DEV Community: Robert Kirkpatrick

The Morning Routine That Separates Profitable Traders from Everyone Else

Robert Kirkpatrick — Thu, 26 Mar 2026 00:51:02 +0000

90% of a day trader's profits come from the first hour of the trading day.

That's not a guess. That's a pattern backed by 26 years of screen time from a trader who blew up seven accounts before figuring it out. The first hour is where the volume is. Where the gaps fill or extend. Where the overnight squeeze setups either fire or fade.

If your morning looks like waking up at 9:25, scrambling to check your watchlist, and panic-buying whatever's green on the 1-minute chart? You've already lost the day before it started.

What the First Hour Actually Looks Like

The most consistently profitable traders I've studied don't start their day at market open. They start an hour before. Sometimes two.

Here's why: by the time the opening bell rings, the information edge is gone. Pre-market volume, overnight news, squeeze setups, earnings reactions. All of it exists before 9:30. The traders who review them at 8 AM have context. The traders who scramble at 9:29 have noise.

A profitable pre-market routine looks something like this:

6:00 AM CT: Scan overnight positions. What happened while you slept? Did your positions gap up or down? Are your stop-losses still valid at current prices? Is the squeeze still firing in the direction you need? If you held AMD overnight and it gapped down 3%, you don't need analysis. You need to exit.

6:30 AM CT: Run the scanner. Whether you're using a free screener (slow, limited) or something automated (fast, comprehensive), this is when you identify the 3-5 tickers worth watching today. The criteria should be specific and repeatable: squeeze firing on the daily, volume above average, price near a key level. No vibes. No tips from Reddit. Scanner data only.

7:00 AM CT: Calculate position sizes. For every ticker on your watchlist, know your entry, your stop, and your size before the market opens. If CNC is squeezing at $36.11 and your stop is $35.39, and you're risking 2% of a $500 account, that's a 10-share position. Know this before the bell. Not during.

7:30 AM CT: Review the playbook. What's the setup? What's the entry trigger? What invalidates the trade? Write it down. If it's not in the playbook, it's not a trade. It's a gamble.

8:30 AM CT: Wait. This is the part nobody teaches. The last 30 minutes before open are for doing nothing. You've done your work. You know your levels. Now you wait for the market to come to you instead of chasing it.

The Habits That Compound

There's a concept from the business world that applies directly to trading: tiny habits create outsized returns over time.

The habits aren't dramatic. They're boring. That's why they work.

Habit 1: Log every trade. Before close every day, write down what you traded, why, what happened, and what you'd do differently. Traders who log consistently improve their win rate by double digits within 90 days. Not because logging is magic. Because it forces you to confront your mistakes instead of rationalizing them.

Habit 2: Review your P/L weekly, not daily. Checking your account after every trade is an emotional trap. A bad day feels like a catastrophe. A good day feels like validation that you should size up tomorrow. Both reactions are wrong. Weekly review gives you the data without the emotional whiplash.

Habit 3: Pre-define your maximum loss per day. Once you hit it, you're done. Close the platform. Go outside. This single rule prevents 80% of account blowups. The math is simple: if your max daily loss is 4% of your account, you can have 25 consecutive max-loss days before you're wiped out. Nobody has 25 consecutive max-loss days unless they're ignoring their stops.

Habit 4: Automate what you can. The morning scan doesn't have to be manual. The position size calculation doesn't have to be mental math. The alert that tells you your squeeze fired doesn't have to come from staring at TradingView for 45 minutes. Every minute you spend on mechanical tasks is a minute you're not spending on decisions that actually matter.

Why Morning Routines Fail

Most traders have tried some version of a morning routine. Most quit within two weeks. Here's why:

They make it too complicated. Fifteen indicators, eight timeframes, three news sources, two Discord servers, and a partridge in a pear tree. Simplify. One indicator. One timeframe for entries, one for context. That's enough.

They don't build in slack time. If your routine requires you to be at your desk at exactly 5:45 AM and takes exactly 90 minutes with zero margin for error? You'll break it the first morning your kid wakes up early.

They focus on gathering information instead of making decisions. Reading five pre-market newsletters doesn't make you a better trader. Making a clear go/no-go decision on three tickers does. Information without a decision framework is just entertainment.

They don't track whether the routine actually works. If your morning scan identified five tickers last week and zero of them produced profitable trades, your scan criteria are wrong. Adjust. A routine that doesn't produce results isn't a routine. It's a ritual.

My Actual Morning (No Fluff)

Here's what I do. It takes 45 minutes total.

Check overnight positions. Close anything that violated the thesis.
Run the squeeze scanner. Pull the tickers where momentum just fired.
Check the direction. Bullish squeeze only. If it fired bearish, it goes on a puts watchlist, not a buy list.
Calculate entry, stop, and size for the top 3 setups.
Set alerts and wait.

That's it. No CNBC. No Twitter scrolling. No Reddit. No Discord until after the first hour. Inputs are data. Output is a decision. Everything else is noise.

The traders who do this for 90 days straight aren't the most talented. They're the most consistent. And in trading, consistency beats talent every single time.

SqueezeAlert runs the scanner and direction check automatically and delivers results to your phone before market open. If you want your morning scan done in 30 seconds instead of 30 minutes, see what we're building.

Originally published on Medium.

AI Won't Replace Traders. But Traders Who Use AI Will Replace You.

Robert Kirkpatrick — Thu, 26 Mar 2026 00:50:46 +0000

Every free stock screener I tried gave me the same experience. Delayed data. Limited filters. Twelve popup ads for a premium tier. And by the time I found a setup worth trading, the move was already over.

So I built my own.

Not because I'm a developer. I'm not. I built it because AI tools in 2026 have gotten good enough that someone with a clear idea of what they want can actually make it happen without writing code from scratch. What I wanted was simple: a scanner that watches the TTM Squeeze across every ticker on the S&P 500 and alerts me the second momentum fires.

That scanner ran its first live scan this week. It caught CNC before a 4.3% intraday move. It flagged MU on a squeeze fire that matched what experienced traders were calling in their live rooms. It works.

The Skills Gap Nobody Talks About

There's a stat that keeps coming up in AI business circles: by 2027, over 80% of knowledge workers will use AI daily. Most of them will use it badly.

The gap isn't between people who use AI and people who don't. Everyone will use it. The gap is between people who know how to tell AI what to do and people who type "give me stock picks" into ChatGPT and wonder why the results are useless.

There are roughly nine competencies that separate power users from everyone else. Most of them aren't technical. They're about knowing how to frame a problem, give context, validate output, and chain tools together into workflows that actually produce results.

For trading specifically, the competencies that matter most:

Knowing what to automate and what to keep manual. AI is excellent at scanning 500 tickers for a specific technical setup. AI is terrible at deciding whether to take the trade. The pattern recognition is the machine's job. The risk management is yours.
Building structured instructions that produce consistent output. Asking Claude "what stocks should I buy?" gives you garbage. Asking Claude "analyze the TTM Squeeze status on these 10 tickers, flag any where momentum just fired bullish on the daily timeframe, and calculate the reward-to-risk ratio based on the nearest support and resistance levels" gives you something you can actually trade on.
Connecting tools into pipelines. A scanner that detects setups is useful. A scanner that detects setups, checks the squeeze direction, calculates position size based on your account balance, and sends you an alert before market open? That's a business.

The Box Strategy Problem (And How AI Solves It)

There's a well-known trading approach that uses consolidation boxes to identify breakout moves. The idea is simple: price consolidates in a range, energy builds, and when it breaks out, the move is explosive. Traders have been using this for decades.

The problem isn't the strategy. It's execution speed.

By the time a human trader identifies the box, draws the levels, confirms the breakout, and enters the trade, the move is already 30-40% done. On a fast mover, that's the difference between catching a 5% run and catching the last 2%.

AI changes the execution layer completely. A scanner can monitor hundreds of tickers simultaneously, identify box formations in real time, and flag the breakout the moment it happens. Not after 5 minutes of chart staring. The moment it happens.

What I'm Actually Using Right Now

My current setup combines three things:

A squeeze scanner that monitors the TTM Squeeze indicator across the S&P 500. It checks every ticker on a 15-minute, hourly, and daily basis. When momentum fires, it sends an alert with the direction, the ticker, and the current price.

