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09/18/2026

Most AI projects do not fail because they were wrong. They fail because nobody could explain them.

Researchers at MIT and the self-driving company Motional built a method called CW-Net. It makes a car's decision-making model explain itself in plain words, like approaching stopped vehicle or close to cyclist, at the moment it acts.

Drivers who saw those explanations got better at predicting what the car would do next. The team ran it on a real car on real roads, not only in simulation, and it did not slow the driving down.

The real prize is in the researchers' own framing. Real-time reasoning lets you test a system while it is running, so you find out the model is wrong before it costs you something.

That is the pattern we see over and over. Accuracy is not usually what kills an AI project. Nobody being able to say why it produced what it produced is what kills it.

Full breakdown in the newsletter. Link in bio.

09/18/2026

Foam spilling over both hands, running down the knuckles, bubbles everywhere. Something is definitely happening. None of it is going anywhere useful.

That image is what the first three months of AI adoption looks like inside most companies.

The volume arrives almost immediately. That part genuinely works. Suddenly there are more drafts, more variants, more analysis, more options than anyone asked for, and it all spills straight past whatever process existed before. Teams mistake the spill for progress because there is visibly more of everything than there was last quarter.

Then the reviews start, and nobody can say what changed. Not because the tools failed. Because output grew and decision-making did not.

Here is the part we end up saying in almost every engagement. Volume is the easy part now. It is close to free. Control is the work, and control is not a tool you buy, it is a set of decisions somebody has to actually make. Where does this go. Who approves it. What do we stop producing. Which of these forty variants is the one we are willing to put our name on.

The difference between a spill and a pour is not the amount of liquid. It is whether somebody decided where it was going.

What we look for when we walk into an organization is not whether they are generating enough. They almost always are. It is whether the decision layer scaled alongside the production layer, and it almost never has. That gap is where the frustration lives, and closing it is unglamorous work: naming owners, setting review points, agreeing what good looks like before the output exists rather than after.

None of that requires new software. All of it requires someone senior to sit down and decide.

Are you producing more than you are deciding?

There is usually one person on a team who has already been using AI quietly for months and has told nobody. They are wai...
09/18/2026

There is usually one person on a team who has already been using AI quietly for months and has told nobody. They are waiting to find out whether it is safe to admit.

That is the bottleneck in most organizations. Not the technology, not the budget. Permission.

The cheapest way we know to grant it is a meeting setting.

Turn on take notes for me in your own leadership meeting first. Not a team meeting. Piloting AI on other people's time reads as a mandate; piloting it on your own reads as a signal, and the difference in how it lands is enormous.

Then announce it out loud. Name the tool, say why you turned it on, and say where the notes are going. Ten seconds is plenty. Silent AI in a meeting makes people suspicious about what is being captured and why. Announced AI makes people curious about how to do it themselves.

Set the norm before a bad habit sets itself. Notes capture decisions, owners, and dates. Nobody needs a transcript of a meeting they attended, and a wall of text will go unread like everything else.

Put one person on the output. They review and correct the notes before circulation. When we build systems for clients, we put a human review step on every AI output before it leaves the building, and this is that same rule at meeting scale.

Then reclaim something visible. Most recurring meetings spend their first 10 minutes recapping the last one. Delete that agenda item. The tool has now paid for itself in a way everyone in the room can feel, which matters far more than a productivity statistic.

Two weeks on, count how many other meetings turned it on without being asked. That number is your adoption signal, and measuring it costs nothing.

How much of your last meeting went to recapping the previous one?

09/17/2026

A company published how dangerous its own product is, then said it is shipping anyway.

OpenAI says its coming model, Astra, is the first to reach the Critical level for cybersecurity in the company's own safety framework. It scored 100% on ExploitBench, a test of building working attacks from known flaws. In expert testing it found flaws nobody had reported and chained them together.

The disclosure is real and OpenAI deserves credit for making it. But a safety framework that has never once stopped a launch is a labeling system, not a brake.

Here is what it means for you. The gap between a flaw existing and somebody using it used to be however long a skilled human needed to find it. That number is heading toward zero on both sides, and the attackers get there first because they do not run a safety review.

We keep an AI use policy that names which tools are approved and who checks their output, and this is the week it earns its keep.

Full breakdown in the newsletter. Link in bio.

The most useful thing you can do for AI adoption in your company is be visibly mediocre at it in front of your team.That...
09/17/2026

The most useful thing you can do for AI adoption in your company is be visibly mediocre at it in front of your team.

That runs against everything executives are trained to do. Project competence, show the finished thing, never look uncertain in front of the people who report to you. And it is exactly why so much AI advocacy lands hollow.

A leader who has never used the tool talks about AI in categories and benefits. A leader who has used it talks about what went wrong on Tuesday. Everyone in the room can tell the difference, and no amount of conviction closes that gap.

