their work.

When I don’t know or understand something I force myself to try it and write about it. In the process I learn. That’s what I’ve done with AI.

The shift in the wind?

But its felt different in my LinkedIn feed recently. The calls to “use AI or your behind” seem to have been replaced with “use AI and you’re a fraud.”

What feels like almost overnight, LinkedIn removed their “Enhance with AI” button for writers and replaced it with a new “Seems like AI slop” button for readers.

I know there’s an all AI generated content problem. I know it’s wrong to have AI create or help create content without disclosure. But I’m not sure encouraging us to report each other of suspected AI use is the solution. Some writers are intentionally adding typos out of fear.

Nearly two years writing about AI without AI assistance and just over a year writing about AI with various forms of AI assistance. I have learned a lot of where that line should be which I will be writing about more in the future.

I’m still learning by writing — trying to figure out the jagged edge of AI help versus AI harm. Maybe I’ll find better ways to use it. Maybe I’ll decide to use it less. Perhaps I’ll decide not to use it at all for certain tasks.

I don’t have it all figured out. One thing I do know is the answer is somewhere in the middle — somewhere between “woefully behind” and “AI fraud.” I also know I would not have learned if I was afraid to use AI at all.

“History never repeats itself, but the Kaleidoscopic combinations of the pictured present often seem to be constructed out of the broken fragments of antique legends.” – Mark Twain & Charles Dudley Warner in The Gilded Age: A Tale of Today.

What I have I learned from my bookshelf?

First, S.I. Hayakawa’s book reminds me that the language we use matters because Language directs Thought and Action. He also reminds me that most things in life are not all evil or all good (two-value orientation). AI be both beneficial and harmful on a scale of how it’s used (multi-value orientation).

More recent books raise questions. Are we to be AI First or is AI Snake Oil? Ethan Mollick introduced me to AI as Co-intelligence. Is it or are we on a slippery slope to no-intelligence?

Ray Bradbury gets me wondering if we’re sounding the alarm on each other – sending out the Mechanical Hounds.

Neil Postman gets me wondering whether we’re destined for Technopoly – where efficiency and technological progress replaces human judgment and wisdom.

Then I see Ross Gay’s book of essays. I fondly remember his in person reading where everyone in the room was visibly moved. Can we come together, even in this unprecidented time of change, to Incite Joy?

Maybe the winds aren’t as strong or shifting as I perceive. My view is skewed by social media algorithms that emphasize extremes for engagement – even on LinkedIn.

Perhaps I need more time with my books and less time in my social media feed? And that thought brings me to the final question that came to mind while looking at my bookshelf:

Are we wearing AI stars or removing them?

Human written. Em dashes & other “AI tells” are mine.

We spend a lot of time talking about how to use AI more effectively.

But what if one of the most valuable AI skills turns out to be knowing when not to use it?

As AI becomes better at generating text, images, code, and analysis, the temptation is to use it for everything. The harder skill may be recognizing the moments when speed should give way to human judgment, firsthand experience, creativity, or genuine connection.

AI literacy isn’t just learning better prompts. It’s learning where AI belongs in your process and where it doesn’t.

AI Felt Unlimited, But The Limits Are Starting To Show

For many users, AI has felt frictionless. You type a prompt, and the answer appears. Maybe you pay a monthly subscription. Maybe your university or company provides access. Maybe you use a free version and only occasionally hit a limit. The meter was mostly invisible.

But the meter was always running. Free users are seeing more, “You’ve reached your free messages.” Businesses are blowing through AI budgets faster than expected.

The real economics behind the helpfull chat boxes are becoming harder to ignore.

Every prompt uses tokens. Every long conversation, file upload, agentic workflow, image request, or coding session uses compute. For now, many of those costs have been hidden, subsidized, bundled, or absorbed by companies trying to grow adoption. But that may not last long.

In his newsletter, Christopher S. Penn put token economics in blunt terms. He estimated that a $200/month Claude Max 20 plan can provide roughly $8,000 worth of token output or a 97.5% discount. No business can sell its product at that discount forever.

AI has felt unlimited because someone else has been absorbing much of the cost. But that may beginning to change.

A TechCrunch article described companies starting to balk at the price of AI as token usage grows. Uber reportedly burned through its 2026 AI coding budget by April. Microsoft pulled back some developer access to Claude Code months after enabling them.

There is an environmental side to this, too. The compute behind AI has energy, water, and infrastructure costs. Using AI more efficiently is not only better for budgets. It is also part of using the technology more responsibly.

The Hidden Cost of “Use AI”

Need ideas? Use AI. Need a summary? Use AI. Need an email? Use AI.

Need a spreadsheet formula, campaign plan, research question, presentation outline, or first draft? Use AI. Sometimes that’s the right choice. But “use AI” can also become too frictionless.

The danger is losing the pause and questions that used to happen before work began.

What am I trying to do? Do I understand the problem? Or is AI even needed? What should I think through myself first?

