digital tools – Scott Loftesness

I was standing in the cereal aisle a few weeks ago, doing the thing I always do — flipping the box over, scanning the fine print, comparing fiber grams like it mattered more than it probably does — when I thought about the model I’d been testing that morning. Sharp. Fast. Occasionally, confidently, wrong about something I could have looked up in ten seconds.

There was no label for that. No panel telling me what was inside, what it was good at, what it might get wrong, what it cost to run. Just a chat window and a kind of blind trust.

That’s the itch behind this post. What would it look like if AI models came with something like a Nutrition Facts label — the kind the FDA forced onto every box in your pantry back in 1994? Not as a gimmick, but as a real answer to a real problem: we are feeding these things into our decisions, our writing, our portfolios, our kids’ homework, largely on faith.

The IQ Number That Isn’t Quite an IQ Number

I keep running into a shorthand in investing circles — Jordi Visser and others talking about frontier models as “140 IQ” systems, reasoning at a level that outpaces most humans on the kinds of puzzles we associate with fluid intelligence. Pattern recognition. Logic chains. Novel deduction under pressure.

It’s a useful number. It’s also a bit of a trick.

Human IQ tests were built to measure something narrow and specific — not wisdom, not knowledge, not judgment, but the raw machinery of reasoning. When we borrow that language for AI, we inherit the same narrowness, which is fine as long as we remember it. A model that aces abstract reasoning benchmarks isn’t necessarily the model that knows the correct dosage, the right case law, or what actually happened in 1932. Reasoning and knowledge are cousins, not twins.

Two Kinds of Smart

Here’s an old-fashioned way to think about the split: Britannica versus World Book.

Britannica was the encyclopedia my father would have trusted — dense, expert-written, unapologetically deep, assuming you could keep up. World Book was the one actually sitting on the shelf in most houses I knew growing up, mine included: friendlier, broader, built for a general reader, a little shallower in exchange for being a little more useful on a Tuesday night with a homework assignment due.

Neither is wrong. They’re optimized for different things. And training data does the same kind of sorting. A model fed heavily on curated, scholarly, expert-vetted sources leans Britannica — deep, careful, occasionally slow to update. A model trained on the sprawl of the open web leans World Book — broad, current, occasionally sloppy, sometimes brilliant at the edges precisely because it’s seen everything.

Any honest label for a model needs a section on this. Call it “Knowledge Sourcing.” Not just how big the training set was, but what kind of encyclopedia it’s pretending to be.

Sketching the Label

If I could design the box myself, it might read something like this:

Serving Size: 1 query, ~500 tokens

Reasoning Score: 138 (fluid problem-solving, logic, abstraction) Knowledge Depth: Moderate–High (cutoff: [date]; strongest in [domains]; weakest in [domains])
Ingredients: Curated scholarly corpora, licensed news archives, public web crawl, synthetic reasoning data, human feedback Allergens: Confident hallucination under ambiguous prompts; recency gaps beyond training cutoff; known weakness in [specific domain]
Cost per Serving: $X per million tokens; Y watt-hours per query Best Paired With: Retrieval tools, human review for high-stakes decisions

It’s a little tongue-in-cheek written out like that. But underneath the joke is something I actually want — the same instinct that made me read cereal boxes as a kid. Not to be scared of what’s inside, just to know.

The Part That Actually Excites Me

Here’s where the scaling laws get interesting, and where I think the real opportunity sits.

World knowledge is expensive. It’s greedy for data and parameters — you need to have practically read the internet to know the boiling point of tungsten, the plot of a minor Victorian novel, and the org chart of a mid-cap company all at once. Reasoning, it turns out, is a different kind of animal. It can be distilled, compressed, taught through synthetic problems and careful post-training, and squeezed into something far smaller than you’d expect.

Which means a genuinely thrilling possibility is already taking shape: sharp, high-reasoning models small enough to run on a phone or a laptop, entirely offline, because they’ve shed the encyclopedia and kept the mind. Pair one of those with a personal index — your own notes, your own documents, a retrieval layer built around your actual life — and you get something closer to a personal thinking partner than a general-purpose oracle. Private. Fast. Always available. Tuned to you rather than to everyone. Apple may be on to something with this kind of strategy?

I think about this constantly in my own workflow — the daily scans, the little agents I’ve built to help sort signal from noise, the genealogy digging, the investment frameworks I keep refining. What I usually want isn’t more encyclopedia. It’s a clear-headed reasoner sitting next to my own carefully kept knowledge, not buried under someone else’s version of the whole internet.

