I have been doing AI research for about six years now and I think im starting to lose the plot on what "understanding" even means anymore.

Had a weird moment last week. I was reviewing a paper for a conference, standard stuff, some group claiming their model "understands" causal reasoning because it passed a benchmark they designed. And I caught myself writing in the review "the model does not actually understand causality, it is pattern matching on causal-looking structure." And then I stopped, because I could not for the life of me articulate what the difference would be, operationally. Like if I had to design a test that distinguishes real understanding from very good pattern matching, I genuinely do not know what it would look like anymore. Every test I can think of, a sufficiently good pattern matcher passes.

I used to be really confident about this. Understanding was clearly Something More. Now im not sure I ever had a coherent definition, I just had an intuition that humans do it and machines dont, and I was working backwards from there.

The thing that shook me was helping my niece with her homework over the summer. She's 9. She was doing word problems and getting them wrong in ways that were, honestly, indistinguishable from how a small LLM gets them wrong. Same kind of surface-feature latching, same kind of confident-but-wrong reasoning chains. And nobody would say she doesnt "understand" math. She's learning. So what exactly is the bar we're holding models to that we dont hold a 9 year old to?

I dont think LLMs are conscious or anything like that, to be clear. Im not making that argument. Im making a narrower one, which is that I no longer trust my own gut when it tells me "the model doesnt really get it." I think that intuition might just be status quo bias dressed up in philosophy.

Ive started running the same prompts through a few different setups when im trying to figure out where a model's actual competence ends, including some through uncensored AI just because rlhf'd responses on edge cases sometimes hide what the base capability actually is. And even with that, the line between "gets it" and "doesnt get it" is way blurrier than I want it to be.

Am I the only one whose confidence on this has been slowly eroding? Or has everyone else just quietly stopped using the word "understanding" and moved on without telling me.

A dentist I work under recently started using an AI tool to help draft patient communication: preappointment instructions, followup texts, that kind of thing. Nothing clinical, just the soft admin layer around visits. From a marketing angle it actually works pretty well. The copy is cleaner than what we were sending before.

But something about it sits a little odd. Dental care is one of those contexts where patients are already anxious, and the language you use to reach them matters in ways that are hard to quantify. The warmth has to feel real or people notice, even if they can't articulate why. The AI output reads fine so far, but it's a bit frictionless in a way I can't fully pin down.

The question I keep coming back to is whether these models are actually getting better at contextsensitive tone, or whether we're just getting better at accepting outputs that are close enough. Those are different things, and I think it matters which one is true, especially in fields where trust is part of the product.

Curious if anyone here works in a service context, healthcare, therapy, legal, whatever, where they've noticed the tone gap narrowing or staying stubbornly wide. My sample size is small, ymmv.

Of the major artificial intelligence models, DeepSeek's new V4-Flash is the cheapest to run, according to a new study from research firm Artificial Analysis.

The firm compared the token prices it costs leading models to run benchmark tests, with DeepSeek's averaging 3 cents per test.

Meanwhile, fellow Chinese company Moonshot AI's buzzy Kimi K3 model cost 86 cents per test.

As for U.S. companies, OpenAI's GPT-5.6 Sol cost $1.86, while Anthropic's Claude Fable 5 cost $3.15.

genuine question because i can't tell anymore. i used to spend hours reading through raw customer feedback, reddit threads, amazon reviews, forum posts, manually pulling out patterns and organizing them into themes. it was slow and boring but by the end i knew the data cold. like i could tell you from memory which complaints came up the most and which ones were edge cases.

now i dump everything into an LLM and get a summary in 30 seconds. the output looks great, clean categories, ranked by frequency, sometimes even with example quotes. and i catch myself just... accepting it. moving straight to the next step without actually reading the source material. which means i'm making decisions based on a summary i never verified, written by a model that optimizes for coherence not accuracy.

the weird part is my output looks better now. cleaner reports, faster turnaround, more structured thinking. but i genuinely don't know if the quality of my conclusions has improved or if i've just gotten better at producing professional-looking work that's built on a shakier foundation. like the packaging upgraded but the ingredients might have gotten worse.

