Lecturing to Those AI Users Who Take What AI Outputs. Period.
August 28, 2026
Another dinobaby post. No AI unless it is an image. This dinobaby is not Grandma Moses, just Grandpa Arnold.
Let me boil down 2,300 words to a summary: Do the work. Yep, that’s what “The Missing Layer in AI-Assisted Research: Evidence Assurance” struck me as asserting. The problem, in my opinion, is unlikely to go away. Even if the US BAIT outfits go belly up and decay in the koi pond, the AI cat is out of the bag. Let’s take three examples I have encountered in the last month (today is August 23, 2026):
- At a local joint called El Nopal, I heard a group of Spanish speaking teens telling one another how smart software could take their Spanish language essays, fix them up, and then present them is English. How old were the teens? I estimated about 15, maybe 16. I remember the cluelessness I faced when I attended the local school in Campinas, Brazil, in the 1950s. If I had a smart software system to help me, would I have used it? In case you are struggling for my answer, here it is: “Of course I would have used it.” I spoke only English and school was 100 percent Brazilian Portuguese.
- A former CIA contact and I attended a local entrepreneur show-and-tell. There were nine speakers. Eight of them used Google Gemini to produce their “art.” One presenter just talked. I asked him who assisted him in putting together his remarks which were textbook 1960 speech class in structure. He said, “ChatGPT.” Okay, that sample rings the bell for 100 percent smart software enabled. By the way, the proposed start up ideas were in need of work.
- I needed a plumber. I made some calls. A registered fellow turned up. He fixed the lead. He had a nifty plastic wrap on his truck. I asked, “Who designed the graphic?” He said, “The plastic sign shop. I told the person at the counter what I wanted. She used some AI system. It printed out two graphics with my name, the slogan “We don’t drain your wallet,” and said, “Pick one. I did. Looks cool, doesn’t it?” Yep, cool.
Thanks, MidJourney. Good enough.
The write up asserts:
When AI research tools first arrived, I immediately saw their advantage as a way to socialize research at scale. Anyone can ask a question and get an answer in seconds, increasing their curiosity about our customers and how to better serve them. But that speed can bypass the methodological discipline market research has spent more than 80 years developing to make its findings credible: documenting where evidence came from, how it was collected, and how much confidence to place in it.
In my three examples, exactly zero cared about any verification or validation. The “good enough” AI is indeed pretty good for my three use cases.
The cited essay says:
Yet the person receiving the answer often has no simple way to know what the AI could actually see, which evidence it used, what it skipped, or how directly its conclusions trace back to primary research. The analyst, research team, and VP can all receive the same confident response without knowing how much assurance the evidence underneath it warrants.
The author is correct. But most users don’t care. If output comes from a computer, it is definitely more “right” than looking at a link and trying to figure out where the possibly incorrect factoid the search system user wants is lurking. That’s too much work for most people. Therefore, smart software is a work reducer. Anyone who wants to use AI for something serious knows that validation and verification are real work. Then people like the former head of the ethics department or the former president of Stanford just do the easy thing, emulating the students eating burritos at El Nopal.
The essay concludes in a series of statements that sound a great deal like my high school debate coach, Kenneth Harris. Ponder this passage:
I’m using the levels of Evidence Assurance to communicate when different degrees of evidentiary assurance are appropriate for market research and business decision-making. A researcher might use a Directional Skill for a low-stakes, repetitive task; a Grounded system when the answer needs to come from the actual research library; and an Auditable system when the evidence may need to withstand scrutiny from a client, executive, legal team, or another researcher.
You don’t need to become a market researcher or a data scientist to use AI well. The same questions apply here as in other research:
Where is the information coming from?
How confident should you be that the answer is accurate?
What are the consequences if the answer is wrong?
Research already has ways to answer those questions: sampling tells us where the data came from, margin of error how much confidence to place in it, and methodology what a finding can and cannot support. AI has made research faster while often stripping away those signals.
The only way to alter the widespread use of smart software, cause people like AI using lawyers to verify case law, and get academics to do academic-like work is to turn off AI. Flip the switch. Kill it.
We know that won’t happen; therefore, what’s the point of explaining the process of verifying and validating information. The horse had fled the barn. The barn burned to the ground. An AI outfit bought the land and will erect a data center where it once stood. Too bad for the horse, right, Pilgrim?
Net net: New rules will emerge. Who will enforce them? Essays won’t do the trick.
Stephen E Arnold, August 28, 2026
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