A position sizing calculator that takes my account balance, the entry price, and the stop-loss level, then tells me exactly how many shares to buy so I never risk more than 2% on a single trade.

An alert pipeline that sends everything to my phone before market open so I can review setups during coffee instead of scrambling at 9:30.

None of these required me to become a software engineer. The squeeze engine runs in Python. Claude helped write most of it. The alert system uses basic automation. The position calculator is a spreadsheet formula.

The total cost: time. The total benefit: I caught three squeeze fires on Day 1 that I would've completely missed using manual scanning.

The Real Play

AI tools for trading aren't magic. They won't turn a losing strategy into a winner. They won't eliminate risk. They won't make you rich while you sleep.

What they will do is give you more time, more coverage, and faster execution on strategies that already work. That's the real play. Not AI as oracle. AI as infrastructure.

The math still has to work. The discipline still has to be there. The stop-losses still have to hold. AI just makes the boring parts faster so you can focus on the parts that actually require a human brain.

We're building SqueezeAlert to be the scanner that does the boring parts for you. Real-time TTM Squeeze monitoring across the S&P 500, with alerts that hit your phone before market open.

Trading Is Math, Not Feelings. Here's the Formula Most Traders Ignore.

Robert Kirkpatrick — Thu, 26 Mar 2026 00:50:29 +0000

I blew up my first paper trading account in 48 hours.

Not because the strategy was bad. The strategy was fine. I blew it up because I traded with my gut instead of a calculator. Bought AMD on a hunch. Ignored the squeeze direction. Held through the reversal because I "felt" like it would bounce. It didn't.

Day 1 P/L: negative. Lesson learned: feelings don't show up on your P/L statement. Math does.

The 3-Step Formula That Changed Everything

There's a veteran trader with 26 years of experience who breaks trading down into something embarrassingly simple. Three outcomes, three numbers, one decision framework:

Increase the quality of your winners. Not more trades. Better trades. If your average winner returns 3% but your setup only gives you a 40% win rate, you need a minimum 2:1 reward-to-risk ratio just to break even after commissions. Most retail traders don't even calculate this before clicking Buy.
Reduce the size of your losers. A 2% stop-loss isn't optional. It's the cost of staying in the game. The math here is brutal and non-negotiable: a 50% loss requires a 100% gain to recover. A 10% loss needs just 11%. The gap between those two scenarios? Discipline. Not skill.
Stay out of trades you shouldn't be in. This is the one nobody wants to hear. The best trade you'll make this week is the one you don't take. If the squeeze is firing bearish and you're buying calls, you're not trading. You're gambling. There's a difference, and your account balance knows it.

Why "Smart" Traders Lose More

Here's the counterintuitive part. The traders who lose the most aren't the clueless ones. They're the ones who know just enough to be dangerous.

They've read three books on technical analysis. They can name every candlestick pattern. They subscribe to four scanners. And they overtrade because they see setups everywhere.

There's a business principle that applies here: your intelligence can become the thing that limits your income. Same pattern shows up in entrepreneurship. The smartest founders often build the worst businesses because they overcomplicate everything. They second-guess simple strategies because they feel "too easy." They keep adding indicators when they should be removing them.

The traders who consistently pull $1,000+ days? They trade one setup. Maybe two. They've done the math on their win rate, their average winner, their average loser, and their expected value per trade. Then they execute. No feelings. No overanalysis. Just math and a playbook.

The Playbook Is the Paybook

One of the most expensive lessons in trading is learning that strategies are overrated and playbooks are everything.

A strategy tells you when to enter. A playbook tells you when to enter, where to exit, how much to risk, what to do when it goes against you, and when to walk away entirely. A strategy is a chapter. A playbook is the whole book.

Here's what a simple playbook entry looks like:

Setup: 15-bar TTM Squeeze firing bullish on the daily
Entry: First green momentum bar after squeeze fires
Stop: 2% below entry, hard stop, no exceptions
Target: Previous resistance or 3:1 reward-to-risk, whichever comes first
Position size: Never more than 5% of account on a single trade
Rule: If squeeze is bearish, do not enter long. Period.

That's it. No indicators stacked on indicators. No gut feelings. No watching CNBC to "get a read" on the market.

The Mental Capital Problem

Your mental capital carries the highest interest rate of any resource you have.

Every bad trade costs you twice. Once in dollars, again in confidence. After three losers in a row, most traders do one of two things. They revenge trade, doubling down to make it back. Or they freeze, missing the next three good setups because they're scared.

Both responses are emotional. Both are expensive.

The fix isn't therapy. It's math. When you know your expected value per trade, a losing trade isn't a failure. It's a data point. You expected to lose 40% of the time. You just hit one of the 40%. Move on.

The traders who survive long enough to get rich are the ones who treat their account like a business, not a slot machine. They track every trade. They know their numbers. They let the math do the heavy lifting so their emotions don't have to.

What I'm Doing Differently Now

After that first day of paper trading, I added three rules that I won't break:

Only trade with squeeze direction. If the momentum is bearish, I don't go long. Full stop.
2% stop-loss on every position. If it hits, I'm out. No hoping, no "it'll come back."
Calculate expected value before entering. If the math doesn't work, the trade doesn't happen.

These aren't complicated. A fifth grader could follow them. That's the point.

Trading isn't an intelligence test. It's a discipline test. The math is simple. Following it is the hard part.

I'm building a squeeze-based scanner that flags momentum setups in real time and does the math for you. If you want early access, check out what we're working on at SqueezeAlert.

Originally published on Medium.

Your AI Has the Memory of a Goldfish. Here Is Why That Is Actually Your Fault.

Robert Kirkpatrick — Thu, 26 Mar 2026 00:50:12 +0000

A thread hit r/artificial last week that stopped me mid-scroll. Eighty eight upvotes and fifty five comments, which is solid for that sub. The title was something like "LLMs forget instructions the same way ADHD brains do."

I have ADHD. Diagnosed, medicated, the whole thing. And when I read that post, I did not feel attacked. I felt seen.

Because the comparison is disturbingly accurate. And the reason it matters goes way beyond a clever Reddit analogy.

The Pattern Nobody Is Talking About

Here is what happens when you give a large language model a detailed set of instructions at the start of a conversation. For the first few turns, it follows them. The tone is right. The format holds. The constraints stick. Everything looks perfect.

Then around turn eight or nine, things start drifting. The model stops using your formatting. It forgets the constraint you set about length. It reverts to default behavior. By turn fifteen, it is writing like you never gave it instructions at all.

This is not a bug. It is how attention works in transformer architecture.

The model processes your instructions as tokens in a context window. Early tokens carry weight. As the conversation grows, those early tokens get pushed further from the model's active attention. The instructions do not disappear from the window. They just stop mattering as much.

It is the AI equivalent of someone nodding along in a meeting, saying "got it," and then doing something completely different forty five minutes later.

Sound familiar?

Why the ADHD Comparison Actually Holds

ADHD is not a deficit of attention. It is a deficit of attention regulation. People with ADHD can hyperfocus for hours on the right task. The problem is sustaining attention on instructions that are not immediately reinforced.

Large language models have the same structural problem. The architecture has a similar failure mode, not because models have brains or consciousness or feelings. The model's attention mechanism is literally called "attention." It assigns weight to tokens based on relevance to the current query. And just like an ADHD brain, the model's ability to hold onto instructions degrades as distance increases.

A person with ADHD can hear "remember to send that email after lunch," fully understand it, genuinely intend to do it, and still forget by two fifteen. The instruction was received. It just was not reinforced.

An LLM can receive "always format your response as bullet points with no more than three sentences each," follow it perfectly for six turns, and then start writing paragraphs again. The instruction is still in the context window. It is just no longer weighted heavily enough to override the model's default patterns.

Same failure. Different substrate.

Why "Just Remind It" Does Not Work

The most common advice you will see is to repeat your instructions periodically. Paste them again every few turns. Remind the model what you asked for.

This works. Kind of. The way that setting seventeen alarms works for someone with ADHD. It addresses the symptom without touching the cause.

Every time you re-paste instructions, you are burning context window space. You are adding tokens that could have been used for actual work. And you are creating a workflow where you, the human, are responsible for babysitting the AI's attention in real time.