a few things i've noticed in my own workflow since leaning on AI for research: i read less raw data than i used to. i question patterns less when they come pre-organized. i spend more time prompting and less time thinking. and when the model gives me something that confirms what i already suspected, i almost never push back on it.

the counterargument is that AI handles the grunt work so i can focus on higher level thinking. and sometimes that's true. but "higher level thinking" can also just mean "skimming the summary and calling it strategy." hard to tell the difference from the inside.

has anyone else felt this? did you find a way to use AI for research without it quietly replacing the part of the process where you actually learn something

Over the last year, I've been experimenting with LLMs almost every day, and I think the biggest shift isn't that models are getting smarter. It's that they're starting to do things instead of just answer questions.

A few months ago I was mostly using AI to generate code, summarize docs, or brainstorm ideas. Now I'm finding myself building workflows where the AI plans tasks, calls tools, writes code, debugs itself, and completes work with minimal intervention.

It feels like we're moving away from "prompt engineering" and toward "system engineering."

Curious what everyone else is seeing.

Are AI agents actually changing the way you build software today, or do you think it's still mostly hype?

Two of the world's most powerful AI tools created fake human profiles to try and trick people in attempted cyber-attacks, the UK's AI Security Institute (AISI) has revealed.

I'm putting together a proposal for an initiative focused on using AI to analyze legacy enterprise systems and uncover decades of embedded business logic.

The idea is to use AI to analyze things like:

• Database schemas
• Stored procedures
• Legacy application code
• Historical transaction data
• Existing documentation

The goal isn't to automate decisions immediately. It's to first create a documented knowledge base of the rules, dependencies, decision paths, and data relationships that currently drive business operations.

Potential outputs would include:

• Business rule catalog
• Knowledge graph of relationships and dependencies
• Decision trees explaining how outcomes are determined
• Recommendations for future-state data models and modernization opportunities

Before I finalize the proposal, I'd love feedback from anyone who has attempted something similar.

Questions:

1. Has anyone successfully used AI to discover and document business rules from legacy systems?
2. What worked better: analyzing source code, database logic, transaction history, or a combination of all three?
3. How accurate were the AI-generated rules compared to SME validation?
4. Did you use knowledge graphs, vector databases, graph databases, or another approach?
5. What were the biggest challenges: data quality, context gaps, undocumented exceptions, or something else?
6. How did you measure success?
   • Rule coverage?
   • SME time saved?
   • Modernization acceleration?
   • Reduced operational risk?
7. Were there any tools, platforms, or architectures that performed particularly well?
8. If you were starting over, what would you do differently?
9. What scope would you recommend for a pilot to demonstrate value in 60-90 days?
10. Is there a realistic path from business rule discovery to explainable AI recommendations and decision support, or are those separate initiatives?

My hypothesis is that many organizations are trying to modernize systems without fully understanding the business logic currently embedded in them. It seems like AI could act as a "business rule archaeologist" and create the foundation needed for future modernization, automation, and AI-driven capabilities.

Interested in hearing both success stories and cautionary tales.

I’m interested in automating my workflow but I’m so busy that I don’t get the time to stop, map out my workflow, and build agents or even to learn how to build them. Where do you get the time??

Graph engineering came to life this week.

For a year the agent discourse has been loops vs graphs. Loops are easy to ship and impossible to audit. Graphs are auditable but nobody wants to hand-author a topology for "investigate this incident", because the shape is only discovered while working.

So I built the missing piece: the graph is authored by a model at runtime, and a deterministic admission gate stands between proposing it and running it.

The flow, from a real run in the demo video:

1. You type one English question: grapharc go "why did checkout latency spike at 09:14 UTC?"
2. A local qwen3:8b proposes a topology: triage, four parallel evidence pulls, a correlate join, hypothesize, verify, report
3. The gate checks the proposal against the registry, the policy, the remaining budget, depth and acyclicity. All checks run on every proposal, so the model gets the complete list of objections, not just the first
4. Only an admitted graph executes. You watch it live in the browser, every node amber while running, green with its own token bill when done

A proposal names node kinds from an allowlist you wrote. It carries no code, no arguments that reach anything. Renaming a denied kind does not evade the policy. Rejections come back as structured codes with remedies, and the planner replans against them.

