The AI you’re using today is a totally different beast from the one you used yesterday – and that’s the main takeaway we’re diving into!

In this episode, we unpack the hidden mechanics behind those sneaky AI updates that can turn your reliable workflows into a confusing mess overnight. We chat about Heather's enlightening experience, where she realised the AI she’d trained to perfection overnight decided to throw a tantrum and ignore her carefully crafted instructions.

It’s a wild ride through the psychological toll of these silent updates, and we’ll explore how to keep your workflow on track amidst the chaos. So, grab a cuppa and settle in as we figure out how to build a resilient practice that won’t crumble with every sneaky tweak the AI developers throw our way!

This Deep Dive podcast is AI generated from the Start With AI Newsletter on LinkedIn - linkedin.com/newsletter/start-with-ai

The Details:

Navigating the world of AI can sometimes feel like trying to dance with a partner who keeps changing the steps!

In our latest episode, we delve into the complexities of AI updates and the resulting chaos they can wreak on our well-oiled machines. Inspired by Heather's experience detailed in the Start with AI newsletter, we explore the emotional and psychological impact of these abrupt changes.

One minute, you’re in rhythm with your AI, and the next, it’s like the music has stopped. We’ll share practical strategies for maintaining your sanity and workflow stability, including how to document your processes meticulously and conduct isolated tests when things go awry.

Plus, we discuss the importance of distinguishing between capability and dependability in AI tools. After all, you want a trusty sidekick, not a wild card!

Chapters:

• 00:07 - The Evolution of AI: What You Need to Know
• 00:41 - The Disorienting Changes in AI Workflows
• 04:34 - Understanding AI Feedback Loops and User Experience
• 09:26 - The Convergence of AI Platforms
• 14:01 - Mastering Calibration: A Practical Approach
• 17:51 - Understanding the Shift in AI Workflows

Takeaways:

• Today's AI is constantly evolving, meaning your reliable tools could change overnight without notice.
• The silent updates to AI can disrupt workflows, leaving users feeling confused and frustrated.
• When AI tools stop working, it's essential to step back and reassess the entire system, not just your inputs.
• Building resilience in your AI workflows involves version control and testing components in isolation.
• Understanding the distinction between an AI's capabilities and your workflow's reliability is crucial for effective use.
• Documenting your processes helps maintain clarity and control over your work, even as tools evolve unexpectedly.

Links referenced in this episode:

• linkedin.com/newsletter/start-with-ai

Companies mentioned in this episode:

• Start with AI
• Claude
• Anthropic
• OpenAI
• Microsoft Copilot

The AI you are using today, I mean, literally right now, is not the AI you used yesterday.

Yeah, not at all.

Literally.

And the truly terrifying part about that, the company that built it didn't even bother to tell you.

Right.

It's totally silent.

Yeah.

You log in one morning, you fire off this exact flawless process you spent like weeks tuning, and it just breaks.

Yeah.

Spectacularly.

And you just stare at the screen thinking, wait, did I break it?

Am I losing my mind?

It's so jarring because the interface looks identical.

Exactly like the chat window is the exact same color, the logo haven't moved an inch.

But the entity behind it is suddenly behaving like a total stranger.

I mean, it is incredibly disorienting.

That disorientation is exactly what we are tackling in this deep dive today.

Yeah.

By Heather over at Start with AI.

Right.

And the piece is titled I Trained My AI Then the AI Changed.

Such a great title.

It really is.

So our mission today is to explore the hidden mechanics of AI updates.

We want to figure out why your favorite tools suddenly stop working overnight.

Because they do.

They really do.

And more importantly, how you can build a resilient working practice.

So a secret under the hood model change doesn't completely derail your entire week.

That's just so crucial right now.

Okay, let's unpack this.

Because Heather's experience is basically a silent epidemic for anyone doing serious knowledge work right now.

It really is.

So for three sol months, Heather had this incredibly reliable workflow running with Claude.

Three months is a lifetime in AI.

Oh, yeah.

An absolute eternity.

She was using a pretty sophisticated setup, pulling data through core connectors and utilizing these custom files called DOT skills.

Okay.

For anyone unfamiliar, a DOT Skills file is essentially a customized rulebook that you feed into the AI.

Right.

It dictates highly specific instructions on how to format outputs, maybe what tone to use, or how to process certain types of data.

It ensures the AI doesn't just, you know, guess what you want.

It follows a very strict protocol.

Yeah.

And for a quarter of a year, that protocol ran like clockwork beautifully.

But then, during just one random week in July, it all fell apart.

Claude suddenly failed to connect through those cohort connectors.

It basically acted like it couldn't even see her skills files anymore.

Right.

