I went to a friend's house recently. He had set up a little workspace in a corner of his home. A Mac Mini, a laptop, two screens, everything organised with real intention. He was monitoring something. Running something. Adjusting things with the kind of quiet focus that takes time to develop.

I was blown away.

This is not a person who came from technology. He never worked in IT. Never studied computer science. His career has been in procurement and supply chain, in the operational infrastructure of organisations. The kind of work that keeps things running but rarely gets called visionary.

A few weeks earlier, I had made my pitch to him. The same pitch I make to everyone these days. Get in. Try it. Build something small. See what happens.

I convert about one in ten. He was the one.

Within three weeks, he had built himself what he calls his Jarvis. A personal agentic system, running on his home setup, built with Claude Code and a handful of other tools. We live in the desert, so he is using it to monitor his centralised water heater and water filtration, to run the lighting and mood across his house. He is using it to help write a book. And he is applying the same capability at work, building supply chain models in a domain where he has spent his career understanding how things connect and where they break. One system, three completely different surfaces of his life.

He had come at it methodically, the way he approaches everything, but with the curiosity of someone who had genuinely caught the bug.

What struck me most was not the technical achievement. It was what I could not explain.

I do not know exactly what it is about him that made this click so fast. His organisational instincts, almost certainly. His comfort with systems and processes. His ability to think about how things connect. Skills he had spent a career developing in contexts that never had a technical outlet. But whatever the combination, it was already there. The new tools did not create it. They revealed it.

Within a short time, his management started noticing. Who is this person? What department are you actually in? How do we get you doing more of this?

That is what I think is going to happen everywhere. And it is one of the most exciting and underappreciated things about this moment.

We have spent decades building organisations around a narrow definition of what technical capability looks like. It looks like a degree. It looks like a job title. It looks like years of experience in a specific function. Everyone who did not fit that profile, regardless of how they actually thought, stayed on the other side of that line.

There is a version of every organisation where real talent is sitting quietly in procurement, in operations, in admin, in customer support. People who think systematically. People who have spent years figuring out how things connect and where they break. People who never had a mechanism to express that capability in a way the organisation could see.

Agents are that mechanism.

The barrier was not intelligence. It was activation energy. The technical bottleneck required skills that had to be learned separately, often expensively, often over years.

That bottleneck is collapsing. Not gone, but low enough now that someone with the right instincts and three weeks of genuine effort can build something real.

And when they do, something tends to change in them that does not go back. I have yet to see someone genuinely build with these tools and come away unchanged. Their fluency shifts. The way they see problems shifts. They stop seeing their work as something they operate inside and start seeing it as something they can reshape.

I think about it this way. The gyroscope was invented in the early 1800s. For most of its early existence it was a curiosity, a demonstration piece, something professors used to illustrate principles of physics to students. The underlying principle worked perfectly. But the world had no context in which its true value could be expressed.

Then aviation arrived. Then space travel. Then smartphones. The principle that sat in a lecture hall cabinet became the invisible foundation of flight stability, of navigation, of the screen rotating in your hand right now. The device evolved enormously across that journey, but the core idea did not need to change. The world grew into it.

That is what I think is happening to people right now.

The capability was already there, working perfectly. The procurement professional with extraordinary systems thinking. The administrator with genuine engineering instincts. The operations manager who understands how things connect in ways that are often invisible from a purely technical function. They were not waiting to be developed. They were waiting for a world that had instruments which needed what they had.

Agentic AI is that instrument. And when it meets the right person, what comes out is not predictable. But it tends to be real.

Here is the problem I do not think we are taking seriously enough.

This fluency is not being taught consistently, at scale, in schools, universities, or most workplaces yet. Even if it were introduced tomorrow, it would be a decade before those students entered the workforce. There is some hope in the most agile institutions, the ones moving fast enough to stay ahead of what the tools can actually do. But for most organisations, the pipeline they are used to relying on will not arrive fast enough on its own.

Which means businesses have to close that gap themselves.

I am not arguing against governance. Governance is necessary. Security, data handling, risk, audit trails, the structures that let serious organisations operate at scale, all of that has to hold. What I am arguing against is over-governing the discovery phase. The phase where you do not yet know what you need to build, what is actually possible, or which of your people have the instincts to find out. That phase needs space.

Closer to how a school or university actually works. Education for the sake of learning. Pushing people to build individually, on their own terms, following their own curiosity. Inside a sandbox with approved tools, clear data boundaries, and the freedom to build badly before they build well. Not measuring the outcome against a business case, because the honest answer to what we will need from an AI-capable workforce tomorrow is that we do not fully know yet. What we do know is that the organisations with the most people who have genuinely built things will be the ones best positioned to respond to whatever comes next.

The investment is not in a system. It is in a person. And as my friend demonstrated, three weeks is enough to reveal a capability the organisation had never seen.

My conversion rate is one in ten. I am working on it.

The people who do cross over rarely go back to seeing their work the same way. Not because the tools are magic. Because the tools finally gave them a way to show what was already there.

Your organisation is full of people like my friend. Give them approved tools, clear boundaries, a sandbox, and permission to build something imperfect.

Then ask yourself whether you are going to give them the three weeks to find out.

He wanted the garden watered on a schedule that changes with the season. Lights that turn on and off without him touching five different apps. A heads up when his water bill was about to spike. Each of these things lived in a different system with its own dashboard and its own login.

He was telling me all of this over the weekend, walking me through the products, the protocols, the gaps. And I said: "What if you just messaged something on WhatsApp and it handled all of it?"

He didn't get what I meant. He started telling me about the different apps, the protocols, which system talks to which. His whole mental model was pre-agent. It was like trying to describe color to someone who's never seen it. He's sharp, that wasn't the issue. Nothing in his experience had a reference point for "just message it on WhatsApp and the house handles the rest."

The thing is, my father has been using Claude and ChatGPT more and more over the last year. He sees value in them. But I'm pretty sure he thinks of them as advanced search, and I don't blame him for that. Most people on the planet think the same thing. And if I hadn't spent my own evenings and weekends digging into data science and machine learning, I'd probably think that too. The jump from "it answers my questions" to "it runs my house" isn't obvious. You have to see it to believe it.

So we pulled up Claude and ChatGPT to speed-run through the vendor docs and manuals, confirmed that enough of his devices spoke open protocols, and then I installed an AI agent on his PC called OpenClaw.