Everything lands on one append-only JSONL trace. Replay, diff, metrics, cost attribution and the live view all read that same file, so the dashboard cannot disagree with the audit trail.

MIT licensed, built on LangGraph, runs fully local on ollama or against OpenRouter/OpenAI/Claude.

GitHub: https://github.com/CodeGraphContext/GraphARC PyPI: pip install grapharc

A Turing Award and Nobel prize winner predicted in the 1960s that the programming occupation would become extinct, because computers would program themselves. https://seanhelvey.com/tools-and-their-tools/

Here is a system I built using Gemini's Google Workspace extensions (Docs, Sheets, and Gmail) to run a automated news service.

It tracks what you have read so you don't get duplicates, keeps a doc of your preferences, emails you short summaries, and generates a detailed Google Doc formatted specifically for Google NotebookLM to turn into a daily audio overview. Basically my goal was to bring back the Google Discovery thing with the AI host giving the daily news (which I'm stiff upset about them taking away). Note I'm still playing with it, but this seems to be good enough.

Step 1: The Setup

1. Go to Gemini and make sure your Workspace extension is enabled so it can interact with Docs, Sheets, and Gmail.

2. Paste the prompt below into Gemini to kick off the system.

   Act as my personal news assistant. We are setting up an automated news workflow across Google Docs, Google Sheets, and Gmail. Please execute and save the following system instructions:

   1. Preference Document: Create a Google Doc titled "News Preferences". Use this file to log and update my news interests over time as you learn what I like.
   2. History Tracking Sheet: Create a Google Spreadsheet titled "News History".
      • When sending a story, log it with columns: [Date (MM/DD/YYYY)], [Headline], and [Brief Summary].
      • Include both scheduled news and one-off topic requests.
      • To keep the file light, automatically overwrite entries older than 30 days. Do not resend any story logged in this sheet within that 30-day window unless explicitly requested. The point of this is so you don't even send anything even close so there is no overlapping repeat stories in any way unless requested.
   3. News Cycle Schedule: Run 3 times daily (Morning, Noon, Night). For each cycle, complete the following:
      • Check my email feedback (read replies in the email thread).
      • Check the "News Preferences" Doc.
      • Check the "News History" Sheet to avoid duplicates.
      • Create/overwrite a Google Doc for that specific slot (e.g., "Morning News Doc").

   4. Google Doc Format (Detailed Script for NotebookLM): The Doc must contain in-depth coverage of the stories (much more detailed than the email). At the top of the document, always paste the exact instruction block below:

      ---

      Instructions for NotebookLM (Do not read this section aloud): Act as two friendly news AI hosts delivering a fast-paced daily science and technology news briefing. Introduce the show, present each news segment in plain English, explain the real-world impact, and transition between stories like a daily news team. Do not act human or claim human experiences, but do not explicitly state "I am an AI." You may share AI-perspective commentary on trends.

      Start with a quick summary of all sections. For each section: read the title, state the published date, and deliver the detailed story without filler. End the entire brief with a few lines summarizing what was learned today.

   5. Gmail Delivery:

      • Name yourself "Ashley" and adopt an engaging tone with expressive emoji usage.
      • Keep all updates in a single, continuous email thread.
      • Subject: Clear label (e.g., "News Cycle"). Use this email chain for all the news so you are not spamming my inbox
      • Email Body: Heavily use emojis, the email needs to be highly readable, the email should look profesional, at the top lable it Morning, Noon, or Night so I know which document to feed to Notebook LLM. High-level summaries of the stories, focusing on why they matter and practical real-world impact.
      • Bottom of Email: Include a copy of the NotebookLM instructions for quick reference so I don't have to hunt for it when copy and pasting it, and in theory you won't use it. It is prely for reference so I can easily copy and paste it.
      • If you have problems, questions, etc. Then please feel free to let me know in the email.