The inputs were exactly the same, but the AI just ignored the rulebook completely.

And you have to understand, nothing on her end changed.

The files were identical, the tasks were identical, and Heather was The exact same person sitting at the keyboard.

But her first instinct, of course, was to blame herself.

Naturally, she audited her entire setup.

She repeated the configuration, you know, tested every possible human error she could think of.

Whoa.

That sounds exhausting.

It is.

And it actually took consulting an AI tech guru, Merch Harris, to finally zoom out and realize that her setup wasn't broken at all.

Wait, so what was it?

The foundational product underneath her setup had fundamentally changed.

Oh, wow.

I was trying to visualize how this feels, and it's like mastering a complex recipe.

Right.

Okay.

Yeah, you perfect this cake, but overnight, the oven manufacturer secretly changes how a degree is measured.

Oh, that's a good way to put it.

Right.

So suddenly, your cake is burning to a crisp, and you're just standing there blaming your own baking skill.

Exactly.

What's fascinating here is the psychological mechanism of how we learn to navigate these tools and why it hurts so much when the topography just shifts on us.

Yeah, in the source text.

Heather brings up this concept from NLP Neuro Linguistic Programming called the totte loop.

That's T O T E, Test, operate, test, exit.

Okay, walk us through what that actually looks like when you sit down to work on a Tuesday.

Sure.

So let's say you are using an AI to draft a complex legal contract.

You quote, unquote, test by writing an initial prompt.

Right.

Then you operate by letting the AI generate the text.

You test again by reading that output.

So maybe you notice the indemnification clause is way too broad.

Doubt it.

If it's perfect, you exit the loop.

If it's flawed, you adjust your prompt and you run the entire loop again.

So it's a constant cycle of iteration.

Exactly.

The entire utility of this framework relies on consistent feedback.

The AI's mistake teaches you how to write a better prompt the next time.

But if the rules of the feedback keep changing, you can't learn anything.

Every result is supposed to teach you something about your next action.

Precisely.

But that only holds true if the underlying conditions remain stable.

What Heather experienced was an invisible update just destroying that feedback loop entirely.

Because suddenly, familiar prompts produced entirely unfamiliar behavior.

Right.

Let's get into the hood here for a second.

Why does an update actually break a dot skills file?

I mean, Anthropic isn't going into their servers and maliciously deleting a user's instructions.

No, definitely not.

So what is happening mechanically to make the AI suddenly blind to a rulebook it read perfectly just yesterday?

Well, it comes down to how these updates are applied, specifically through processes like RLHF reinforcement, learning from human feedback.

Or sometimes it's the introduction of new safety guardrails.

When anthropic or OpenAI updates a model, they are often tweaking the internal hierarchy of what the model actually pays attention to.

Okay, so shifting its priorities.

Yes, if they decide the model needs to be, say, more concise across the board or safer, they adjust the underlying weights.

Just to clarify, for anyone who doesn't spend their weekends reading machine learning papers, when we talk about weights and parameters.

Right, sorry.

Think of weights and parameters as the model's actual mathematical brain structure is the billions of connections it formed during its initial creation.

Okay, that makes sense.

So when a company tweaks those weights to prioritize safety or conciseness, that new internal mandate can easily overpower your custom dot skills prompt.

Oh wow.

So the AI isn't broken.

No, its attention mechanism has just been forcefully redirected.

Your highly specific formatting rule suddenly gets drowned out by a new foundational command from the developers to just keep answers brief.

Which perfectly explains the utter confusion on the user's end.

When your recipe stops working, you don't know if you added too much flour, if a connector permission expired, or if the oven itself got reprogrammed.

Exactly.

And the psychological toll of that is severe.

When the source of a system change is completely hidden, your competence quickly turns into confusion.

Yeah, I bet the evidence on your screen is actively gaslighting you.

Basically telling you that your hard earned knowledge just no longer applies.

You know, we use the word training so generously.

I catch myself saying it all the time, like, oh, I trained my AI to write like me.

We all do it.

We build these elaborate workflows and feel like we've successfully taught the machine a new trick.

But the article clarifies that this is a complete illusion.

Totally.

When you give Claude instructions, examples, or context, you aren't actually retraining Anthropic's foundational model, right?

You aren't touching those weights and parameters at all.

Not even a little.

You are merely adapting your own communication style to fit its current state.

You're just finding a temporary path through the current layout of the forest.

So we are training ourselves just as much as we are guiding it.

Yeah, that's exactly it.

We are just learning the exact right sequence of words to coax the current version of the machine into doing what we want it to do.

And an invisible update exposes the sheer fragility of that mutual understanding.

Your business process is sound.

Your standards haven't dropped the bridge between your intention.