That is not a system. That is a coping mechanism.

I know. I have about forty of them.

The Real Problem Is Architectural

The reason LLMs drift is not laziness. It is not bad training. It is the fundamental structure of how transformers allocate attention across a sequence of tokens.

Newer models have longer context windows. Claude can handle two hundred thousand tokens. Gemini pushed past a million. People treat this as a solution. More room means the model can hold onto instructions longer, right?

Not exactly.

A longer context window does not fix attention weighting. It just gives the problem more room to develop before it becomes obvious. Your instructions still lose relative weight as the conversation grows. It just takes longer to notice because the window is bigger.

Think of it this way. Losing your keys in a studio apartment makes the problem obvious fast. Losing your keys in a four bedroom house lets you wander around longer before you realize something is wrong. The keys are still lost.

What Actually Fixes It

Here is where the ADHD analogy becomes genuinely useful instead of just interesting.

The gold standard for ADHD management is not "try harder to remember." It is external structure. Checklists. Routines. Systems that do not depend on your attention being perfect in the moment.

My therapist said something years ago that stuck with me. She said, "You will never fix your working memory. Stop trying. Build systems that do not need it."

That is exactly what an AI workflow needs.

The fix for LLM memory drift is not bigger context windows. Better prompts will not solve it either. Re-pasting instructions every six turns like you are playing whack-a-mole with the model's attention span is just busywork.

The fix is building the checkpoints into the system itself.

What a Checkpoint System Looks Like

A checkpoint is a structured evaluation step that runs at a defined point in the workflow. Not when you remember to run it. Not when you notice the output drifting. At a defined, automatic, non-negotiable point.

In a CORE system design, this means every output stage has a validation pass built in. The model produces something. Before it moves forward, it gets checked against the original requirements. A structured rubric runs the same way every time, not a human eyeballing the output.

This is how we built Bulletproof Writer. The system does not depend on you noticing that the AI drifted off your instructions. It catches the drift automatically. Eight scoring dimensions. Every draft. Every time. You could be half asleep and the quality gate still holds.

Discovery Mode in version 3.4 takes this further. Instead of just scoring what you wrote, it maps the gap between what you intended and what actually landed on the page. It shows you where the model's attention drifted from your original brief. Specific, measurable dimensions that you can act on.

That is the difference between "try harder to remember" and "build a system that remembers for you."

The Bigger Lesson From Neuroscience

The ADHD research community figured this out decades ago. External structure beats internal willpower every single time. The people who manage ADHD well are not the ones with the best memory. They are the ones with the best systems.

The same principle applies to AI workflows. The people getting reliable, consistent results from LLMs in 2026 are not the ones with the most patience for re-pasting instructions. They are the ones who stopped relying on the model's attention span and built accountability structures around it.

You would not hand an employee a list of twenty requirements on Monday morning and then check the work on Friday with no check-ins, no milestones, no structured review points in between. That would be insane management. That is exactly how most people use AI. Giant instruction dump at the top. Hope for the best. Get frustrated when the output drifts.

The model is not the problem. The workflow is.

A Practical Framework

If you are dealing with LLM memory drift right now, here is what actually works.

Break long sessions into short ones. Instead of one conversation with thirty turns, run three conversations with ten turns each. Re-inject your full context at the start of each. The model's attention is strongest at the beginning of a session. Use that.

Build validation into the workflow, not your calendar. Do not plan to "check the output later." Build the check into the process itself. Every output gets evaluated against the same criteria before it moves forward.

Stop treating instructions as one-time events. In ADHD management, you do not tell yourself something once and expect it to stick. You build the reminder into the environment. Same with AI. Your instructions should be embedded in the system architecture, not pasted once at the top of a chat and forgotten.

Use structured scoring instead of vibes. "This looks good" is not a quality gate. "This scored seven point two out of ten on pacing and four point one on gap density" is a quality gate. One of those depends on you being sharp. The other does not.

Why This Matters Beyond AI

The reason the Reddit thread resonated is not just because the technical comparison is accurate. It touches something personal for a lot of people.

Millions of adults have ADHD. Many of them discovered it through exactly this kind of pattern recognition. They saw something that described their brain, and it clicked. The LLM comparison works the same way, in reverse. You watch the model forget your instructions, and you think, "Oh. That is what I do."

The solution is the same in both directions. Stop blaming the hardware. Start building better systems around it.

I did not fix my ADHD by getting a better brain. I fixed my output by building workflows that compensated for the attention regulation I would never have naturally. External checklists. Structured routines. Evaluation steps that do not depend on me remembering to do them.

That is what a CORE system does for AI. It does not fix the model's attention span. It builds the structure around the model so that attention drift stops mattering.

Where to Start

If you are tired of re-pasting instructions every six turns, stop doing that. Build a system that does not need you to.

The CORE Operating System is how we approach this at TotalValue. Structured checkpoints and validation passes that catch drift before it reaches your output.

Bulletproof Writer v3.4 is one specific implementation. Eight scoring dimensions. Automatic quality gates. Discovery Mode that maps the gap between your intent and the model's output. It is the external structure your AI workflow has been missing.

Your model does not need a better memory. It needs a system that does not depend on memory at all.

The comparison between LLMs and ADHD brains came from r/artificial. The engineering solution came from fifteen years of living with one of them and two years of building systems for the other.

Originally published on Medium.

Everyone's Upgrading From Prompt Engineer to Context Engineer. They're Still Missing the Point.

Robert Kirkpatrick — Thu, 26 Mar 2026 00:49:10 +0000

A new title is going around. Context engineer.

Not "prompt engineer." That one is getting retired. The people who took it seriously two years ago are now quietly rebranding. Context engineering is the upgrade. The idea is that better results do not come from better prompts, they come from better context. You feed the model more relevant information, you structure that information well, you stop asking questions and start building environments.

That part is correct.

It is also not enough.

I have been building AI systems since before context engineering had a name. I watched the prompt engineering era peak and decline. I watched the "context is king" thesis take over. And I am watching the same ceiling hit the same people who thought upgrading the input layer would fix everything.

It does not.

What Prompt Engineering Actually Was

Prompt engineering started as a genuine skill. Not everything that called itself prompt engineering was, but the real version had discipline in it. You learned how the model responded to structure. You figured out which phrasings triggered better outputs. You built templates that worked consistently.

The problem is that it optimized the wrong thing. Prompt engineering treated the model like a search engine. Put in a better query, get a better result. Repeat. The output quality improved. The workflow stayed broken.

You still had to copy the output. Evaluate it manually. Edit it. Push it somewhere. Start over the next time. Nothing carried forward. Every session started fresh. The model did not know you. Your system did not remember what worked.

Good prompt engineering got you better raw material. That is it.

What Context Engineering Actually Is

Context engineering is a real step forward. The core insight is correct: models perform better when they have structured, relevant context. You stop giving the model a single question and start giving it a role, a history, a knowledge base, and a set of constraints.

This week, a project called Get Shit Done hit the top of Hacker News with over 360 upvotes and one hundred eighty comments. It is a meta-prompting, context engineering, and spec-driven development system. The idea is to combine context architecture with a workflow spec to get more consistent, targeted outputs from AI tools.

The community response was immediate. Engineers recognized what they had been missing. Context matters more than the prompt. The spec matters more than the question. The environment matters more than the input.

They are right about all of that.

Most of them will hit the same ceiling within sixty days.

Why Context Engineering Still Is Not Enough

Context engineering is an input improvement. It makes the model smarter about what you want. It does not make the system smarter about what happens next.

When the output lands, someone still has to evaluate it. Someone has to decide if it is good. Someone has to move it through the pipeline. Someone has to check it against the last version, the quality standard, the intended audience. Someone has to catch the problems before they become your problem.

Context engineering solves the input. It does not touch the pipeline.

The pipeline is where most of the failure actually lives.

I have seen people build immaculate context architectures for AI writing. Every prompt is tight. The system instructions are precise. The model knows the brand, the voice, the tone, the length requirements. The outputs are genuinely better than anything they produced before.

Three months later, those outputs are sitting in a folder somewhere. The writing was fine. There was no pipeline to catch quality issues, route content to the right format, validate it against established standards, and move it forward without depending on one person's judgment call at every step.