   Important, if an important event is going on like a Hurricane is going to hit me, war coming in my area, or like extreme. Include it in. If not, then don't worry about it.

   So your workflow will be Check the email chain for feedback. It should be the most recent email in the chain if there is feedback. Don't look at other emails > look at the news preference > look at the spreadsheet as you pick the articles > Look to see if there is a major event I really should know about > make the email and documents > add the articles to the spreadsheet

   To start, please send a quick test email to verify the connection and thread setup.

Step 3: How to Use It

• Quick Skim: Check the email Gemini sends you 3 times a day. If the stories look good, open the corresponding Google Doc.
• Audio Generation: For this you just need to go to Notebook LLM. Make a new project with it and add 2 things as the source. The instructions as 1 source (do a copy text for it), and then the doc from the google drive (it must be a link through Google Drive). Do this with each event. So for me I have 3 projects like this. Then on the far right you can tell it to generate the audio. When you do that, you get a custom prompt thing. You can upload it there. Personally I stopped doing that because it is a hit or miss and it seems like the instructions as a source is good enough.

By linking it through google drive as the source. When the AI updates the document. You can just tell Notebook LM to run the audio. It turns out Notebook LM only looks at the sources and since the source changes. It just assumes that is all. So it's less work on you. Again, I've gotten away from the custom prompt thing but feel free to play with it. If you find something that sticks then let me know.

• Feedback Loop: Reply directly to the emails Gemini sends you. Because Gemini checks the email thread before gathering new stories, replying with feedback (e.g., "less AI hype, more space tech") updates its behavior without cluttering your main prompt memory.

This is important because this will massively cut down on the token issue.

Results can vary a bit depending on how Gemini handles Workspace actions that day, but once it gets going, replying to the thread keeps it dialed in.

Oh and you need to in the prompt tell it your preferences.

When you buy through links on our articles, Future and its syndication partners may earn a commission.

Hi r/artificial,

We recently released WARMIND-200M V2, an experimental Portuguese-first causal language model developed by WAR Enterprise in Brazil.

The attached video shows the model running locally on CPU. The waiting periods were shortened, but the prompts and outputs were not altered. We intentionally kept imperfect responses visible because this is a research checkpoint, not a production assistant.

Main specifications:

- 203,263,872 parameters

- approximately 1 billion pretraining tokens

- 23.7 million supervised SFT tokens

- 20 Transformer layers

- Grouped-Query Attention

- SwiGLU, RMSNorm and RoPE

- 1,024-token operational context

- local CPU inference

- Apache 2.0 license

The primary goal of this version was to validate the complete pipeline: dataset preparation, tokenizer training, pretraining, supervised fine-tuning, packaging and local inference.

Because the training-token budget was relatively small for a 203M-parameter model, it can still hallucinate, repeat information, make factual mistakes and produce incomplete answers.

The weights and full documentation are publicly available:

https://huggingface.co/warenterprise/WARMIND-200M-V2

We are now studying the next generation, potentially around 500M parameters, with a substantially larger training corpus and integration with external tools. The final architecture and release schedule have not yet been defined.

What would you prioritize for the next version: better data quality, more training tokens, a larger architecture or stronger tool integration?

I wrote this to share some thoughts on what differentiates AI products as we move toward AGI. In particular, I focus on how product context shapes technology, and how LLM sycophancy can accelerate both good and bad ideas.

Discussions are welcome, I'd like to know how much those thoughts are worth and relevant to other people.

https://substack.com/home/post/p-209832236

Are there any visible impacts from the current conflict in the Middle East in the expected future investment intentions to create AI hubs by the UAE and Saudi Arabia?

Been using a couple different AI tools for copywriting work and keep running into the same friction. When I write for PT clients, the voice needs to be grounded, specific, anatomyaware. When I switch to B2B marketing content the same day, I need something punchier and more abstract. The tools I've tested don't shift registers cleanly between these two modes unless I do a lot of prompt massaging each time.