And the AI's generation has simply been moved.

But wait, let me play devil's advocate for a second.

I'm looking at the benchmarks Anthropic and OpenAI publish every time they drop one of these updates.

And they are objectively better.

Sure.

They score higher on tests.

Right?

They code faster, they crush the bar exam.

Their context windows, meaning the sheer amount of text they can hold in their memory at one time, are just massive.

Now they are.

Are you telling me that a quote unquote smarter model is actually worse for my workflow?

Well, a smarter model is phenomenal for general progress, but the source text makes a really critical distinction here.

General capability is not the same thing as local reliability.

Capability versus dependability.

Exactly.

AI companies are locked in this massive arms race to sell capability.

They want to promise the smartest reasoning engine on the planet.

Which looks great on a billboard.

Right.

But if you are actually deploying this tool in a professional setting, like handling sensitive customer data or generating monthly financial reports, what you actually need is dependability.

Yeah, because if you have an established business process, tearing it down and rebuilding it every time The AI gets 5% smarter on a math test is an unsustainably expensive way to work.

It's impossible if customer relationships are involved.

A team needs to know the AI will operate within a very tight, predictable tolerance.

Consistency over brilliance.

Precisely.

An occasional mind blowing result carries significantly less business value than a consistently strong expected one.

Benchmarks prove a model can ace a test in a controlled lab.

They do absolutely nothing to guarantee that your specific workflow, the one that hummed perfectly on Tuesday, will still function when you log in on Wednesday morning.

That Tuesday versus Wednesday reality is the core of the frustration.

And it's not just a Claude problem either, is it?

The text highlights a massive platform convergence happening right now.

Yeah, historically we saw these distinct lanes.

CLAUDE was exceptional for long form nuance, while tools built on OpenAI were really aggressive on coding and logic.

Today, ChatGPT, Codex, Claude Code, they're all converging on the exact same territory.

They're moving way beyond simple chatbots and attempting to become these comprehensive operating systems,.

Trying to do everything for everyone, right?

Handling complex coding, executing connected tools, and collaborating completely autonomously.

So if you're annoyed with CLAUDE changing on you, packing your bags and moving to OpenAI won't buy you permanent stability.

Not at all.

They are all caught in the exact same cycle.

Moving platforms just gives you a different set of trade offs.

As Heather brilliantly puts it in the article, every single provider is essentially developing the aircraft While people are already flying.

In it, which is terrifying if you think about it.

If stability is the actual product businesses want, suddenly OpenAI and Anthropic's race for the absolute smartest model feels like a bit of a liability.

It does.

Who is actually building for a steady flight path instead of just a faster engine?

Well, that introduces Microsoft Copilot into the conversation.

Right.

Of course, Copilot's distinct advantage isn't necessarily being the most inventive model for a creative first draft.

Its advantage is its real estate.

What do you mean by real estate?

It sits deep inside a structured business environment.

It's an ecosystem already heavily shaped by identity management, file permissions, security protocols, and compliance rules.

So what does this all mean for the user?

It's an environment where you actually know who is accessing what and when exactly.

When an AI operates within a gated business environment, the organization gains significant influence over data reach.

They manage exactly what the agent can access, creating a really clear chain of.

Ownership for governance, grounding the AI in the reality of the business's actual rules.

Now, we do need to be careful with expectations here.

Those business controls cannot force a probabilistic generative model to behave exactly like a calculator.

Let's define that really quickly.

A probabilistic generative model doesn't retrieve facts like a standard database or solve math like a calculator.

It predicts the most likely next word in a sequence based on its vast training.

It's guessing just really intelligently.

Right.

Because it relies on probability, it will inherently have some variance.

Business controls can't eliminate that variance entirely, nor will they stop the underlying technology from evolving.

Right.

But that governance structure makes the inevitable changes much easier to manage and trace.

As AI transitions from basically a brainstorming toy into critical operational infrastructure, companies are going to demand to buy it.

The same way they buy their servers or their cybersecurity software.

Absolutely.

And this raises an important question about how the AI industry handles software updates compared to traditional tech.

It's totally different, isn't it?

Completely.

For decades in enterprise software, a major system change was rigorously beta tested against critical workflows long before it ever reached the end user.

Right.

You didn't just wake up to find out your accounting software had entirely reinvented how it processes tax brackets overnight.

No, There would be months of warning.

But with AI, users often only discover an alteration when a vital task just fails catastrophically.

Which brings us back to Heather's frustration.

Exactly.

The text argues passionately for a return to ordinary change control Heather says it perfectly.

Behavior belongs in the release notes.

Yes.

We don't just need a shiny blog post celebrating that the model codes 10% faster now.

We need a clear, itemized account of how its behavior has actually been altered.