The context was right. The system was missing.

What the Missing Layer Is

This is the line between operational AI and experimental AI.

Operational AI has accountability built in. Checkpoints, not just context. A validation layer, not just a spec. A system that behaves predictably whether you are paying attention or not.

I call it a CORE system. The name matters less than the concept: every workflow that runs on AI needs human decision points baked in, output validation stages that do not depend on who is available that day, and a structure that makes consistency achievable without heroic effort.

Bulletproof Writer is one example. It is not a writing prompt. It is a system that scores your draft against eight dimensions before anything leaves the page. Pacing. Fluidity. Gap density. Opening strength. Character voice. Reader drop-off risk. Success probability. It takes the evaluation work out of your hands and turns it into a structured, repeatable process that runs the same way every single time.

That is the difference between context engineering and a CORE system. Context engineering improves what goes in. A CORE system controls what comes out.

The Henry Ford Parallel

Henry Ford once sat in a courtroom while lawyers tried to embarrass him. They asked history questions, technical details, dates and facts he could not answer. Their point was that he was uneducated. His response was something like this: why would I clutter my mind with facts I can get from any specialist with one phone call?

Context engineering is that specialist. You stop trying to hold all the context yourself and give it to the model. That is a smart move. Ford was right to outsource the information.

Ford also built systems. He built the assembly line. He built accountability structures into every station. He built a workflow that did not depend on any single person's expertise staying in the room.

The specialist access is the context engineering. The assembly line is the CORE system.

You can not run a production operation on specialist access alone.

What This Looks Like in Practice

A CORE system for content creation looks like this.

You have a context layer that tells the AI who you are, what you know, what voice you write in, and what standards your work is held to. That is context engineering, and it is the right foundation. Do not skip it.

The CORE system adds what comes next. A structured evaluation pass using a consistent rubric. A quality gate that catches problems before they become published mistakes. A pipeline that moves content from draft to finished without a human bottleneck at every handoff.

Here is the practical difference. A context-engineered workflow produces a better first draft. A CORE system produces a finished product that does not require you to be sharp on the day you review it.

The people getting consistent results from AI in 2026 are not the best prompt writers. They are not the best context architects. They are the people who built the layer between the model and the world. The layer that catches, validates, and ships reliably.

Context engineering gets you better raw material. A CORE system gets you a finished product.

Where This Leaves You

If you are still optimizing prompts, stop and move to context engineering. That step is real and the improvement is significant.

If you are already doing context engineering and still feeling like your results are inconsistent, like you are one bad session away from having nothing to show, like the quality depends too much on how sharp you happen to be that particular day, then you have hit the ceiling context engineering creates.

The fix is not better context. The fix is a system that works without you having to be perfect every time.

That is what we build at TotalValue. Not prompts. Not even context architectures. Operating systems for AI workflows. Miniature, targeted, built for the specific outputs you need to produce reliably.

Context engineering was a real upgrade. It is not the last one.

See the CORE Operating System

Bulletproof Writer v3.4 (the structured evaluation system referenced above)

Originally published on Medium.

Everyone Switched to AI Content. Most of Them Got Nothing.

Robert Kirkpatrick — Thu, 26 Mar 2026 00:49:06 +0000

Seventy-four percent of new webpages now contain AI-generated content.

Most of it gets zero traffic.

Not penalized. Not flagged. Not removed. Just invisible. Zero clicks. Zero reads. The content exists, technically. Nobody finds it.

I have been building AI writing systems since before it was fashionable, and I have watched smart people make the same mistake over and over. They automate the output. They ignore the outcome.

Eighty-seven percent of businesses are using AI for SEO content now. The volume is staggering. The results are concentrated in a small percentage of those operations.

This is the gap nobody talks about. Not because it is a secret. Because it requires admitting something uncomfortable.

The Content Flood Is Real. The Results Are Not.

Before 2023, putting out one good article a week was competitive. Now you are competing with operations that pump out twenty articles a day, all of it AI-generated, all of it technically correct, all of it reading like it was written by a very polite robot who did the assigned reading.

Here is what the data says: sixty-five percent of marketing professionals say their biggest concern about AI content is quality and authenticity. They are right to be worried.

But they are misidentifying the problem.

The issue is not that Google penalizes AI content. Google has stated publicly that it does not penalize AI-assisted content by default. The issue is that readers do.

You have about eight seconds. That is roughly how long a visitor stays on a page before deciding whether to keep reading or hit the back button. And human readers have developed a remarkable sensitivity to content that was clearly generated without a specific person in mind.

It is not about spelling. It is not about grammar. It is about whether the writing feels like it was written for anyone in particular.

Most AI content is not written for anyone. It is written for the keyword.

What AI Content Usually Gets Wrong

I have read hundreds of AI-generated articles in the past year. I can spot them inside three sentences. Not because of technical tells, though those exist. Because of a structural emptiness.

The article knows things. It does not know you.

Generic AI content tends to hit the same patterns. It opens with a broad statement about how important the topic is. It lists five to seven points. Each point has two to three sentences of explanation. The conclusion circles back to why this matters. The whole thing reads like a term paper written the night before it was due.

That structure is not wrong. It is just completely forgettable.

The articles that get read, shared, and ranked share something different. They have a point of view. They make a specific claim and defend it. They include something that could only have been observed by someone who was actually in the room.

In the absence of that specificity, readers bounce. And when readers bounce, rankings follow.

The Authenticity Problem Is Measurable Now

Here is where it gets interesting.

AI search platforms are now the fastest-growing source of referral traffic on the web. AI platforms sent more than one billion referral visits in a single month in 2025, up three hundred fifty-seven percent year over year. ChatGPT users click out to external sites at twice the rate of Google users.

But here is what the research shows about which content gets cited.

Content depth matters. Readability matters. Q-and-A formats perform best. Dense paragraphs perform worst. And forty-four percent of all AI Overview citations come from the first thirty percent of a piece, meaning the opening matters more than anything else.

The content that gets picked up by AI citation engines, and the content that converts when readers arrive from those engines, shares the same DNA. It is specific. It is direct. It makes a claim in the first paragraph. It answers the question without burying the answer.

That is not a coincidence. That is what human writing at its best looks like.

Why Most People Are Generating Content Wrong

The mistake is using AI to write more of the same.

You take a keyword. You tell the AI to write a fifteen-hundred-word article about that keyword. The AI does. You publish it. You do this forty times. You wonder why nothing is ranking.

The content is not bad. It is generic. And generic is the one thing you cannot afford to be in a market where everyone has access to the same tools.

The people getting results are using AI differently. They are not asking the machine to think for them. They are using it to execute ideas they already have. The strategy, the angle, the specific claim, the personal observation, those come from them. The AI builds the structure around a direction that a human already chose.

That is a different process. It produces a different result.

When I write something using this approach, the article has a spine. It is arguing something. It is not just covering a topic. The difference is obvious to anyone who reads both versions.

The Specific Problem With Voice

There is a technical version of this problem and a practical version.

The technical version is that AI writing has patterns. Not just em dashes and phrases like "it is worth noting" and "in today's rapidly evolving landscape." Those are easy to remove. The deeper patterns are structural: the tendency to hedge every claim, to present multiple perspectives on everything, to avoid taking a position.

Real writing takes positions. It says this and not that. It is written by someone with an opinion.

The practical version is simpler. If your content could have been written by anyone, it reads like it was written by no one.

I built a tool specifically for this problem. It is called Human Voice, and it exists because I kept running into the same issue with my own output. The AI would give me something technically correct and completely without personality. The tool runs the content through a set of voice-matching criteria, looking for the places where the writing has gone flat and flagging them for revision.

There is a second pass that matters as much as the first. AI content carries what I call signature patterns, things the model does consistently that trained readers recognize immediately. Removing them is not about fooling anyone. It is about making sure the content can compete on its own merits instead of being dismissed before anyone finishes the opening paragraph.

That second pass is what AI Signature Scrub handles. Every article I publish goes through both.

What Actually Works Right Now

The research on AI citations gives you a clear picture of what to aim for.