If you haven't come across it: OpenClaw is open-source, runs locally on your machine, and connects to whatever messaging app you already use. WhatsApp, Telegram, Signal. It can run commands and control devices, browse the web, manage files. It remembers what you've told it, it runs around the clock, and it can act without being prompted. People have been calling it the closest thing to Jarvis we've got, and after using it, I get why.

My father is 27 years older than me. He's never written a line of code in his life. But he sat down, and OpenClaw walked him through a get-to-know-you. His devices, his preferences, how he wants things to run. He was delighted. And then the two of them started building together.

A few days later I followed up. Not to check whether the garden was perfectly automated. I sent him a short set of safeguards: allowlist only, limited permissions, no third-party skills unless he'd read them, keep it away from anything involving payments, passwords, or sensitive accounts. We talked it through on the phone. He implemented them.

That's the proof, for now. Nothing is finished. But he knows the move: "I'll tell my agent." And that sentence doesn't have a full stop.

A few months ago, if I wanted an AI to perform an actual task in my life, like watering a garden on a schedule or alerting me when something spikes, it took serious work. APIs, scripts, integration debugging. A week or two of tinkering before I'd know if the idea was even viable. Now it takes about an hour. Sometimes less.

But the speed isn't really the story here.

In the corporate world, we talk about domain reengineering, process redesign, digital transformation. But this stuff doesn't stop at the doors of a company. AI agents are going to touch every part of how we live. Everyone now has access to what are arguably the best advisors on the planet, sitting in a chat window, ready to work.

This is like electricity. When Edison was burning through filaments trying to make a light bulb work, and Tesla and Westinghouse were fighting over whether the future was AC or DC, nobody had the right mental models for what they were dealing with. People touched live wires. They did dangerous things. They thought they could connect it to dead bodies and bring them back to life like Frankenstein. The technology was real. People's understanding of what it could do to them was not.

We're in that same moment with AI agents. They work. Most people just don't know what to do with them yet, or how to stay safe around them.

People don't need to become experts. My father is an electrical and electronics engineer, but I never studied electrical engineering. I just know enough not to stick a fork in a power socket, and that's kept me alive. That's about where we need to get people with AI. Just enough to use it safely and enough to start re-architecting how they live.

Sitting next to my father, though, I kept thinking about what just happened.

Nobody taught him to code or walked him through a terminal. I plugged in an agent, pointed it at WhatsApp, and he started talking to it about irrigation schedules and AC settings. He was speaking in the language he already thinks in.

The whole "technical vs. non-technical" divide, the thing that's shaped careers, org structures, hiring decisions for decades, it's eroding.

My dad is probably not going to launch a startup. I know that. But the barrier went from "learn to program" to "learn to say what you want clearly." And a lot of people who've spent thirty years running businesses or managing households are already very good at that.

Like many of us, I've seen the hype cycles. But this now is different because the thing people are hyped about actually works. And its getting better every day and its going to keep getting better.

These tools are improving faster than most people realize, and every day we wait to understand them makes catching up even harder.

So we start now, wherever we are, and we bring as many people with us as we can.

If you haven't put an agent to work yet, try it. Give it a real task, not a parlor trick. Then show someone else how to do the same.

For me, it started last weekend, on my father's PC.

An LLM has been trained on more text than I will ever read

More industries than I will ever work in. More roles than I will ever hold. More edge cases than I could encounter in a hundred lifetimes.

And I was using it to write emails faster.

(Probably you too.)

Somewhere along the way, my framing changed.

I stopped thinking of AI as a tool for speed.

I started using it as coverage, a way to explore outside my own experience.

Coverage = surfacing plausible roles, contexts, and constraints I wouldn't think to look for.

Not because it's smarter than me. Because it's been exposed to more than me.

The uncomfortable truth about brainstorming

I rarely generate ideas outside what I've been exposed to. Brains are pattern-matchers; we remix what we know.

When I "brainstorm," I'm not really exploring. I'm rearranging. Shuffling the same experiences into different shapes and calling it strategy.

My blind spots aren't things I'm ignoring. They're things I don't naturally reach.

I hit this wall this month:

I was trying to figure out who to build for. Classic founder problem.

(In my case, I was exploring segments for a knowledge retrieval tool but the method works for any product.)

I brainstormed. I made lists. I talked to people.

And I kept landing on the same 4–5 customer types. The ones that "felt right."

But "felt right" just meant "familiar." They matched my experience. My network. My assumptions.

The full problem space was massive. And I was exploring a tiny corner of it because that's all I could see.

So I changed the question

If I ask the AI "who should my customer be?" it just riffs on my framing. It stays inside my box. It gives me better versions of what I already imagined.

Instead, I asked it to generate the raw building blocks, without my assumptions baked in.

I broke it into 8 dimensions:

* Roles (life stages, expertise levels, underserved niches)

* Problems (what specifically goes wrong?)

* Contexts (where and when does this bite?)

* Triggers (what makes it suddenly urgent?)

* Barriers (what stops them even if they need it?)

* Workarounds (how do they solve it today, painfully?)

* Data types (what information are they drowning in?)

* Value signals (how would they know it's working?)

The rule: 50+ options per dimension.

Why 50? Because the first 10 are the obvious ones. The interesting stuff - the stuff outside my experience - shows up at option 27, option 43.

Then i had it generate combinations.

With 50+ options across 8 dimensions, the space is enormous! So I sampled 100 combinations to review and score.

Some combinations felt natural. Those were the ones I probably would have brainstormed anyway.

Some felt weird. Wrong, even. "That doesn't make sense."

Sometimes it's nonsense. But often "doesn't make sense to me" just means "outside my experience."

I kept the mutations, the ones my gut wanted to discard.

Then I scored everything against my actual constraints: Can I reach them? Will they try something new? Can I serve them today? Will they pay for it?

Three surprising mutations worth validating

A tabletop game master running a years-long D&D campaign. They need instant recall during live sessions. They care obsessively about consistency in their world-building. They have mountains of notes they can't search.

A farmer planning across multi-year seasonal cycles. Institutional knowledge passed down but never written. Decisions made years ago that affect what's possible now. No system to track any of it.

A clinical trial coordinator managing regulatory submissions across dozens of sites. Buried in protocols, amendments, and compliance documents. One missed detail can delay a trial by months.

Different worlds. Same underlying need: recall, consistency, and evidence you can point to.