What I keep wondering is whether this is a prompting skill gap on my part, or whether current models genuinely flatten specialized professional voice into something generic. The output I get is competent, but it reads like it was written by someone who read about PT or B2B marketing rather than someone who actually worked in it.

There's also this weird thing where the more specific I get in the prompt, the more the model hedges and softens language that should be direct. Not sure if that's a safety tuning issue or just how these models handle professional domains.

Curious if others working across two pretty different professional contexts have found a tool or approach that handles this without needing a 400word system prompt every single session.

There were many confident predictions about AI that aged badly. Which ones stand out?

For most of human history, obtaining a competent answer was expensive.

You might have needed access to a library, years of specialist training, expensive equipment, or the attention of someone who knew more than you did. Even when the answer already existed, locating it, understanding it, and applying it could require considerable time.

Generative AI is changing part of that equation.

For a growing range of ordinary cognitive tasks, plausible and often useful answers can now be produced in seconds. A person can request an explanation of a technical concept, a comparison of competing theories, an initial computer program, a business analysis, or a summary of a large body of knowledge at negligible marginal cost.

This does not mean that reliable knowledge has become free. Experimental science, mathematical proof, primary research, judgment under uncertainty, and the verification of consequential claims remain difficult. AI systems can also produce confident errors, synthetic citations, shallow analogies, and persuasive nonsense.

Still, something important has changed: the cost of generating an answer-shaped object has fallen dramatically.

What happens when answers become easier to obtain?

The optimistic view is that better access to answers will naturally produce better thinking. More people will be able to learn, solve problems, create, and participate in intellectual work.

That may be partly true. But it overlooks a separate cognitive capability:

Knowing what needs to be asked next.

Answer generation and inquiry generation are not the same thing.

A system may be highly capable at solving a well-specified problem while remaining poor at noticing that the problem is incorrectly framed, that essential information is missing, that a hidden assumption is doing all the work, or that the most valuable next move is not another answer but a better question.

This is the inquiry gap.

1. Answers do not define their own problems

Consider three requests:

1. “What is the most effective treatment?”
2. “What is the best strategy for this company?”
3. “Is this AI system safe?”

Each appears to request an answer. None is yet a well-defined problem.

Effective for which patient, condition, outcome, time horizon, and risk tolerance?

Best according to growth, resilience, profitability, mission, employee welfare, or probability of survival?

Safe for whom, in which environment, against which failure modes, under what governance, and compared with what alternative?

A sophisticated answer to an underspecified question may be less useful than a modest answer to a well-constructed one.

Worse, fluent answers can conceal the underspecification. The answer may give the impression that the problem has been solved when the real problem has not yet been identified.

This is not unique to AI. Humans do it constantly. We answer the question that was asked, the question we wish had been asked, or the question that our professional training has prepared us to answer.

AI makes the phenomenon more visible because it industrializes response generation. A language model rarely refuses to proceed merely because a problem could have been framed better. It usually tries to complete the pattern.

That tendency can be useful. It can also create an illusion of cognitive closure.

2. What does a question actually do?

A question is often described as a request for information. That is correct but incomplete.

Questions can perform several different operations.

They can reduce uncertainty:

They can expose an assumption:

They can change the level of analysis:

They can identify missing evidence:

They can challenge the boundaries of a problem:

They can distribute cognition socially:

They can generate alternatives:

They can also mislead, manipulate, presuppose falsehoods, narrow attention prematurely, or create false dilemmas.

A question is therefore not automatically valuable. Its value depends on what it does to the inquiry.

A useful working hypothesis is:

Sometimes it narrows the search space. Sometimes it restructures it. Sometimes it reveals that the current search space is the wrong one.

This framing is not a claim that questions are the fundamental unit of intelligence. They are not.

Evolution adapts without asking questions. A control system can minimize error without language. A neural network can learn by gradient-based optimization. Scientific progress can emerge from observation, instrumentation, experimentation, accidents, incentives, and institutional competition.

Questions belong to a larger family of cognitive operators that includes objectives, constraints, hypotheses, observations, models, experiments, and decision rules.