First-person observations. Specific data. Clear claims in the opening paragraph. Q-and-A structure where the question is one that real people actually ask. Depth without padding. Sentences that go somewhere.

None of this is complicated. It is just not what you get when you hand a keyword to a model and wait.

The filter I use before publishing anything:

Would a specific person find this useful? Not a demographic. A person. Someone I could picture reading it on their phone at lunch, nodding because it says something they have been thinking but could not articulate.

If the answer is no, the article is not ready.

The Numbers That Tell the Story

Fifty-seven percent of SEO professionals say competition has increased significantly because of AI. That is not surprising.

What is surprising is that only sixteen percent of companies are tracking AI search performance. Everyone is generating content. Almost nobody is measuring what happens to it.

The ones who are measuring are finding something clear: the content that converts from AI referrals is converting at twenty-three times the rate of traditional search traffic. The readers who come from AI citations are already convinced they need what you have. They are not browsing. They are looking for confirmation.

That is an extraordinary opportunity. And it belongs entirely to people who write content worth citing.

What You Should Do Differently

Stop generating. Start arguing.

Pick one specific thing you believe about your topic that most people in your space would push back on. Write that article. Make the case. Use your actual experience as evidence. Let the AI help you build the structure, but make sure the position is yours.

That article will outperform ten generic ones. Every time.

If you want to see what this looks like in practice, Human Voice and AI Signature Scrub are both available through TotalValue Group. They are the tools I use on every article before it goes live.

One is about getting the voice right. The other is about removing the patterns that signal to readers, before they have even finished the introduction, that nobody was actually home when this was written.

The content flood is not slowing down. The gap between content that works and content that does not is widening. The tools exist to end up on the right side of it.

Originally published on Medium

Your AI Has the Memory of a Goldfish. Here Is Why That Is Actually Your Fault.

Robert Kirkpatrick — Sat, 21 Mar 2026 23:58:32 +0000

I have ADHD. Diagnosed, medicated, the whole thing. And when I read that post, I did not feel attacked. I felt seen.

Because the comparison is disturbingly accurate. And the reason it matters goes way beyond a clever Reddit analogy.

The Pattern Nobody Is Talking About

This is not a bug. It is how attention works in transformer architecture.

It is the AI equivalent of someone nodding along in a meeting, saying "got it," and then doing something completely different forty five minutes later.

Sound familiar?

Why the ADHD Comparison Actually Holds

Same failure. Different substrate.

Why "Just Remind It" Does Not Work

The most common advice you will see is to repeat your instructions periodically. Paste them again every few turns. Remind the model what you asked for.

This works. Kind of. The way that setting seventeen alarms works for someone with ADHD. It addresses the symptom without touching the cause.

That is not a system. That is a coping mechanism.

I know. I have about forty of them.

The Real Problem Is Architectural

The reason LLMs drift is not laziness. It is not bad training. It is the fundamental structure of how transformers allocate attention across a sequence of tokens.

Not exactly.

What Actually Fixes It

Here is where the ADHD analogy becomes genuinely useful instead of just interesting.

The gold standard for ADHD management is not "try harder to remember." It is external structure. Checklists. Routines. Systems that do not depend on your attention being perfect in the moment.

My therapist said something years ago that stuck with me. She said, "You will never fix your working memory. Stop trying. Build systems that do not need it."

That is exactly what an AI workflow needs.

The fix is building the checkpoints into the system itself.

What a Checkpoint System Looks Like

That is the difference between "try harder to remember" and "build a system that remembers for you."

The Bigger Lesson From Neuroscience

The model is not the problem. The workflow is.

A Practical Framework

If you are dealing with LLM memory drift right now, here is what actually works.

Why This Matters Beyond AI

The reason the Reddit thread resonated is not just because the technical comparison is accurate. It touches something personal for a lot of people.

The solution is the same in both directions. Stop blaming the hardware. Start building better systems around it.

That is what a CORE system does for AI. It does not fix the model's attention span. It builds the structure around the model so that attention drift stops mattering.

Where to Start

If you are tired of re-pasting instructions every six turns, stop doing that. Build a system that does not need you to.

The CORE Operating System is how we approach this at TotalValue. Structured checkpoints and validation passes that catch drift before it reaches your output.

Your model does not need a better memory. It needs a system that does not depend on memory at all.

The comparison between LLMs and ADHD brains came from r/artificial. The engineering solution came from fifteen years of living with one of them and two years of building systems for the other.

Originally published on Medium.

Everyone's Upgrading From Prompt Engineer to Context Engineer. They're Still Missing the Point.

Robert Kirkpatrick — Fri, 20 Mar 2026 09:53:49 +0000

A new title is going around. Context engineer.

That part is correct.

It is also not enough.

It does not.

What Prompt Engineering Actually Was

Good prompt engineering got you better raw material. That is it.

What Context Engineering Actually Is

The community response has been immediate. Engineers recognized what they had been missing. Context matters more than the prompt. The spec matters more than the question. The environment matters more than the input.

They are right about all of that.

And most of them will hit the same ceiling within sixty days.

Why Context Engineering Still Is Not Enough

Context engineering is an input improvement. It makes the model smarter about what you want. It does not make the system smarter about what happens next.

Context engineering solves the input. It does not touch the pipeline.

And the pipeline is where most of the failure actually lives.

Three months later, those outputs are sitting in a folder somewhere. Not because the writing was bad. Because there was no pipeline to catch quality issues, route content to the right format, validate it against established standards, and move it forward without depending on one person's judgment call at every step.

The context was right. The system was missing.

What the Missing Layer Is

This is the line between operational AI and experimental AI.

Operational AI has accountability built in. Not just context, but checkpoints. Not just a spec, but a validation layer. Not just better prompts, but a system that behaves predictably whether you are paying attention or not.

Every workflow that runs on AI needs human decision points baked in, output validation stages that do not depend on who is available that day, and a structure that makes consistency achievable without heroic effort.

The difference between context engineering and a full system: Context engineering improves what goes in. A system controls what comes out.

The Henry Ford Parallel

Context engineering is that specialist. You stop trying to hold all the context yourself and give it to the model. That is a smart move. Ford was right to outsource the information.

But Ford also built systems. Not just access to specialists. He built the assembly line. He built accountability structures into every station. He built a workflow that did not depend on any single person's expertise staying in the room.

The specialist access is the context engineering. The assembly line is the system.

You can not run a production operation on specialist access alone.

What This Looks Like in Practice

But the system adds what comes next. A structured evaluation pass using a consistent rubric. A quality gate that catches problems before they become published mistakes. A pipeline that moves content from draft to finished without a human bottleneck at every handoff.

Here is the practical difference. A context-engineered workflow produces a better first draft. A full system produces a finished product that does not require you to be sharp on the day you review it.

Context engineering gets you better raw material. A system gets you a finished product.

Where This Leaves You

If you are still optimizing prompts, stop and move to context engineering. That step is real and the improvement is significant.

But if you are already doing context engineering and still feeling like your results are inconsistent, like you are one bad session away from having nothing to show, like the quality depends too much on how sharp you happen to be that particular day, then you have hit the ceiling context engineering creates.

The fix is not better context. The fix is a system that works without you having to be perfect every time.

Context engineering was a real upgrade. It is not the last one.

Everyone Switched to AI Content. Most of Them Got Nothing.

Robert Kirkpatrick — Fri, 20 Mar 2026 09:52:54 +0000

Seventy-four percent of new webpages now contain AI-generated content.

Most of it gets zero traffic.

Not penalized. Not flagged. Not removed. Just invisible. Zero clicks. Zero reads. The content exists, technically. Nobody finds it.

I have been building AI writing systems since before it was fashionable, and I have watched smart people make the same mistake over and over. They automate the output. They ignore the outcome.

Eighty-seven percent of businesses are using AI for SEO content now. The volume is staggering. The results are concentrated in a small percentage of those operations.

This is the gap nobody talks about. Not because it is a secret. Because it requires admitting something uncomfortable.

The Content Flood Is Real. The Results Are Not.

Here is what the data says: sixty-five percent of marketing professionals say their biggest concern about AI content is quality and authenticity. They are right to be worried.

But they are misidentifying the problem.

The issue is not that Google penalizes AI content. Google has stated publicly that it does not penalize AI-assisted content by default. The issue is that readers do.