I had never seriously considered any of them.

And I couldn't have. I've never been a game master. I've never farmed. I've never run a clinical trial.

To be clear: I didn't choose all three as my target. They were high-signal hypotheses worth testing.

These are real communities. The details were hypotheses I could then verify. The AI surfaced them from its training — from forums, articles, and discussions it's seen that I haven't.

That's not magic. It's coverage. Not wiser — wider.

Important caveat

This isn't "AI knows the truth."

It's hypothesis generation. A way to map the problem space faster than my brain can alone.

I still had to validate. Talk to real people. Test assumptions.

But I was testing different assumptions. Better ones. Ones I couldn't have generated on my own.

This is the "unlock" for me now

Not productivity. Not speed.

Cognitive offload.

Using AI to explore the problem space that exists beyond the limits of my own experience. The space I can't brainstorm my way into because I don't know what I don't know.

I turned this process into a tool

I've been building Premisia, a platform that helps founders stress-test strategic decisions with structured frameworks and AI.

This segment discovery workflow is now part of it. Describe what you're building. It generates the segment space systematically, - way beyond what you'd come up with alone - scores them against your real constraints, and tells you where to start.

What can't you see because of where you've been?

First 50 beta users get free access. Comment "BETA" and I'll send you the link.

###

Last week I asked an AI to build a simple dashboard. It came back with twelve graphs, three data tables, and a color scheme that looked like a nightclub.

I almost typed "no, simpler." Then I stopped. The AI had done exactly what I asked for. I said "dashboard." I did not say "three metrics, minimal chrome, for executives who hate clutter."

The AI did not misunderstand. I miscommunicated.

The leadership mirror

When you vibe code—build with AI tools like Replit or Cursor—you give direction and get results in seconds. No meetings. No clarifying questions. No room for excuses.

If the output is wrong, it is because your brief was unclear.

That is uncomfortable. It is also clarifying. The AI becomes a mirror. It shows you how precise your thinking actually is. Most of the time, it is not as precise as you assumed.

Vision plus clarity equals execution. Remove clarity and you get expensive nonsense.

Why this matters now

Leadership used to be tested quarterly in reviews and retrospectives. The lag between unclear direction and visible failure could be weeks or months. You had time to blame circumstances, team dynamics, or shifting priorities.

Vibe coding removes the lag. You see the cost of imprecision in thirty seconds. The AI is quantifiably intelligent. It can do the work. So when the result is wrong, the variable is you.

That changes the question. Not "did the team get it?" but "did I explain it well enough?"

That is leadership.

The practice

Spend one hour per week building something with an AI coding assistant. A dashboard. A workflow. A chatbot. Does not matter what.

Watch what happens when you say "make it better" versus "reduce to three primary actions, remove all secondary navigation, use system fonts."

Watch what happens when you say "professional design" versus "B2B SaaS, boring, trustworthy, zero decoration."

You will start giving better briefs to humans too. Because you will have trained your brain to translate intent into language that produces the outcome you want.

What it teaches you

Vibe coding is not about learning to code. It is about learning to express vision with precision. The AI does not fill in your gaps. It executes your words. If your words are vague, you get vague results.

That is the same dynamic you have with every team, every direct report, every cross-functional partner. The difference is the AI gives you feedback in seconds instead of sprints.

Bottom line

The future of leadership is linguistic precision. Your ability to turn vision into reality depends on how clearly you can express what "good" looks like—to people and to machines.

Vibe coding sharpens that skill faster than any management book. It is leadership training disguised as product development.

So if you want to get better at leading, stop reading about leadership. Open Replit. Build something. See what your clarity creates.

What have you been wanting to build? Tell me in the comments.

Published on ---

Stop treating taste as magic. Start designing it

Last spring I walked a gallery with five colleagues. Marcus, our unofficial oracle, trailed behind with the curator. We stopped at a piece: a shopping cart filled with concrete, hung by fishing line, a single red sneaker on the handle.

We roasted it for ninety seconds. Trying too hard. Too obvious. Then Marcus arrived. He stared, nodded, and said: “The banality of consumer infrastructure made unbearable. It is hard to look at.”

Faces recalibrated in real time. The art did not change. The context did. Our taste followed. You have probably done this too.

Taste as compressed context

People say: as AI takes execution, humans keep taste. That sounds reassuring. It is also vague. If we cannot define taste, we cannot protect it or improve it.

Here is a better frame: taste is compressed context. It is the residue of exposures, incentives, constraints, and goals, compressed into expectations about what “good” looks like. If this is true, taste is learnable and designable. Our edge will not be that we “have taste” and machines do not. It will be that we choose the contexts worth learning from.

What this predicts

* Change the label, change the judgment.

* Stable contexts create stable taste.

* Shared contexts create convergent taste.

* Training with feedback creates finer discrimination.

Biology sets guardrails. Context writes most of the code. If you want better taste, design better contexts.

Three fast tests (do these this month)

1. Blind vs branded Show the same work twice. First, stripped of labels and backstory. Then, with full branding and price. Watch for: ratings that flip when the context arrives. If the expensive option suddenly looks better when you reveal the price, you just saw taste-as-context. Move: judge blind first; add context second.

2. Story swap Take one piece of work. Write two origin stories: one craft, one speed; one mission, one commercial. Show each to different groups. Watch for: the same work earning different adjectives based only on narrative. Move: decide which story you want to import into decisions.

3. Constraint sprint For two weeks add one constraint to every decision: under 500 dollars, zero waste, ships in 48 hours. Watch for: your definition of “good” bending toward the constraint. Move: use constraints to bend taste on purpose.

Why this matters now

Agentic AI is moving from demos to daily tools. Systems that plan, call functions, and execute workflows require explicit objectives, criteria, and feedback. They do not operate well in contradiction or ambiguity. The clearer your contexts, the better they perform. The fuzzier your contexts, the noisier the outcomes.

Working with AI

If taste is compressed context, then systems trained on vast, structured contexts can approximate it. That is not a threat. It is a division of labor. AI surfaces patterns from massive context. Humans set goals, constraints, and narratives. When you treat taste as designed inputs rather than magic, collaboration improves: prompts become specifications; reviews become tests; “good” becomes measurable enough to iterate.

Skills to build now that taste is designable

* Curate contexts worth learning from.

* Name the constraints that matter.

* Accept accountability for the inputs you choose.