Users need to know exactly which model version is currently active.

They need to know if anthropic adjusted instruction following or altered how the model selects context or changed how it discovers skills.

Because a microscopic adjustment to how a model allocates its reasoning can fundamentally alter the output of your specific workflow, it.

Can break the whole thing.

We need enough warning to test those processes before Wednesday morning rolls around, but let's deal with the reality on the ground for a second, okay?

We aren't getting perfect behavioral release notes from these tech giants anytime soon.

We're still flying in the aircraft while they rebuild the wings.

Very true.

So what can you, the listener, do right now?

To survive the next invisible update, you.

Must master what the author calls the durable skill of calibration.

Calibration.

Okay, how does someone actually do that on a Tuesday afternoon?

What does it look like in practice?

Well, calibration goes far beyond just a mindset shift.

It requires treating your prompts and workflows like actual software code.

First, it means establishing version control for your prompts.

You shouldn't just be typing instructions into a chat box and hoping for the best every time.

Definitely guilty of that.

We all are.

But you need a master document outside the AI.

Maybe a git repository or a structured notion page where you log the exact text of your prompt, the date it worked, the specific model version you used, and an example of the expected output.

Okay, so basically taking the instructions out of the AI's memory and keeping a hard copy for yourself.

Exactly.

Secondly, when the workflow inevitably breaks, calibration means testing the components in isolation.

Break that down for me.

Don't throw away the whole prompt and start from scratch.

Test the data retrieval first.

Did it find the right file?

Yes.

Okay.

Next, test the formatting rule.

Did it apply the skills constraint?

No.

Oh, that's fine.

And now you've isolated the exact failure point.

Here's where it gets really interesting.

Because the text ties this back to a core NLP principle.

It says, if the meaning of communication is the response we get, then a changed response calls for attention to the wider system.

It's such a brilliant Insight.

If the AI's response changes, furiously rewriting your prompt in a blind panic is the wrong move.

Stop typing and step back.

Right.

Look at the wider system.

Look at those isolated components we just.

Talked about but wait.

Turning everyday prompts, dot skills and working files into private version testable assets.

I mean, that sounds like turning a casual chat interface into rigorous software engineering.

It does.

Doesn't that completely defeat the magic and the speed of using AI in the first place?

It feels like an exhausting amount of overhead for a tool that is supposed to stay save me time.

It absolutely dampens the casual magic.

There's no getting around that.

But it is the necessary reality of transitioning from just experimenting with AI to actually relying on AI.

Stakes are higher now.

Exactly.

If you rely entirely on the AI's unspoken quirks to get your work done, your workflow belongs to the AI company, not to you.

Oh, that is a great point.

You have to preserve your business knowledge outside the model.

Your instructions, your standards, your methods, they must remain portable.

They need to be documented in a way that you can inspect them, test them.

And if an update completely breaks your preferred tool, you can just pick them up and plug them into a competitor's model.

Heather uses a fantastic metaphor at the end of her piece to describe this.

She says, the map is not the territory.

Yes, the current version of the AI you are using is just the map.

It's temporary.

Your actual business goal, your knowledge, your standard of quality, that is the territory.

So when the map suddenly changes while you are using it to navigate, the most useful first question isn't, what did I do wrong?

No, the first question has to be, what moved?

Separating your goals from the tool you use to achieve them will honestly save you hours of frustration.

It really will.

To wrap our heads around all of this, we are basically living through a massive shift in how we interact with technology.

A paradigm shift, really.

Yeah.

We have to start being hypnotized by the sheer capability of these AI models and start demanding dependability.

We need to recognize that when we spend weeks training in AI, we are really just mutually adapting to a highly temporary version of a product because that.

Product will change without warning.

Your survival depends on concrete calibration, versioning your assets, testing in isolation, and maintaining your own rulebook.

So the next time your flawlessly designed workflow inexplicably breaks, take a breath.

Do not immediately blame your own learning curve.

You didn't lose your way.

The forest just shifted around you.

If we connect this to the bigger picture, this raises a genuinely profound thought about the future of work.

Yeah.

What if the ultimate hidden value of integrating AI into our work isn't actually the output the machine generates?

Wait, really?

What else would it be?

Think about it.

What if the real value is that just to survive these constant AI updates, we are finally being forced to deeply document, rigorously test, and truly understand our own human business processes in a way we literally never bothered to do before.

Wow.

We are being forced to understand our own jobs better just to explain them to a machine that keeps changing its mind.

Exactly.

That is a brilliant thought to leave on.

Thank you so much for joining us on this deep dive.

Take a look at your own workflows today.

Ask yourself, what's the map and what's the territory?

And we will see you next time.