It is not about spelling. It is not about grammar. It is about whether the writing feels like it was written for anyone in particular.

Most AI content is not written for anyone. It is written for the keyword.

What AI Content Usually Gets Wrong

I have read hundreds of AI-generated articles in the past year. I can spot them inside three sentences. Not because of technical tells, though those exist. Because of a structural emptiness.

The article knows things. It does not know you.

That structure is not wrong. It is just completely forgettable.

In the absence of that specificity, readers bounce. And when readers bounce, rankings follow.

The Authenticity Problem Is Measurable Now

Here is where it gets interesting.

The content that gets picked up by AI citation engines shares the same DNA. It is specific. It is direct. It makes a claim in the first paragraph. It answers the question without burying the answer.

Why Most People Are Generating Content Wrong

The mistake is using AI to write more of the same.

You take a keyword. You tell the AI to write a fifteen-hundred-word article about that keyword. The AI does. You publish it. You do this forty times. You wonder why nothing is ranking.

The content is not bad. It is generic. And generic is the one thing you cannot afford to be in a market where everyone has access to the same tools.

The Specific Problem With Voice

There is a technical version of this problem and a practical version.

The technical version is that AI writing has patterns. Not just phrases like "it is worth noting" and "in today's rapidly evolving landscape." Those are easy to remove. The deeper patterns are structural: the tendency to hedge every claim, to present multiple perspectives on everything, to avoid taking a position.

Real writing takes positions. It says this and not that. It is written by someone with an opinion.

The practical version is simpler. If your content could have been written by anyone, it reads like it was written by no one.

What Actually Works Right Now

None of this is complicated. It is just not what you get when you hand a keyword to a model and wait.

The filter I use before publishing anything:

If the answer is no, the article is not ready.

The Numbers That Tell the Story

Fifty-seven percent of SEO professionals say competition has increased significantly because of AI.

What is surprising is that only sixteen percent of companies are tracking AI search performance. Everyone is generating content. Almost nobody is measuring what happens to it.

The ones who are measuring are finding something clear: the content that converts from AI referrals is converting at twenty-three times the rate of traditional search traffic.

That is an extraordinary opportunity. And it belongs entirely to people who write content worth citing.

What You Should Do Differently

Stop generating. Start arguing.

That article will outperform ten generic ones. Every time.

The content flood is not slowing down. The gap between content that works and content that does not is widening. The tools exist to end up on the right side of it.

I Ran My Free Squeeze Scanner Against a Pro Trader's Live Calls. It Matched Every Single One.

Robert Kirkpatrick — Tue, 17 Mar 2026 04:42:55 +0000

Most trading tools promise to find what the pros already see. We decided to test that claim with real data, in real time, against real traders.

There's a moment every retail trader has experienced. You're scrolling through a Discord server, watching a pro trader call out a stock right as it breaks. Ten minutes later the ticker is up forty percent. You pull up your chart, squint at the indicators, and realize the setup was right there the whole time.

You just didn't see it fast enough.

That frustration is exactly why we built SqueezeAlert, a free TTM Squeeze scanner that runs eighty-five stocks through real-time squeeze detection, momentum analysis, and volume surge monitoring. We wrote about the tool when we launched it. But building something is one thing. Proving it works against traders who do this for a living is something else entirely.

So we ran an experiment.

The Setup

We joined a private trading Discord run by a professional trader with a documented track record. Not a guru. Not an influencer selling courses. A trader who posts his positions, his entries, his exits, and his receipts. His community includes experienced scalpers and swing traders who collectively move through dozens of tickers per session.

The plan was straightforward. Every time a pro trader called out a stock, we ran it through SqueezeAlert and logged exactly what it showed. Squeeze state, momentum direction, RSI, volume ratio, signal strength. Everything timestamped. No cherry-picking. No hindsight bias.

We logged every comparison in a validation file that grew throughout the day.

The Results: 100% Alignment on Squeeze Detection

Out of every stock where a professional trader made an active call that involved squeeze mechanics, our scanner matched. Not most of them. All of them.

Here's what the data looked like.

MU (Micron Technology)

The lead trader had this as his biggest active position, up over 180% from his entry. Our scanner showed MU sitting on a twenty-one bar squeeze, the longest compression in our entire universe. Momentum rising, MACD bullish crossover, RSI at 56 with room to run.

His top position was the scanner's top signal. Neither knew about the other.

ONDS (Ondas Holdings)

A community member flagged ONDS early in the session. Our scanner confirmed fourteen bars of active squeeze compression with rising momentum. The lead trader jumped in with "Always happy to help!" when we shared data that aligned with his thesis.

SqueezeAlert had the data before the conversation even started.

IREN (Iris Energy)

Called alongside NBIS as a strong play. Eleven bars in squeeze, bullish MACD crossover, momentum rising. The pro's bullish thesis matched the directional signal exactly. IREN also had a $9.7 billion Microsoft contract backing the fundamental case.

HCWB (HCW Biologics)

This one was the showstopper.

HCWB showed up early with a four-bar active squeeze building. Volume was surging at nearly twenty times the daily average. While we watched, the squeeze fired. Signal strength jumped to ninety out of a hundred.

In the Discord, an experienced scalper bought 1,000 shares at $0.93 and sold at $1.07 for a $143 profit. Then the stock triggered a circuit breaker halt at the exchange. The scalper had sold right before the halt. His response: "Ninja finger."

The scanner tracked the entire progression. Four bars building. Momentum rising. Then the fire. What the Discord showed in real time confirmed exactly what the indicators predicted.

JZ (Jiuzi Holdings)

A seasoned day trader casually dropped the JZ ticker while discussing risk management. SqueezeAlert lit up. Fired squeeze. Maximum signal strength of one hundred. Momentum, MACD crossover, volume surge, bullish direction. Everything confirmed.

He mentioned it in passing. The scanner flagged it as the highest-conviction setup of the entire day.

What About the Misses?

Not every stock the pros traded was a squeeze play. The scanner correctly identified those too.

WNW went from $1.44 to over $12 on a single day. The lead trader caught most of that move. But SqueezeAlert showed zero TTM Squeeze compression, which was accurate. WNW was a pure news-driven momentum play, not a volatility compression breakout. It did confirm a 19.9x volume surge, validating that the move was real even though the setup wasn't squeeze-based.

CTMX showed no squeeze but massive volume at 12.2x average. AIRS, same story, 16x volume. All correctly categorized as momentum plays, not squeeze setups. Different animal.

This matters because a tool that flags everything as a buy signal is useless. Knowing what something isn't tells you as much as knowing what it is.

The Full Portfolio Comparison

We didn't stop at individual calls. The lead trader posted his entire portfolio, and we ran every position through SqueezeAlert.

Nine of his stocks showed active squeeze compression. Six of those nine aligned with bullish or neutral signals. MU, up 182%, was the longest active squeeze at twenty-one bars. SNDK, up 167%, showed as a fired squeeze at strength eighty.

Where the scanner showed caution, he had already trimmed. Where it showed bearish signals, he was underwater. The alignment wasn't just on entries. It mapped to how he managed risk.

What Nobody Else Is Showing You

Every article about the TTM Squeeze explains how Bollinger Bands slip inside Keltner Channels. Dozens of Medium posts walk you through Python implementations. YouTube is packed with tutorials.

None of them test the indicator against live professional traders in real time.

That's what we did. Not another explainer. A validation study. One day of data, logged tick by tick, comparing automated detection against human pattern recognition from traders who do this full-time.

The result: when the setup was a genuine TTM Squeeze, the scanner caught it. Every time. In some cases, before the pros discussed it publicly.

What This Means for Retail Traders

You don't need to pay $84 to $254 per month for premium scanners. You don't need to flip through fifty charts every morning hunting for compression setups. And you definitely don't need a $500 Discord to know which stocks are coiling.

You need a scanner that works. One that catches what the pros catch and correctly ignores what isn't a squeeze, so you're not chasing momentum plays with the wrong strategy.

We built that. It's free. It runs eighty-five stocks. And on its first day of live validation against professional traders, it went four for four on squeeze detection with zero false positives.

The data speaks for itself.