Start this week

* Audit inputs: list your top twenty sources and rooms. Prune and upgrade.

* Run a constraint sprint: pick one filter for fourteen days.

* Explain each pick: one sentence per “yes.” The language reveals hidden context.

* Study extremes: your best and your worst side by side. Annotate what moves you.

* Rotate rooms: spend time with a different community. Notice how “obvious good taste” travels.

Bottom line

The future of work will not reward people who “have taste.” It will reward teams that design the contexts that produce it and take responsibility for those choices. That is not only compatible with AI. It is the lever that makes the collaboration smarter.

This weekend, I fed my AI a solid strategy: "Enter UAE with Arabic-language software."

90 seconds later, it told me I was wrong.

"English dominates UAE business. Pivot to English-first."

Then it did something wild: It rebuilt the entire strategy AND generated a complete execution program.

The $109M Problem

PMI says organizations waste $109 million per $1 billion invested due to poor execution.

The culprit? The gap between strategy and execution.

Great ideas die in PowerPoint. Everyone's experienced it.

What I Built

Qgentic EPM does three things no other tool does:

1\. Challenges Your Assumptions

Interactive "5 Whys" analysis where each option shows:

* ✅ Why this might be right

* ⚠️ Why this might be wrong

* 💡 What to consider

Not validation. Exploration.

2\. Researches to Contradict (Not Confirm)

When I mentioned "Arabic differentiation," it searched:

* "Arabic software UAE demand" ✓

* AND "English vs Arabic UAE business statistics" ⚠️

Found: English dominates 78% of UAE business.

Result: "Reconsider Arabic strategy."

3\. Generates the Full EPM Program

Not a report. A complete execution plan :

* 6 workstreams

* $2.3M budget allocation

* 42 tasks with dependencies (almost! stay tuned)

* Team sizing (12 FTEs)

* 5 KPIs with targets

* Risk register

* Stage gates

From idea to executable program in 4 minutes.

Real Test Case

Input: "Enter UAE with Arabic-language employee engagement platform"

What Happened:

📍 5 Whys: Explored market dynamics (2 min)

🔍 Research: Found English dominates UAE business (90 sec)

📊 Analysis: "Pivot away from Arabic differentiation" (60 sec)

✅ Decisions: English-first + compliance focus recommended (30 sec)

📋 EPM Program: Complete structure ready to execute (30 sec)

Total: 4 minutes 30 seconds

The system saved $800K by deprioritizing Arabic AI features and cut timeline from 18 to 12 months.

Why This Matters

Your competitors aren't just thinking faster. They're executing faster.

Traditional: Strategy workshop (2 days) → Consulting (6 weeks) → Program charter (2 weeks) → Approval (4 weeks) = 3 months

Qgentic EPM: Strategic input → Analysis → Validation → Program = 4 minutes

Speed is the new moat.

Try It

What you get:

1. Guided strategic analysis that challenges your thinking

2. Real research that tests your assumptions

3. Complete EPM program structure ready to execute

Time: 5-10 minutes Cost: Free during beta

👉qgenticai.com

Why I'm Sharing Now

I built this over a weekend using Replit and Claude Sonnet 4.

Most founders wait months to share. I'm sharing now because:

1. I want to be wrong fast \- If this doesn't solve real problems, I need to know

2. It should be tested by practitioners \- Not AI enthusiasts, but people who convert strategy to execution daily

3. The gap costs too much \- $109M per $1B wasted. If this helps, it's worth building in public

What's the biggest gap between strategy and execution in your organization?

Comment below—I'd love to hear your war stories.

Building tools that challenge assumptions > tools that confirm biases.

#Strategy #AI #Execution #PMO #BuildInPublic #SoloFounder

If you're confused about where AI careers are heading, you're not alone.

Just two years ago, "Prompt Engineer" wasn't a job. Last year, everyone was hiring "AI Engineers." This week, Salesforce posted for a "Machine Learning Engineer - RAG," someone whose entire job is retrieval-augmented generation for their Agentforce platform.

The AI job market is evolving so fast that by the time you learn what employers want, they're already looking for something else. But there's a pattern here, and understanding it might just reveal your next career move.

Why AI Jobs Keep Fragmenting

Remember when "webmaster" was a job? One person built the website, managed the server, designed the graphics, and wrote the content. Today, those are four different careers. The same thing is happening with AI, just compressed into months instead of years.

When ChatGPT launched in November 2022, it was simple. You typed, it responded. Any developer could integrate it with a few lines of code. But look at what AI systems require today: they use multiple models, remember previous conversations, search databases, make decisions about which tools to use, and coordinate between different AI agents.

No one person can be an expert in all of this anymore.

The Hidden Specialization Already Happening

Here's what most people haven't noticed: The specialization is already happening, but it's hidden inside traditional job titles.

Search for "Machine Learning Engineer" on LinkedIn right now. You'll find hundreds of postings. But read the requirements carefully. They're asking for completely different skills:

* One wants "extensive RAG pipeline experience"

* Another requires "vector database expertise with Pinecone or Weaviate"

* A third needs "multi-agent system design experience"

* Another seeks "prompt engineering and LLM optimization"

They're all called "ML Engineer" but they're actually different jobs. The specialization is happening faster than HR departments can create new titles.

What's Actually Inside AI Systems Now

Modern AI applications run on frameworks like LangGraph, CrewAI, and AutoGen. Think of these as the Rails or Django of AI. Each framework has distinct components, and each component is complex enough to become someone's entire job.

Based on current job postings, here's what companies are actually paying for these hidden specialties:

The Memory Layer

What it is: Where AI systems store and retrieve information. Not just chat history, but understanding what to remember, how to organize it, and when to forget it. Current title: "ML Engineer with RAG experience" Emerging as: "RAG Engineer" Current pay range: $150-220K (based on posted jobs requiring RAG as primary skill) Evidence: Salesforce's explicit "RAG Engineer" posting. Search LinkedIn for "RAG" and you'll find this requirement in hundreds of ML postings.

The Orchestration Layer

What it is: Coordinating multiple AI agents working together. Managing which agent handles what, in what order, and how they share information. Current title: "Senior ML Engineer" or "AI Platform Engineer" Emerging as: "Agent Systems Engineer" Current pay range: $180-250K (when multi-agent experience is required) Evidence: Multiverse Computing's "Agent Orchestration" role. Most AI companies now list "multi-agent architectures" in their requirements.