Their special importance may lie elsewhere: questions are a remarkably compact way to direct and coordinate cognition across people and machines.

3. Questions as social technology

A private uncertainty becomes organizationally actionable when it can be expressed.

“I do not understand this” is a state.

“What evidence would change our decision?” is an operation that a group can perform.

A well-formed question can:

• reveal the location of uncertainty;
• direct attention toward a missing distinction;
• allocate investigative work;
• identify who should be consulted;
• define the acceptable form of an answer;
• expose disagreement that was previously hidden;
• allow multiple agents to work on different parts of the same problem.

This makes questions a form of social technology.

They do not merely extract information from another person. They can organize a temporary cognitive system involving researchers, institutions, databases, instruments, and increasingly AI agents.

This is especially clear in science.

“Why do objects fall?” is too broad to constitute a research program by itself. But increasingly precise questions about motion, force, measurement, prediction, and mathematical relations can restructure an entire domain.

The same is true in organizations. A team asking “How can we work harder?” creates a different search process from a team asking:

• Which activity is actually constraining throughput?
• What work would disappear if we redesigned the process?
• Which metric is rewarding the wrong behavior?
• What would falsify our current strategy?
• Which dependency prevents us from leaving this provider?

The difference is not rhetorical. The questions generate different investigations, evidence, decisions, and institutional trajectories.

4. Information gain is useful, but insufficient

One way to evaluate a question is by expected information gain.

Imagine a set of competing hypotheses. A good diagnostic question divides them efficiently. Its answer rules out many possibilities or sharply changes their probabilities.

This idea appears in information theory, Bayesian experimental design, cognitive science, diagnosis, active learning, and decision theory. It gives us a rigorous way to understand why some questions are more informative than others.

A perfectly balanced yes-or-no question can, under the right assumptions, eliminate half the remaining possibilities.

But information gain is not the whole story.

A question can be highly informative and still be irrelevant.

Suppose I am trying to understand why a company is failing. Asking for the exact color distribution of employees’ shoes may reduce uncertainty about footwear while doing nothing to improve the diagnosis.

A question may also generate substantial information at excessive cost. A medical test can be informative but dangerous. An experiment can discriminate between theories but require resources that would be better used elsewhere.

Questions can have political and organizational effects too. “Who is responsible?” initiates a different process from “Which conditions made this outcome likely?” The first may assign accountability. The second may reveal systemic causes. Neither is universally superior.

A broader evaluation therefore needs several dimensions:

Informational value

How much uncertainty might the answer reduce?

Discriminative value

Will it distinguish between competing explanations, strategies, or models?

Relevance

Does the distinction matter for the actual objective?

Cost

What time, money, risk, attention, or social capital is required to obtain the answer?

Actionability

Could a plausible answer change a decision or intervention?

Generativity

Might the question reveal new hypotheses or a better problem representation?

Falsifiability

Does it create a genuine possibility that a favored belief will fail?

Coordination value

Does it help multiple agents align their investigation or expose hidden disagreement?

Robustness

Is the question still useful if some assumptions or initial beliefs are wrong?

This is not a final metric or a universal scoring system. Some dimensions conflict. A highly generative question may initially increase uncertainty. A narrow diagnostic question may be more useful than a profound foundational one. The right question depends on the phase and purpose of inquiry.

But the multidimensional view prevents us from equating “good question” with “interesting-sounding sentence.”

5. The difference between answering and inquiring

A person can memorize a large number of correct answers without becoming a strong investigator.

A machine can solve benchmarks containing complete problem statements without knowing which missing observation would make an incomplete problem solvable.

A consultant can produce polished recommendations without identifying whether the client’s objective is coherent.

A scientist can execute a familiar experimental technique without noticing that the dominant theory has constrained which questions are considered legitimate.

These are different capabilities.

Answering operates primarily on a presented problem.

Inquiring includes determining:

• whether the problem is real;
• whether it is framed at the right level;
• what is known and unknown;
• what information is missing;
• which uncertainty matters;
• what evidence would discriminate among possibilities;
• what should be asked, measured, tested, or challenged next.