Here is what I would want every executive to understand. Somewhere in your company, someone has been using AI quietly for months and has told nobody, because they are waiting to find out whether it is safe to admit. Until somebody senior goes first, your early adopters stay quiet and their colleagues never learn from them.

That is not a training problem or a budget problem. It is a permission problem, and you are the only person who can solve it.

Personal engagement from the top sends a signal that no memo, no town hall, and no budget allocation can match. People do not calibrate on what leadership funds. They calibrate on what leadership does.

So this week, use AI on something you actually own, then show your team the task, the output, and the corrections you had to make. The corrections are where the value is, because they give everyone else permission to produce something imperfect and improve it.

When did your team last see you get something wrong, in public, on purpose?

09/16/2026

AI did not get cheaper this week. One specific way of using AI got much cheaper.

Anthropic released Claude Fable 5.1 on September 1. Base pricing did not move, staying at $10 per million input tokens and $50 per million output. What changed is cached input, the material a tool re-reads over and over, which fell from $1.00 to $0.25 per million tokens.

Anthropic says that makes typical work about 25% cheaper, and work running long agent tasks up to 45% cheaper.

Look at what they cut and what they left alone. That is not a discount, it is a bet on where the next few years live. It also happens to be the way of using AI that embeds deepest in your operations and is hardest to leave later. Worth knowing which side of that you are on.

Rerun your AI cost math this month. Something you priced as too expensive in July may not be anymore.

Full breakdown in the newsletter. Link in bio.

09/15/2026

Whatever you publish is training data. The federal government just put that in writing.

On September 1 the Justice Department filed a statement of interest in the copyright cases against OpenAI, telling a New York federal judge that training large language models on copyrighted writing counts as fair use. It argued the opposite ruling would hurt American innovation and national security.

This is the first time the federal government has taken a side on the merits. It does not bind the judge, and Judge Sidney Stein still decides. But the direction matters more than the ruling for a business your size, because suing was never a realistic option anyway.

So the question flips. It stops being how do I keep my work out of these models, and becomes how do I get credited inside their answers. That is why we build client content to answer real questions plainly rather than chase rankings.

Full breakdown in the newsletter. Link in bio.

09/14/2026

A clinic bought AI software. Your health premium went up.

Marsh surveyed more than 1,800 US employers and found health benefit cost per employee is expected to rise 8.2% in 2027, the biggest jump since 2003. Employers said their current plans would cost 11% more if they changed nothing at all.

One of the drivers Marsh named is AI-powered claims software. Doctors' offices are using it to submit more claims, and higher-level claims, than they did before. Meanwhile 59% of employers plan to cut costs somewhere in their benefits next year.

Every AI success story assumes the savings stay with whoever bought the software. This is the proof that they do not. When you read an AI efficiency claim, ask who is sitting on the other side of that transaction.

Full breakdown in the newsletter. Link in bio.

Every story this week is about the same two questions: who actually captures the savings, and who can see inside the sys...
09/14/2026

Every story this week is about the same two questions: who actually captures the savings, and who can see inside the system.

A clinic bought AI billing software and employee health premiums went up 8.2%. The Justice Department told a court your published writing is fair game for training. Anthropic cut one specific cost by 75% to steer where you build. When we put AI into a client's workflow, we name the person who checks the output and we make the system show its reasoning.

Read the full breakdown: https://aismartventures.com/posts/ai-strategy-insights/

There is a gap most leadership teams notice long before anyone mentions it out loud. The gap between an executive who en...
09/14/2026

There is a gap most leadership teams notice long before anyone mentions it out loud. The gap between an executive who endorses AI and an executive who uses it.

We close that gap with one exercise, and it takes about a week.

Start by picking a task you genuinely own. Not a demo, and not something invented for the purpose. The weekly ops summary. The board pre-read. The competitor roundup. Something recurring and slightly tedious, where a bad result would actually annoy you. Real stakes are what keep the exercise honest.

Then brief it the way you would brief a new hire. The goal. The constraints. What good looks like. What to do when it gets stuck. Most disappointing AI output traces back to a thin brief rather than a weak model, and executives are often the worst offenders here because they are used to delegating in half sentences to people who already know the context.

Hand it over and leave it alone while it works. Hovering turns delegation back into typing.

The review is where the learning happens. Do not accept the output and do not bin it. Ask for changes, exactly as you would with a person. The middle section is thin, add the regional numbers. That habit is the whole skill.

Keep a running list of what you corrected. Each correction becomes a line in next month's brief, and after three rounds most of them stop being necessary. That list is your real AI policy, written from experience instead of copied from a template.

Then do the part that actually moves your organization. Tell your team what came back wrong. Share the task, the output, and the corrections. Naming the failures gives everyone else permission to experiment, and nothing slows adoption down faster than a leader who only reports wins.

What recurring task would you hand over first?

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