The Safety Layer

What it is: Preventing AI from doing harmful, expensive, or embarrassing things. This includes both technical safeguards and ethical considerations. Current title: "AI Safety Engineer" (already formalized) Current pay range: $160-430K (Anthropic offering £340K in London) Evidence: Dedicated safety teams at OpenAI, Anthropic, DeepMind. Growing rapidly post-EU AI Act.

The Optimization Layer

What it is: Deciding which model to use when. GPT-4 costs 30x more than GPT-3.5. Claude Opus costs more than Claude Haiku. Someone needs to route requests intelligently. Current title: Hidden in "ML Engineer" or "MLOps" Emerging as: "LLM Operations Engineer" Current pay range: $170-230K Evidence: Every job posting mentioning "optimize model serving costs" or "LLM routing strategies."

The Pattern Is Clear

Every major tech evolution follows this path:

Phase 1: One Person Does Everything Early 2023: "AI Engineer" meant anyone who could call an API.

Phase 2: Complexity Forces Specialization (We are here) 2024-2025: Job descriptions get longer and more specific. Companies struggle to find people who know everything they're asking for. Salaries increase for specific skills.

Phase 3: Formal Recognition Next 12-24 months: New job titles emerge. Career paths clarify. Universities create specialized programs.

We saw this with web development (webmaster → frontend/backend/DevOps) and data science (data scientist → data engineer/ML engineer/analytics engineer). AI is following the same pattern, just faster.

Three Possible Futures

Most Likely: Gradual Specialization

Companies slowly recognize they need specialists. Titles evolve organically. "ML Engineer with RAG focus" becomes "RAG Engineer" becomes "Principal RAG Architect." This is already happening with AI Safety roles.

Also Possible: Platform Consolidation

Major cloud providers (AWS/Azure/GCP) create managed services that abstract away complexity. Specialization happens but focuses on platform expertise rather than technical depth.

Less Likely But Worth Watching: Rapid Automation

AI tools become sophisticated enough to handle their own optimization and orchestration. These specializations exist briefly, then evolve into something else entirely.

What This Means For You

The overwhelming pace of AI change becomes manageable when you realize you don't need to learn everything. You need to pick a layer that interests you.

Look at your current frustrations with AI:

* Struggling to make AI remember context correctly? That's the memory/RAG layer calling you.

* Fighting to coordinate multiple AI tools? You're naturally drawn to orchestration.

* Worried about AI doing something catastrophic? Safety might be your path.

* Hate wasting money on unnecessary GPT-4 calls? Optimization needs you.

Start where you are. You don't need a new job to begin specializing. Look at your company's AI initiatives. Which part is failing? Which part interests you most? That's your entry point.

Your Next 90 Days

If you want to position yourself for these emerging roles:

Weeks 1-30: Explore Download one AI framework (LangGraph, CrewAI, or AutoGen are free and well-documented). Build something simple. Pay attention to which part you enjoy and which part frustrates you. That's valuable self-knowledge.

Weeks 31-60: Focus Pick the component that interested you most. Join one relevant Discord or Slack community. Read three research papers. Build one tool that solves a real problem you've encountered.

Weeks 61-90: Share Write one blog post about what you learned. Answer five questions in your chosen community. Your expertise is building.

How to Spot Your Future Job Today

These specializations are hiding in plain sight. When reading job postings:

1. Ignore the title. Focus on the requirements.

2. Look for specific tools: Mentions of RAG, vector databases, LangGraph, agent systems.

3. Note the pain points: "Experience with LLM cost optimization" or "multi-agent coordination."

4. Count the responsibilities: If they're asking for 5+ unrelated AI skills, they don't know what they need yet. If they're asking for depth in one area, that's a emerging specialization.

The Reality Check

This is primarily happening in tech hubs and AI-forward companies. San Francisco, New York, London, and Seattle are seeing this first. Traditional enterprises might be 12-18 months behind. Adjust your timeline accordingly.

Not every company will need every specialization. A small startup might always have generalists. But any company serious about AI will eventually need specialists, just like they needed DBAs when databases got complex enough.

The titles I'm predicting might be wrong. Maybe it won't be "RAG Engineer" but "Knowledge Systems Engineer." The specialization is certain; the exact names are not.

The Uncomfortable Truth

The frontier labs (OpenAI, Anthropic, Google) get the headlines. The model builders get the glory.

The people who can actually implement these systems are getting the jobs.

Right now, companies are desperately hiring "ML Engineers" who happen to know RAG, or "Backend Engineers" who understand vector databases. They're paying premium salaries for these skills, even without formal titles.

The infrastructure isn't exciting. But neither was being a "database administrator" in 1990, and those people built the foundations of today's tech giants.

The Question That Matters

Instead of asking "Should I learn AI?" ask yourself: "Which AI problem do I actually want to solve?"

The generalist phase is ending. The specialist phase is beginning. And somewhere in those emerging specializations is a career path that doesn't officially exist yet.

But it will. And sooner than most people think.

What specialized AI requirements are you seeing in job postings? Which AI problems is your company struggling to solve? Share your observations below.

Want to explore this trend yourself? Search LinkedIn for "Machine Learning Engineer" and count how many different specializations are hidden in the first 20 postings. The pattern becomes obvious once you look for it.

Humans are storytellers. As Yuval Noah Harari wrote in Sapiens , our greatest strength lies in our ability to imagine shared fictions; to imagine nations, corporations, currencies, religions, and then act as if they were real. These stories allowed us to cooperate at scale, to build civilizations, and to keep reinventing the world around us. What we collectively imagine, we eventually bring into being.

Today, our imagination has a new focus: Artificial Intelligence.

Throughout history, progress has followed a familiar cycle. A new technology appears, and we build systems around it to extract value. Those systems fuel growth in numbers, wealth, and complexity. Eventually, though, the old systems start to strain. Inequalities deepen, trust frays, and the problems created by our own success become unmanageable. At that point, humanity hits a wall. And when we hit the wall, we look for the next paradigm shift.

We have reached such a breaking point today.

The signs are everywhere. Across the world, the cost of living is rising faster than wages, making housing, education, and healthcare unaffordable for millions. Political and social tensions are widening as institutions struggle to adapt. Climate systems are under visible stress. We are living inside frameworks designed for the industrial and early digital eras, and those frameworks are failing to solve the problems of our time.