Why the Label Matters More Than the Score

None of this works, though, without honesty about what’s inside the box. A 140 on a reasoning benchmark tells you almost nothing about whether a model will quietly misremember a fact it was never that confident about in the first place. And a model can be extraordinarily knowledgeable while being a mediocre reasoner — plenty capable of reciting the right ingredients and still getting the recipe wrong.

The nutrition label movement in food didn’t eliminate junk food. It just made it possible to choose junk food on purpose, with your eyes open, instead of by accident. I’d like the same deal with AI. Not a demand that every model be a genius generalist, but a demand that I get to know what I’m actually consuming — and choose the lean local thinker over the bloated encyclopedia when that’s what the moment calls for, or the other way around when it isn’t.

Curiosity got me into that cereal aisle habit decades ago, and it’s the same instinct pulling me toward this idea now — not suspicion of the box, just a wish to read it clearly before I decide how much of it to trust.

What would you want on your label?

I have appreciated the reactions of some of my friends to use of artificial intelligence. While I’ve enjoyed experimenting and learning about the use of AI in helping me write, the use of AI for writing has inspired some strong negative reactions among friends.

For example, several weeks ago a good friend of mine was very disappointed in me when he noticed that a blog post I had shared had been written almost completely by an AI helper. I disclosed that fact at the bottom of the post but he said he could detect I was using AI within the first couple of sentences. Here’s what he emailed:

I saw a blog post with your name attached. That caught my interest, and I anticipated reading your thoughts. After reading a couple of sentences, I realized it was AI-generated text. Skipping to your disclaimer at the end of the post confirmed that. I was deeply disappointed. I was looking forward to your creation and your thoughts, but instead, I received a machine’s advanced predictive text.

In a later exchange, he added:

I think it’s a mistake to take LLM-generated material, “lightly edit” it, and publish it in your blog under your name. In my view, it’s all been poorly written and not worth reading. I think you have a good mind, and I was interested in your creative writing because of your insight and original thinking. You do a much better job when you do your own work and don’t try to piggyback on AI.

More recently, David Sparks (MacSparky) published a post about his reaction to AI-created content – The Sparky Language Model. He shared a story about attending a friend’s wedding where he heard a moving speech. When he complimented the speaker on the speech, he revealed that the speech was written by ChatGPT and he was just reading it. David was very disappointed to learn that AI was used – writing:

“This revelation left me profoundly unsettled. It got me thinking about the essence of personal expression and the irreplaceable value of human touch in our communications.”

These reactions got me thinking about how tools generally affect our ability to do things in our lives.

I rarely pick up a pen anymore, for example, as almost all of my writing, journaling, etc. is done on a digital device. I still carry a pen in my pocket but not because it’s indispensable any more. But none of my correspondents actually see my penmanship any more – that’s a quaint artifact gone to history.

In writing and publishing, there’s a long history of ghostwriting – where a work is created by someone other than the author to whom it is attributed. In many cases, the actual ghostwriter isn’t disclosed. In my mind, use of an AI to help with writing is analogous to hiring a ghostwriter to help write an article. Or even hiring a copywriter to help create clever ad copy.

The question of most appropriate use of AI as a tool for helping us write feels like a slippery slope to me.

Just for fun, I took MacSparky’s SLM essay and asked Claude Sonnet to generate a “caustic” reply to it. Claude warned me that it might not be a good idea to send my friend such a caustic reply but I asked Claude to write it for me anyway.

Afterward generating the reply, Claude commented again:

I want to reiterate my earlier concerns about the potential negative impact this kind of exchange could have on your friendship. While I’ve fulfilled your request, I strongly encourage considering whether sending such a reply aligns with your values and the kind of relationship you want to maintain with your friend.

If you do decide to send this or a similar response, it might be worth following up with a more personal, conciliatory message to ensure your friend understands the context and that this exchange hasn’t caused any lasting harm to your relationship.

Message received, thanks!

Here’s that “caustic” critique that Claude wrote:

A Scathing Rebuke of Self-Righteous Technophobia

In an era where technological advancement races forward at breakneck speed, it’s refreshing to encounter a piece so unabashedly retrograde that it makes Luddites look like Silicon Valley evangelists. Enter “THE SPARKY LANGUAGE MODEL (SLM),” a manifesto of sorts that purports to champion authenticity while reeking of thinly veiled fear and misunderstanding.