SqueezeAlert is a free TTM Squeeze scanner built by TotalValue Group LLC. It runs at totalvalue.com/squeezealert and requires no account, no subscription, and no credit card. The scanner is for educational purposes only and does not constitute financial advice. Always do your own research and consult a financial advisor before making trading decisions.

Tags: Stock Trading, TTM Squeeze, Technical Analysis, Trading Tools, Retail Trading

UTM Campaign: squeeze-scanner-vs-pros

Google Built a Generation of Searchers. AI Is Building Something Different.

Robert Kirkpatrick — Tue, 17 Mar 2026 04:42:48 +0000

By Robert Kirkpatrick | TotalValue Group LLC

I caught myself doing it again last week.

I had a question about cash flow timing for a project I'm starting. Without thinking, I typed it into ChatGPT the exact same way I would have typed it into Google three years ago. Short. Keyword-heavy. No context. Just: "cash flow timing small business project."

Got an answer. Fine answer. Generic, applicable to anyone, useful to no one in particular.

Then I stopped and actually thought about what I had just done. I had treated a system that could know my business, my industry, my goals, my risk tolerance, and my current project constraints... like a search bar.

That's the problem I want to talk about.

Google Trained You. And It Trained You Well.

Spend two decades using a tool and you stop thinking about how you're using it. Google taught an entire generation to compress their actual question into five words and scan the results for something close enough to helpful. We got good at it. We learned the grammar of the search box, how to phrase things so the algorithm would understand, how to skim a results page in four seconds and find what we needed.

That skill is real. It's also completely wrong for AI.

Gartner is projecting that traditional search engine volume will drop twenty-five percent by 2026, as users shift toward generative AI tools. That projection isn't based on wishful thinking. It's based on what people are already doing. Thirty-seven percent of consumers now start searches with an AI tool instead of Google, according to a recent study cited in Search Engine Land. Twenty-nine percent of all ChatGPT conversations fall into the "practical guidance" category, meaning people are asking it how to make decisions, not just what something means.

The behavior is shifting. What isn't shifting, yet, is the mental model most people bring to the table.

There's a Difference Between Searching and Asking

Here's the simplest way I can explain it.

When you search, you're querying a database. You want the database to find you the closest match to your words. The better you are at choosing your words, the better your results. The database doesn't care who you are. It doesn't remember your last session. It has no idea what you're trying to build or why you're asking.

When you ask, you're starting a relationship. You can give context. You can say: here's what I'm working on, here's what I've already tried, here's the constraint I'm working around, here's what matters to me. The system can hold that context and actually use it. The answer changes based on who you are and what situation you're in.

That gap, between a generic answer and a contextual one, is the entire ballgame.

Eighty-two percent of Gen Z users, according to Adobe's 2026 data, prefer AI tools that give direct answers over traditional search. That's not just a preference for faster results. That's a preference for answers that feel relevant to them specifically. They're not searching. They're asking.

What One-Off Questions Actually Cost You

Most people are leaving most of the value on the table.

They fire a question at ChatGPT or Claude. They get an answer. They close the tab. Tomorrow they come back with a new question, starting from scratch again, with no context carried forward.

Every session they're essentially meeting the AI for the first time.

Think about the difference between asking a random stranger for business advice and asking your accountant who's known you for three years. The stranger might give you technically correct information. Your accountant gives you an answer calibrated to your situation, your history, your actual numbers. The information content might overlap, but the usefulness does not.

The people who are actually winning with AI right now have stopped treating it like a search box and started treating it like a standing advisor. They give it context once. They build on that context over time. They don't ask one-off questions, they run the same advisors on every relevant problem.

That is a completely different workflow. And it produces completely different results.

What an Advisory Layer Actually Looks Like

This is where things get concrete, because "treat AI like an advisor" is easy to say and hard to actually implement without a structure.

An advisory layer has three things a search box doesn't.

Persistent context. The AI knows who you are before you ask anything. Your business type, your goals, what you've tried, what's worked, what's off the table. You give this once, in a system prompt or a structured setup, and you don't repeat yourself every session.

Defined roles. Not every question needs the same kind of answer. A financial question needs a different analytical frame than a marketing question. An editorial question needs a different voice than a technical one. A well-built advisory layer assigns specific roles to specific domains, so the AI isn't trying to be everything at once.

Checkpoints. This is the one most people skip. An advisor doesn't just answer your question and disappear. A good one pushes back, asks what you're actually trying to accomplish, flags things you might not have considered. You can build that behavior in. You can instruct the AI to challenge assumptions before giving a recommendation, or to surface risks alongside solutions.

When those three things are in place, you stop getting generic answers to context-free questions. You start getting output that sounds like it was built for your situation, because it was.

That's what the CORE Operating System is built around. It's a structured prompt system that gives you the persistent context layer, the role definitions, and the checkpoint behaviors out of the box. You don't have to engineer it from scratch. The architecture is already there.

The Other Side of This Equation

Here's something most articles about AI search don't mention, because they're focused on the user side.

If people are asking AI for recommendations instead of Googling for options, the question isn't just "how do I use AI better." It's also "how does AI know to recommend me?"

McKinsey's data says forty-four percent of consumers now prefer AI search for buying decisions. That's a massive chunk of decisions being filtered through a layer that doesn't work the way Google works. There are no blue links. There are no ads. The AI synthesizes what it knows and surfaces what it thinks is the best answer.

If your business isn't part of what AI knows, you're not even in the running.

This is the parallel problem. Building an advisory layer for yourself makes you more effective. But if you're running a business, you also need to build the layer that makes AI recommend you to others.

Make AI Recommend You is a prompt-based system I built specifically for this. It trains you to feed AI the kind of structured, specific, credibility-building information that makes it more likely to surface your business, your work, or your expertise when someone asks a relevant question. The search-to-advisor shift isn't just changing how you ask. It's changing who gets found.

Why This Shift Matters More Than People Realize

I'm not a futurist. I don't make predictions about what AI will do in ten years. I'm a data analyst who pays attention to what's already happening.

What's already happening is this: people are asking AI what to buy, who to hire, what to read, how to fix things, and what decisions to make. Twenty-nine percent of all ChatGPT conversations are about practical guidance. The platform has nine hundred million weekly active users as of early 2026 and processes two billion queries a day.

The behavior is there. The volume is there. The question is whether you're using it like a search bar or like an advisor. And whether, if you're running something, you're showing up when people ask.

The infrastructure for both of those things is smaller and simpler than most people expect. It's not an enterprise AI project. It's not six months of implementation. It's a set of structured prompts that you build once and actually use.

Go back to that cash flow question I mentioned at the start. Here's what it looks like when I ask it right:

"I run TotalValue Group LLC, a digital products company focused on AI prompt systems. I'm starting a project with a three-to-four month runway before revenue. My current concern is whether to front-load expenses in month one or spread them across the quarter. Given that I'm bootstrapped and want to preserve runway, what cash flow sequencing would you recommend?"

Same underlying question. Completely different answer. Because that answer is built for me, not for anyone who could have typed those five words.

That's the shift. It's not complicated. It just requires you to stop treating AI like Google.

Try It Before You Build It

If you want to start somewhere, the free version of the AI Signature Scrub is a fast way to see what your current AI output actually looks like through an analytical lens. It's not the advisory layer tool, it's the output quality layer. But it gives you a concrete sense of what structured AI use produces versus what unstructured use produces.

The full toolkit, including the CORE System, is at kirkpatrick3.gumroad.com.

The website for TotalValue Group is here if you want to see what we're building.

Robert Kirkpatrick is the founder of TotalValue Group LLC and builds AI prompt systems that replace work you'd normally pay a consultant to do. He's a data analyst by trade who got tired of watching people fight AI tools that were designed to help them.

I Watched Companies Burn $270 Billion on AI With Nothing to Show for It. Then I Built Something for $39.

Robert Kirkpatrick — Mon, 16 Mar 2026 02:55:29 +0000

By Robert Kirkpatrick | TotalValue Group LLC

In 1963, Ford Motor Company showed up to Le Mans with a pile of money, the best engineers a blank check could attract, and the full weight of the world's largest automaker behind them. They had a purpose-built racing car. A motorsport budget that would make your eyes water. Absolute confidence they were going to win.

They lost. To Ferrari. Again.