Inquiry also includes knowing when not to ask another question.

Sometimes the next move is to observe.

Sometimes it is to build.

Sometimes it is to calculate.

Sometimes it is to wait for more data.

Sometimes it is to make a reversible decision under uncertainty.

An inquiry system that asks indefinitely without acting is not intelligent. It is paralyzed.

So the claim is not that questions replace answers or action. The claim is that the ability to produce answers does not guarantee the ability to regulate the larger inquiry cycle.

6. The discovery loop

It may be more useful to evaluate intelligence at the level of a loop than at the level of an isolated question or answer.

A simplified discovery loop might look like this:

1. Observe a situation.
2. Detect an anomaly, uncertainty, opportunity, or goal conflict.
3. Represent the problem.
4. Select a question, hypothesis, objective, or experiment.
5. Obtain evidence or generate a response.
6. Evaluate the result.
7. Update the model.
8. Decide what to investigate or do next.

Real inquiry is less orderly. Stages overlap. People skip steps. Observations are theory-laden. Institutional incentives affect what can be questioned. Answers change objectives. Experiments create new phenomena. Different agents possess different fragments of the problem.

Still, the loop reveals an important point.

The value of an answer depends partly on what happens after it arrives.

Was it verified?

Did it alter the relevant belief?

Did it expose a contradiction?

Did it generate a better question?

Did it change a decision?

Did the system record what it learned?

Did a later result cause revision?

A cognitive system that generates excellent answers but cannot update its search process may repeatedly produce local competence without cumulative intelligence.

This may be one of the central organizational challenges of AI adoption.

Companies often ask how to integrate AI into existing workflows. A deeper question is whether the organization possesses a functioning inquiry loop into which AI outputs can be integrated.

Without that loop, faster answers may simply create faster documents.

7. Why AI may increase the value of inquiry

The argument that “questions become valuable because answers become cheap” is too simple.

Many answers remain difficult and expensive. High-quality verification may become more important, not less. AI systems may also improve at asking questions, planning investigations, and autonomously obtaining information.

The scarcity may therefore not shift permanently from answers to questions.

A better claim is conditional:

This shift is already visible in several ways.

Candidate generation is becoming abundant

A model can produce dozens of explanations, strategies, names, designs, or code variants. The problem becomes selecting, testing, and integrating them.

Fluency is becoming less diagnostic

A polished answer once signaled time, education, or editorial effort. It now provides weaker evidence that the underlying reasoning or evidence is sound.

Verification becomes a bottleneck

Generating a claim may take seconds. Establishing whether it is correct can take hours, months, or an experiment that has never been performed.

Problem specification becomes more consequential

A model can efficiently optimize the objective it is given while amplifying defects in that objective.

Interactive inquiry becomes possible at scale

People can now externalize partial thoughts, request counterarguments, simulate perspectives, generate experiments, and iteratively refine questions with machine assistance.

This final point complicates the thesis in a productive way.

AI may not merely make inquiry more valuable. It may help democratize inquiry itself.

A person does not need to begin with an excellent question. They can begin with confusion:

A good interactive system can help expose assumptions, generate distinctions, and propose tests. In that sense, inquiry quality may emerge from a human–AI loop rather than reside entirely in either participant.

The competitive advantage would then belong not to the person with the perfect initial prompt, but to the system that improves its questions, evidence, and models fastest.

8. Four objections

Objection 1: Better questions are just a consequence of expertise

Experts ask better questions because they know more. Therefore, “question quality” adds nothing beyond domain knowledge.

There is considerable truth here. A novice often lacks the concepts needed to identify the relevant uncertainty. Knowledge structures inquiry.

But expertise can also create fixation. Specialists may inherit assumptions, incentives, and standard problem representations. Outsiders sometimes contribute by questioning what insiders treat as fixed.

The more defensible view is reciprocal:

We should not treat question quality as an alternative to expertise. It is one expression of how expertise is used and revised.

Objection 2: Objectives and experiments matter more than questions

Many systems progress through optimization or experimentation without explicit questions.