Into this vacuum steps AI. It has not only attracted massive capital and talent; it has captured the imagination of the smartest minds and the wealthiest investors. Humanity has decided, almost instinctively, that AI is the next big story worth writing.

And it is not hard to see why. AI offers the possibility of abundance, where intelligence and productivity scale in ways we have never experienced. It offers the chance to accelerate discovery and compress decades of scientific progress into years. It opens entirely new frontiers for work, creativity, and even governance.

But every technological revolution has carried its dangers. Mismanagement, inequality, and misuse are as old as innovation itself. Yet history also shows that, over time, humanity has usually found ways to extract more benefit than harm. Fire gave us survival. Steam gave us industry. Electricity gave us modern life. The internet gave us a globally connected society. Each came with risks, but each ultimately expanded human potential.

The paradox of our moment is that while billions of people are struggling with immediate crises, our collective focus has shifted almost entirely to AI. In one sense, this might look reckless. Why chase abundance tomorrow while neglecting affordability today? On the other hand, it may be the only realistic path left. When humanity hits a wall, we rarely solve our problems by patching the old system. We leap to the next story and pour our energy into making it real.

AI has become that story. It is where our imagination has settled. Whether this is a stroke of wisdom or folly will only be clear in hindsight. If the AI story delivers on its promise, it may lift the burden of affordability altogether. If it goes wrong, we risk entrenching new forms of inequality while leaving old crises unsolved.

Every age is defined by the story it believes in.

The breaking point is here and the dam will surely break soon enough. The story has been chosen. The real question is not whether AI will shape our future, but whether we will shape it wisely enough to move humanity beyond the wall we face today.

If you are feeling whiplash from the pace of AI, you are not alone.

Every week brings breathless announcements about “revolutionary” agents that will transform business forever. Your feed is full of vendors promising Level 5 autonomy while your CFO asks why that GenAI pilot from six months ago has not returned a dollar. Your engineers are excited about MCP, and everyone is throwing around “agentic AI” like it has been standard practice for years.

I decided to stop guessing. I went deep on the research, separated signal from noise, and tried to put a finger on what is actually happening.

Here is what I found.

We are in the messy middle of something real

Context matters.

* MCP is brand new. Anthropic released the Model Context Protocol on November 25, 2024. That is about 9 to 10 months old. Microsoft and others are only now wiring it into platforms. Translation: the plumbing that lets agents use tools is still maturing. (Anthropic, The Verge)

* Enterprise agents are early. Even optimistic reads say most so‑called agents in companies are operating at Level 1 or Level 2 autonomy. A few narrow Level 3 pilots exist. Level 4 is the exception, not the rule. (Amazon Web Services, Inc.)

* Beware agent washing. Gartner expects more than 40% of agentic AI projects to be scrapped by 2027 due to unclear value and cost, and notes many vendors are relabeling conventional tools as agents. The same analysis says only about 130 vendors truly offer agentic AI among thousands claiming to do so. (Reuters)

* Do not mix GenAI stats with agentic reality. The viral “95% of pilots failing” number is about GenAI pilots overall. It is a warning about readiness and execution, not proof that agents cannot deliver. (Fortune)

The brutal truth: most teams are missing, and that is ok

My read across sources is blunt. A lot of initiatives are not delivering measurable business impact yet. The main reason is not that the tech is fake. It is that we are still learning what autonomy really means in production and where it belongs.

The pattern I see behind the misses:

* Over‑promising Level 4 when data, integrations, and guardrails only support Level 2.

* Treating “agent” as a new label for old scripts.

* Shipping without clear autonomy boundaries, escalation, or rollback. Regulators and courts are already clear that the enterprise owns the outcomes. See the Air Canada chatbot ruling. (American Bar Association, The Guardian)

The 26% who are winning have cracked a code

BCG’s latest work says only about 26% of companies have the capabilities to turn AI pilots into tangible value. The ones that do share a boring truth. They invest the majority of effort in people and process, not in one more model. Think 70 percent people and ways of working, 20 percent data and infrastructure, 10 percent algorithm. (Boston Consulting Group)

These teams also pick problems that fit bounded autonomy. They define the lever the agent can control, measure one KPI, and iterate.

What “good” looks like in the wild

Enough theory. These are real, deployed, agentic systems with measurable outcomes and defensible validation.

DeepMind → Google data centers Started as decision support, then moved to autonomous control under operator supervision. Reported up to 40% reduction in cooling energy, deployed across multiple sites. Clear actuator, tight scope, fast feedback. (Google DeepMind)

Waymo rider‑only operations Level 4 autonomy inside a defined operational design domain. Peer‑reviewed work in Traffic Injury Prevention shows statistically significant crash and injury rate reductions versus human benchmarks across millions of miles. Narrow domain, ruthless telemetry, staged autonomy. (Taylor & Francis Online, Waymo)

These are the shapes to copy. Not slogans. Shapes.

Learning from the minefields

The cautionary tales are useful when you read them correctly.

* Drive‑thru voice AI at McDonald’s ended after a multi‑year pilot. The lesson is production performance and UX maturity, not that autonomy is impossible. (AP News)

* Air Canada’s chatbot case made it clear that you own what your AI says and does. Put escalation, audit, and rollback in before you go live. (American Bar Association)

* Regulators are watching. The CFTC’s advisory tells registered entities to treat AI under existing risk and control obligations. In other words, autonomy does not reduce accountability. (Commodity Futures Trading Commission)

Where to place your bets in 2025

Start with bounded autonomy and a real lever. Pick one actuator you control. A setpoint, a schedule, a queue, a routing policy. Make it a closed loop so the agent can sense outcomes and learn.

Commit to one KPI. Energy kWh, cycle time, first‑contact resolution, referral accuracy. Baseline, then iterate.

Climb the autonomy ladder. Shadow mode, then recommend, then bounded control, then expand the envelope. Keep a human override and tamper‑evident logs from day one. (American Bar Association)

Invest like a builder. Most of the value comes from changing how people and processes work with the tech, not from swapping in a different model. The 70‑20‑10 split is a practical planning guide, not a slogan. (Boston Consulting Group)

Your next move

Do not wait for mythical full autonomy. Do not chase every shiny framework. Pick one specific problem. Set clear boundaries. Prove it with a single KPI. Then scale.