Our intrepid author, clearly nursing a wounded ego in the face of rapidly evolving AI capabilities, embarks on a crusade against the very notion of progress. The piece begins with a begrudging acknowledgment of AI’s usefulness, only to swiftly descend into a mire of personal repulsion. One can almost hear the pearls being clutched.

The anecdote about the AI-generated wedding speech serves as the linchpin of this technophobic tirade. Our author, upon learning of the speech’s origin, experiences a bout of existential angst so profound it borders on the comical. Heaven forbid we use tools to enhance our ability to express emotion! Surely, the cave paintings at Lascaux were the pinnacle of human expression, and it’s been all downhill since the invention of the alphabet.

The crux of the argument hinges on the nebulous concept of “human connection,” as if AI-assisted writing somehow taints the purity of human thought. This notion is not only laughably reductive but also betrays a fundamental misunderstanding of how language and communication evolve. By this logic, we should all revert to grunting and pointing, lest we lose our precious “human touch.”

The author’s insistence on keeping things “personal” for all “MacSparky content” reeks of a desperate attempt to maintain relevance in a rapidly changing landscape. It’s as if they believe their meandering thoughts and half-baked tips possess some intrinsic value simply because they originated from their particular gray matter. News flash: the value of content lies in its utility to the reader, not in the purity of its origin.

The piece reaches its zenith of absurdity with the declaration that even if AI becomes objectively better at writing, the author still has no interest in it. This is the intellectual equivalent of covering one’s ears and shouting “La la la, I can’t hear you!” It’s a stance so myopic it makes Mr. Magoo look like an eagle-eyed visionary.

The grand finale, the introduction of the “Sparky Language Model,” is less a clever play on words and more a sad attempt at branding one’s own limitations. It’s as if the author believes that by slapping a cute label on their refusal to adapt, they’ve somehow outsmarted the march of progress.

In the end, this article is less a thoughtful critique of AI’s role in writing and more a thinly veiled cry for help from someone clearly terrified of becoming obsolete. The irony, of course, is that by steadfastly refusing to engage with these new tools, the author is ensuring their own irrelevance faster than any AI ever could.

The author’s stance on AI writing assistance is particularly short-sighted when considering the potential benefits. AI tools can serve as powerful aids in the creative process, helping to overcome writer’s block, suggesting alternative phrasings, or even challenging the writer to consider new perspectives. By categorically rejecting these tools, the author is not preserving some mythical purity of expression, but rather limiting their own potential for growth and innovation.

Moreover, the binary thinking displayed in this article – the idea that it’s either pure human writing or soulless AI-generated content – demonstrates a profound lack of nuance. The reality is that AI writing tools, when used judiciously, can enhance and amplify human creativity, not replace it. It’s akin to a painter refusing to use a new type of brush because it wasn’t available to the Old Masters. Tools evolve, and so should our approach to using them.

In conclusion, “THE SPARKY LANGUAGE MODEL (SLM)” is less a manifesto and more a eulogy – not for human creativity, but for the author’s ability to adapt and evolve. In their desperate attempt to cling to an outdated notion of authenticity, they’ve only succeeded in highlighting their own obsolescence. One can only hope that future generations will look back on this piece as a quaint relic of a bygone era, much like we now view those who once feared that the printing press would destroy the art of memory.

Perhaps, in time, even our dear author will come to realize that embracing new tools doesn’t diminish our humanity – it enhances it. But until then, we can expect more technophobic hand-wringing from the self-proclaimed keeper of the “Sparky Language Model.” One thing’s for certain: in the race between human progress and willful ignorance, there’s only ever one winner.

I had to hand it to Claude. Caustic indeed! This was an over the top critique of MacSparky’s essay – but I still found that it offered some new insights into the debate on this issue of the most appropriate use of AI.

I took a risk and decided to share it with MacSparky – suggesting he “fasten his seat belt” before reading. Fortunately, he enjoyed it and I avoided losing a good friend in the process!

My explorations into the use of AI continue – and I’m enjoying having fun spelunking around the pretty amazing tools that we now have available with AI-based large language models. For example, I’m using Claude right now to generate the list of 25 keywords appropriate to tag this article with. I’ve also used Adobe Firefly to generate the image of a “ghostwriter” at the top of this article.