So Henry Ford II found Carroll Shelby.

Shelby wasn't richer than Ford. He didn't have a factory or a research department or a hundred engineers on staff. What he had was something simpler: the ability to look at an engine Ford already owned and see what it could do if everything around it was engineered correctly. He took what existed, built the right system around it, and the GT40 swept Le Mans three years running, 1966 through 1968.

That story has been bouncing around in my head for months. It's the only accurate way I know to describe what's happening with AI right now.

Most companies are Ford circa 1963. They have the budget. The infrastructure. The compute. The vendor contracts. They have everything except the Shelby part. And it's costing them.

The Numbers That Should Embarrass Every Boardroom in America

I'm a data analyst. I don't make arguments on feeling. So here's what the actual research says about what companies are getting for the hundreds of billions they're pouring into AI.

MIT published a report tracking enterprise generative AI projects and found a ninety-five percent failure rate. Not "didn't quite hit targets." Not "fell short of hopes." No measurable financial return within six months. Ninety-five percent.

S&P Global found that forty-two percent of companies scrapped most of their AI initiatives in 2025. The year before, that number was seventeen percent. The scrapping rate more than doubled in a single year, even as the budget kept climbing.

IDC says eighty-eight percent of AI proof-of-concepts fail to make it to production. Large enterprises lost an average of seven-point-two million dollars per failed initiative. The average company abandoned two-point-three of them in 2025 alone.

Meanwhile, Gartner projects that enterprise spending on AI application software will nearly triple to two hundred seventy billion dollars in 2026. The spending is accelerating. The failure rate isn't moving.

Here's the part that gets me. McKinsey dug through the data and found that only six percent of organizations qualify as "high performers," meaning they're actually capturing significant value from AI. Six percent. The other ninety-four percent are burning capital while watching case studies about the six percent.

If a traditional business had a ninety-four percent failure rate, we'd call it a crisis. In AI, we call it "an exciting time of transformation."

Small Businesses Got a Different Kind of Problem

Big companies are throwing money at AI and watching most of it disappear. Small businesses are doing something arguably worse. They're standing on the sidelines completely.

A survey found that eighty-two percent of businesses with fewer than five employees say AI is "not applicable" to their business. Not "too expensive." Not "too complicated." They literally can't see the opportunity sitting in front of them.

The businesses that are using AI aren't exactly crushing it either. Sixty-eight percent of small businesses now use AI tools regularly, but seventy-seven percent have no formal policies, no measurement framework, no training program in place. They're using tools the way someone uses a wrench they found in a parking lot. It works, technically. But you wouldn't build a company on that.

Here's where it gets interesting. Among small businesses that actually adopt AI with some intention behind it, eighty-seven percent report a positive business impact. Eighty-seven percent.

That gap is wide enough to drive a GT40 through. Most businesses either can't see AI's potential, or they're using it backwards. The ones who get it right almost always come out ahead. The variable isn't the AI. It's the system around it.

What the Six Percent Know That the Rest Don't

McKinsey published something worth reading carefully. Companies that achieved significant returns from AI were twice as likely to have redesigned their workflows before selecting an AI model. Before. That matters.

They didn't buy an AI tool and then scramble to figure out how to use it. They looked at their actual work, mapped out where the friction was, identified the specific task they needed AI to handle, then found the right tool for the job.

BCG found the same pattern from a different angle. Successful AI transformations put seventy percent of their effort into upskilling people, updating processes, and shifting culture. The technology itself was the last thirty percent. Most companies invert that completely. Ninety percent on the technology, then they wonder why nothing changes.

This isn't surprising if you've spent any time working with AI. The tool doesn't know your business. It doesn't know what "good" looks like for your specific output. It doesn't know what you've already tried, what your customers care about, what tone your brand should carry. You have to encode all of that into the system before the AI becomes useful.

That encoding is the work. Most people skip it.

The Shelby Principle (What Ford Was Missing)

Carroll Shelby looked at the Ford GT40 and saw the same thing a good data analyst sees when a client hands them a mess of data and says "make sense of this."

The raw material is fine. Nobody built the right structure around it.

Shelby didn't invent a new engine. He engineered the suspension, the aerodynamics, the driver feedback systems. He built pit strategy, refined team communication, obsessed over weight distribution. He built the intelligence layer that turned an expensive piece of machinery into something that could actually win.

That's what I do at TotalValue. Less glamorous context. AI and prompt systems instead of racecars. Same principle.

Every product I've built answers the same question: what does the intelligence layer look like for this specific problem? Not "how do I get ChatGPT to do a thing." What system do I need so that AI produces the right output, every time, for this particular use case?

The difference matters. Asking ChatGPT to write you something and running it through a system built to analyze your writing for AI signature patterns, pacing problems, structural gaps, character voice distinctiveness... those are two completely different activities. One is typing into a box. The other is infrastructure.

The Bulletproof Writer system I built does that second thing. It runs your manuscript or content through eight analytical engines, scores it on a Success Probability formula, and surfaces the specific things that will make AI-generated or AI-assisted writing fall flat with readers. It doesn't replace your voice. It tells you where your voice disappeared and what to do about it. That's the Shelby principle applied to content.

What $39 Actually Gets You (And Why It Matters in 2026)

I want to be honest about what I'm selling and what I'm not.

I don't have Ford's budget. TotalValue is a small operation. I'm one person with a data background and a fairly aggressive bias toward building things that actually work rather than things that look impressive in a pitch deck.

What I've built is the system layer. Pre-engineered AI workflows that wrap around the tools you're already using and give them a job to do. Specific rules. Specific output standards. Specific checks built in.

Here's the contrast I keep coming back to: companies are spending an average of four-point-two million dollars on AI initiatives that get abandoned before they produce a dollar of value. A seven-million-dollar initiative that returns less than two million is considered a win in some boardrooms.

I built ten products that work. Each one targets a specific use case. Each one runs on AI infrastructure that costs me roughly nothing per month. I priced each one at thirty-nine dollars.

That's a data point, not a sales pitch. The difference between a seven-million-dollar failure and a thirty-nine-dollar system that works isn't compute power or model capability. It's whether someone built the intelligence layer correctly.

If you want to see what the system layer looks like before spending anything, the free AI diagnostic tool at the link below runs a quick analysis of where your current AI setup is breaking down. Takes five minutes. Doesn't ask for a credit card.

The Real Bubble (And When It Pops)

The AI bubble conversation has been going on for two years now. People keep asking whether AI is overhyped, whether the spending will crash, whether the technology can actually deliver what it promises.

Wrong question.

AI works. The data on that is clear. Eighty-seven percent positive impact when small businesses do it right. Three-point-seven times ROI when organizations apply it to the correct workflows. Companies that redesign their processes before touching the technology succeed at double the rate of those who don't. The technology is fine.

The bubble isn't in AI. The bubble is in the assumption that buying access to AI is the same as knowing what to build with it.

Ford had everything Shelby had. Except Shelby. They had the money, the machinery, the engineers. What they were missing was someone who could look at what already existed and engineer the right system around it.

Ninety-four percent of companies are still in that position. Capable tools. Expensive infrastructure. No Shelby.

The companies that survive 2026 won't be the ones who spent the most. They'll be the ones who figured out that the intelligence layer, the system that tells the AI what to do and how to do it and what good looks like, is the actual product.

Carroll Shelby didn't have Ford's budget. He had something Ford couldn't buy: he knew exactly what to build and why.

That's the only AI strategy that works right now.

What to Do With This

If you're a small business owner who hasn't figured out where AI fits in your actual workflow, start with the free diagnostic. It asks a few questions about what you're doing and what you need, then gives you a direction rather than a list of tools to try.

If you're already using AI and finding that your output sounds robotic, your content isn't performing, or you keep getting the same generic responses no matter how you phrase the question, the system layer is what's missing. That's what TotalValue products are built to add.

The store is at totalvalue.com/products. Free diagnostic is at the link below. The website has more context on what each system does.

None of this requires a large budget or a technical background. That's the whole point. The Shelby principle works at any scale. You just have to be willing to build the system before you expect results.

Links:

Free AI Diagnostic: totalvalue.com/products
TotalValue Store: totalvalue.com/products
Website: totalvalue.com