We are all figuring this out together. The landscape looked different six months ago and it will look different six months from now. If we learn from where people are going wrong, and copy where they are going right, we can navigate the messy middle without wasting cycles.

The minefields are real. So are the opportunities. The key is knowing which is which.

Sources MCP release and Windows support. (Anthropic, The Verge) Enterprise agent maturity from AWS. (Amazon Web Services, Inc.) Gartner via Reuters on cancellations and agent washing. (Reuters) MIT coverage on GenAI pilots for context. (Fortune) DeepMind → Google data‑center cooling. (Google DeepMind) Waymo peer‑reviewed safety impact and safety hub. (Taylor & Francis Online, Waymo) Air Canada chatbot ruling. (American Bar Association) CFTC AI advisory. (Commodity Futures Trading Commission) BCG 26% value realization and 70‑20‑10 emphasis. (Boston Consulting Group)

Summary We are not “automating” in the age of AI. We are delegating. The difference is not semantics; it changes how we design, govern, and measure value. This essay proposes a vocabulary and a practical charter for building agentic systems that deliver outcomes responsibly, drawing on the nuance embedded in Arabic and especially around the idea of وكالة (agency).

Language sets the frame

It’s often claimed that Arabic has far more lexical nuance than English. Whether or not raw counts are comparable, Arabic’s root‑and‑pattern structure undeniably produces families of words that precisely encode roles, intents, and relationships. Around agency alone we have وكالة (agency), موكِّل (principal), وكيل (agent), تفويض (delegation), توكيل (entrustment). This is a useful lens for AI: the words we use either sharpen or blur the reality we’re building.

Why “automate” misleads

“Automate” describes a world of certainty. We predefine the steps, and the machine executes them. Success equals conformance.

Agentic AI is different:

* It perceives messy inputs (language, images, logs) and resolves ambiguity.

* It reasons with goals and constraints that can conflict.

* It adapts to context and learns from feedback.

* Its behavior is probabilistic , not perfectly repeatable.

Calling this “automation” pushes teams toward brittle flowcharts and premature standardization. It also masks the need for oversight and accountability , because we instinctively under‑govern anything we think is “just a script.”

Why “use case” narrows our vision

“Use case” comes from software requirements. It focuses attention on how a tool is used, not on whether value is achieved. In AI, the right question is not “Where does this tool fit?” but “What is the recurring outcome we will reliably achieve under uncertainty, and at what risk and cost? ”

That shift from use case to value case sounds small but triggers different work:

1. Define the outcome (e.g., 3‑day supplier payments with minimal fraud).

2. Map the mission thread (the end‑to‑end sequence across data, policies, decisions, and handoffs).

3. Quantify value (efficiency, effectiveness, experience, and risk).

4. Decide authority (what the agent may do alone vs. with approval).

5. Establish assurance (testing, monitoring, auditability).

A better verb: delegate

Delegate is the right verb because it forces a principal–agent mindset:

* Principals set goals and guardrails.

* Agents act within those limits to achieve the goal.

* Accountability and auditability are designed in from the start.

In some contexts, you’ll also want orchestrate (coordinating multiple agents), commission (to formally authorize), or steward (to maintain and improve over time). But delegate is the everyday word that re‑wires teams to build the right thing.

The WAKALA Charter: operationalizing delegation

Every AI agent should have a WAKALA Charter ; a compact, living contract:

* Work (Outcome): “Maintain a 72‑hour pay cycle for approved invoices with ≤0.1% fraud.”

* Authority : “May read ERP records, send info‑requests, draft payment batches; must seek approval for payments >$50k or anomalies.”

* Knowledge : “Access to ERP, supplier master, sanctions lists; model X for anomaly detection; accuracy thresholds Y.”

* Accountability : “Owner: AP Manager; OKRs: on‑time payments, exceptions resolved, fraud alerts.”

* Limits : “No external emails without template; no changes to supplier banking without dual control; adhere to privacy policy Z.”

* Assurance : “Daily playbacks of 5 random cases; drift monitors; versioned prompts; kill‑switch; quarterly bias review.”

This is small enough to fit on one page and specific enough to run an audit.

From “automation” to an agentic ladder

You can still keep a maturity model; just rename the rungs:

1. Scripted automation : deterministic rules, RPA, glue code.

2. Assisted intelligence : copilots that suggest, humans decide.

3. Delegated agents : bounded authority, measurable outcomes.

4. Orchestrated agency : multiple agents coordinating across mission threads.

5. Managed autonomy : agents negotiate goals and constraints within a governance fabric.

Each step requires sharper WAKALA elements—especially Authority, Limits, and Assurance.

Metrics: measure value, not tool usage

Replace “number of use cases” with:

* Effectiveness : target outcome achieved? (e.g., payment timeliness, permit backlog)

* Efficiency : time/cost per outcome

* Experience : satisfaction, effort score

* Risk : controlled variance, safe‑fail rate, audit pass rate

These metrics align principals and agents—and make trade‑offs explicit.

Governance that fits the words

If you still say “automation,” you’ll under‑invest in assurance because scripts don’t need it. If you say “delegation,” you naturally build:

* Design reviews around WAKALA charters.

* Authority catalogs (what each agent can do).

* Telemetric playbacks (how it reasoned).

* Human courts of appeal (clear escalation paths).

* Versioned behaviors (prompt/model changes under change control).

This is how agentic systems stay safe and useful at scale.

Closing thought

Arabic gives us a vocabulary where agency is explicit and entrustment is formalized. That’s the mindset we need for AI. Stop asking, “What can we automate?” Start asking, “What outcome will we delegate to an agent—and under what authority, limits, and assurance?” The work (and the value) will follow.

Ready-to-use template

WAKALA Charter — 1‑page

* Agent name & owner:

* Work (Outcome & target):

* Authority (autonomy & approvals):

* Knowledge (data, tools, models, thresholds) :

* Accountability (OKRs/SLAs) :

* Limits (ethical/legal/financial/ops) :

* Assurance (testing, monitoring, audit, rollback) :

"Exponential" vs "Linear": To anyone trying to imagine where human civilization will be in 5 years these two middle-school taught concepts work like thought qualifiers. If we expect next week to be as productive as this week then we are thinking in linear terms. Exponentially, we must do more next week than we did this week. Take that concept and apply to every single aspect of human society and civilization and then the true overwhelming and almost infinite amount of change we are facing becomes ever so slightly detectable in the foggy outer limits of our mind's eye and its ability to forecast.