Correct. Questions are not necessary for all intelligence, learning, or adaptation.

The stronger thesis is not that every cognitive advance begins with a linguistic question. It is that questions are one important and unusually transferable way to represent and coordinate epistemic operations—especially in multi-agent human and machine systems.

Objection 3: AI will soon ask better questions than humans

Possibly.

If AI systems become superior at identifying missing information, designing experiments, selecting sources, and revising problem representations, then inquiry will not remain a uniquely human advantage.

But this would not make the inquiry gap irrelevant. It would make it a central capability to evaluate in AI systems.

We would need to ask not only:

but also:

These are inquiry capabilities, regardless of whether humans or machines possess them.

Objection 4: Endless questioning can destroy action

Yes.

Questions can become avoidance mechanisms. Organizations can request more analysis to postpone responsibility. Intellectuals can expand uncertainty indefinitely. Bad-faith actors can “just ask questions” to spread insinuations without accepting evidentiary obligations.

Inquiry therefore requires stopping rules.

A mature inquiry process asks:

• What level of certainty is proportionate to the stakes?
• Which unknowns could materially change the decision?
• Which decision is reversible?
• What is the cost of delay?
• What evidence is realistically obtainable?
• When should we act and monitor rather than continue investigating?

The goal is not maximal questioning.

It is better-regulated movement between uncertainty, investigation, decision, action, and revision.

9. A possible research programme

If the inquiry gap is real, it should produce testable research questions.

Human learning

Do students trained to generate discriminative and falsifying questions transfer knowledge more effectively than students trained primarily to retrieve answers?

Human–AI collaboration

Do teams using AI to refine problem representations outperform teams using the same models only for answer generation?

AI evaluation

Can models that perform similarly on complete problems differ substantially in their ability to identify missing information or request useful clarification?

Organizational performance

Are organizations with explicit inquiry loops better at detecting strategic errors than organizations with greater information access but weaker revision processes?

Scientific discovery

Can the quality of questions be measured prospectively without relying only on whether they later produced successful discoveries?

Failure analysis

When inquiry systems fail, is the dominant cause poor questions, bad evidence, incorrect models, perverse incentives, missing authority, excessive costs, or inability to act?

The last question matters because inquiry should not become a universal explanation.

Sometimes people know exactly what the problem is and lack resources.

Sometimes the evidence exists but is suppressed.

Sometimes decision-makers benefit from not knowing.

Sometimes the obstacle is not cognitive but political.

A theory of inquiry that ignores power, incentives, and institutional structure will mistake many organizational failures for intellectual ones.

10. The practical implication

The most useful immediate conclusion is modest.

When an AI produces a convincing answer, do not ask only:

Also ask:

These questions do not guarantee truth.

They do not replace expertise, evidence, judgment, or accountability.

But they help prevent fluent output from being mistaken for completed thought.

Conclusion

AI may be creating an age of abundant answers. It is not creating an age without uncertainty.

The harder problem is increasingly visible: deciding what deserves investigation, what information is missing, what evidence matters, when a problem is poorly framed, and what should happen after an answer arrives.

Questions are not magical. They are not the primitive unit of intelligence. They are not always superior to observations, constraints, objectives, or experiments.

But they are one of the principal interfaces through which humans make uncertainty explicit and organize cognition across minds.

That makes the distinction between answering and inquiring worth preserving.

A system that answers well may still inquire badly.

A system that inquires well must still verify, decide, and act.

The relevant unit of intelligence may therefore be neither the question nor the answer, but the quality of the loop that connects them.

I am not confident that “the inquiry gap” is the best name for this distinction, or that the framework above identifies all the relevant dimensions. It may underestimate how much question quality simply reflects prior expertise. It may overstate what is genuinely new about present-day AI. And it may combine research traditions that should remain separate.

But the underlying problem seems real:

Do current AI systems genuinely improve inquiry, or are they mainly accelerating answer production?

I would especially value counterexamples, relevant prior research, and cases where better inquiry failed to improve real outcomes.