If the person using AI doesn’t understand the underlying work, will a more powerful model help produce better work, or simply produce a polished version of weak thinking they’re not able to judge anyway?

That last question matters for higher education. Students need to learn how to use AI, but they also need to learn how to work when AI is not available, not allowed, too expensive, or not right for the task. If student learning depends too much on AI they may price themselves out of the jobs they’re preparing for.

When Skills Depend on a High Token Budget

A student who can only produce good work with unlimited access to the most powerful AI is not as AI-ready as they may think. They may be AI-dependent.

This applies to basic communication, too. If students never learn how to write a clear email, organize a simple slide deck, summarize a source, or explain a recommendation, they may have to spend tokens every time they communicate.

They’re not more efficient because they know AI. They become more expensive because they depend upon AI.

This will matter more as organizations start managing AI costs more carefully. Employers may not always give every employee unlimited access to every model. They may expect employees to know when a task requires, simple AI, advanced AI or no AI.

Business Insider reported that Coinbase CEO Brian Armstrong is trying to keep AI costs roughly flat while token usage grows by routing prompts to cheaper models to match the task to the right level of AI. That’s not only a technical skill. It requires understanding the work.

Someone who knows the subject can often get a useful answer from a smaller, cheaper model because they know what to ask, what context matters, and how to judge the output. Someone who doesn’t understand the work may need a more powerful model, more attempts, and more tokens just to get close. In that sense, human skill becomes part of AI efficiency.

Tokenmaxxing was short lived, the next metric will be tokenminning.

Prompt skills may matter more when AI is no longer treated as free and unlimited.

Prompting Is Not a Substitute for Knowing

On the other hand just knowing prompting skills isn’t enough. Shallow AI literacy says: “I know how to prompt.”

A deeper version says: “I understand the work well enough to know when AI can help, how to ask for that help, what kind of model is needed, what the output is worth, and when I should do the thinking myself.” That’s the skill employers will be seeking and higher education should be developing.

In marketing, that might mean knowing when AI can help generate alternative versions of a positioning statement but not replace the hard work of the human insight into the market and target audience.

In advertising, it might mean using AI to explore headline territories but still knowing which line has a real idea behind it or will resonate with how people really think or talk.

In research, it might mean using AI to summarize possible sources while still opening the article, reading the methodology, and deciding whether the evidence supports the claim.

In design, it might mean using AI to produce quick visual directions while still grounding the work in human observation, user needs, constraints, and context.

The important skill is not using AI. Its knowing what kind of human judgment the task requires before deciding what kind of AI assistance is useful.

The Most Expensive Answer Is Not Always the Best Answer

One reason this matters is that more AI does not always mean better work.

A study, “How Do AI Agents Spend Your Money? Analyzing and Predicting Token Consumption in Agentic Coding Tasks”, found AI agents can consume vastly more tokens than simpler AI workflows. Agentic tasks can use up to 1,000 times more tokens than code reasoning and code chat, and that higher token use doesn’t always translate into higher accuracy.

It’s easy to assume more AI means more capability. But more is not always better.

Sometimes a smaller model with a better prompt is enough. Sometimes a search engine is enough. Sometimes a conversation with a colleague is better. Sometimes sitting with the problem for ten minutes is the fastest path to a better idea.

That may sound old-fashioned, but it’s also practical. Burning AI tokens on a task a simple Excel formula could handle is not maxing your value to your employer.

If AI becomes part of the cost structure of work, using the most powerful model for every task may become like ordering Surf and Turf for every meal. Eventually someone notices the bill.

A Guide To Using The Level Of AI V2

Click on the image to download a PDF of this guide. Graphic created by ChatGPT.

Human Plus AI, Not AI for Everything

AI should amplify what students and professionals can do, not become the only way they can do it. That means the best AI users won’t be the ones who use the most AI. They’ll be the ones who know when to think, when to prompt, when a smaller model is enough, and when the work should stay human.

The same caution I offered writers and readers applies here. Don’t abdicate your human discernment to AI companies that have incentive to make AI feel frictionless, indispensable, and unlimited.

As AI becomes more powerful, organizations will measure it more closely. The people paying for these tools will want to know whether AI is saving time, improving quality, reducing risk, or simply moving cost from payroll to tokens.

That is why AI literacy needs to be taught in the classroom and on the job. Not just how to use AI, but when to use it, how much to use, and when not to use it. And that takes human judgment.

AI use disclosure: The central idea for this post was mine. It came from thinking about AI writing, AI detection, and the growing cost of token-based AI use. I used ChatGPT as a thought partner to develop the structure, test the higher-education angle, and identify possible supporting sources on AI token costs, model routing, and agentic AI workflows. I opened and checked sources before using them. I then rewrote, reorganized, added, cut, and edited the draft to reflect my own experiences, views, and voice. The stories, examples, and final decisions are my own.