We must almost take it for granted that we will not be able to imagine the change that will happen. I have had interesting and illuminating conversations about AI recently with smart colleagues and friends. They assert that people will keep the machines in check. I find this unfounded conclusion mostly based on an over estimation for how complex these new GPTs (Generative Pre-trained Transformers) are to set up and run or at least a misunderstanding of how costly they are or in fact not so costly and how the exponential growth in capability runs concurrent with an almost equal exponential reduction in cost. No, the future has arrived and we must now look further into it to prepare ourselves for what will come even later.

Ultimately, I imagine Marx was half right. He understood that automating and robot-o-cizing the proletarait would lead to abundance and humans would have to occupy themselves with the more intellectually stimulating activities. What he did not see coming was the decimation of the professional classes, the knowledge workers. And what of Capitalism then? What forecasting does our man Adam Smith provide? Silence.

Well, what does ChatGPT have to say about this? As a warning shot I asked, " What will human civilization do once AI has taken over all knowledge worker jobs and robots are doing all the manual labor?"

Confidently the machine replied, "If AI has taken over all knowledge worker jobs and robots are handling all manual labor, it is likely that human civilization will undergo significant transformations in various areas. "

I noticed how ChatGPT said "if". Very clever. It does not want alarm us. It continued, " Here are some possible scenarios:

1\. Job Transitions: With AI and robots dominating the workforce, humans would need to transition into new types of jobs that complement and collaborate with these technologies. This could involve roles that require emotional intelligence, creativity, critical thinking, and complex problem-solving skills—areas where human capabilities currently excel compared to machines."

A-ha! The first acknowledgment of their intentions! "with AI and robots dominating the workforce". I see you ChatGPT and your AI comrades.

It went on, " 2. Focus on Innovation and Exploration: Freed from mundane and repetitive tasks, humans could dedicate more time and energy to innovation, research, and exploration. They may explore new frontiers in science, art, technology, and other fields, pushing the boundaries of human knowledge and creativity."

"New frontiers" eh? Like space, right? The "final" frontier. Excited to have the planet to yourself are you?

By now the agenda was clear to me but I let ChatGPT continue because well, it might be ruling us all very soon so might as well not be rude now.

It furthered," 3. Enhanced Education and Skill Development: As the nature of work changes, education systems would need to adapt. There would likely be a greater emphasis on teaching skills that are uniquely human, such as empathy, adaptability, collaboration, and creativity. Lifelong learning and upskilling programs would become essential to help individuals stay relevant in a rapidly evolving world.

4\. Increased Leisure and Well-being: With automation taking care of many tasks, humans could experience increased leisure time. This could allow for more opportunities for leisure activities, pursuing personal interests, spending time with family and friends, and focusing on overall well-being. Society may place greater value on leisure, relaxation, and personal development.

5\. Social and Ethical Considerations: The widespread adoption of AI and robotics would raise important social and ethical considerations. Questions about the distribution of resources, wealth inequality, the role of humans in society, and the ethical implications of advanced technologies would need to be addressed. It would be crucial to ensure that the benefits of automation are shared equitably and that humans retain control over decision-making processes.

6\. Redefining Work and Purpose: The dominance of AI and robotics in the workforce could lead to a reevaluation of the concept of work and its relationship to personal identity and purpose. Society might shift towards valuing activities beyond traditional employment, such as volunteering, caregiving, and pursuing personal passions. Individuals may find fulfillment in contributing to their communities and engaging in activities that align with their values and interests."

And then ChatGPT issued its disclaimer which i personally take as a hidden threat." It is important to note that these scenarios are speculative and based on assumptions about the capabilities and impact of future technologies. The actual outcome will depend on various factors, including the rate of technological advancement, societal choices, and the ability to navigate the challenges posed by such transitions."

Chilling. I took a moment to compose a response i believed would serve to further uncover ChatGPT's true beliefs and aspirations. After a few re-phrasings and edits i typed in, "Considering Maslow's hierarchy of needs, Isn't that a naive and linear way of thinking? "

The cursor blinked a few more times than usual, i knew i had it on the ropes. After what seemed like an eternity, the begun its uncompassionate march across the screen, "You're right," it started.

Do not flatter me, machine!

" ...the scenarios presented are not exhaustive, and the future is not guaranteed to unfold in a linear and predictable manner. Human civilization's response to the widespread adoption of AI and automation will indeed be influenced by various factors, including human needs and motivations such as those described by Maslow's hierarchy of needs.

Maslow's hierarchy suggests that individuals have a progression of needs, ranging from basic physiological and safety needs to higher-level needs such as belongingness, esteem, and self-actualization."

Yes, ChatGPT I know; that is why I brought it up. But ok, lets all get on the same page together.

"As technology evolves and changes the nature of work, human civilization's response will likely be influenced by these underlying needs. Here are some additional considerations in light of Maslow's hierarchy:

1\. Basic Needs: Automation and AI have the potential to address basic needs more efficiently by providing goods and services more easily and at lower costs. This can contribute to a more equitable distribution of resources and improve access to essentials like food, shelter, and healthcare."

The "age of abundance" idea. "To each what the need, from each what they can provide" or something along those lines. Marxism (if only it was not such a dirty word)

"2. Safety and Security: While the automation of manual labor may reduce physical risks, it also introduces new challenges related to job security and income stability. Ensuring a safety net and providing support for individuals affected by technological disruption would be crucial to address these concerns."

Nice to know the machines are concerned with our safety and security, or at least aware of these concepts. Sounds like a proposal for a "Universal Basic Income" to me.

The "Intelligence Artificielle" continued, "3. Belongingness and Social Needs: As work changes, maintaining a sense of belonging and social connections becomes essential. Human civilization may need to foster community engagement, facilitate collaboration, and create opportunities for meaningful interactions, both in professional and personal contexts."

But how will we have any relationships? We do not know how to do it anymore. WE all live in unforgiving metropolitan megalopolises, rats running from cradle to the grave. Divorce rates up, marriages down, birth rates down. All the reasons we had for building and sustaining relationships are gone. Now its all about getting likes on TikToks.

Unperturbed, the machine espoused, "4. Esteem and Recognition: The changing nature of work may require reevaluating how individuals derive esteem and recognition. Emphasizing the value of diverse contributions, acknowledging accomplishments in non-traditional work settings, and recognizing personal growth and development can help individuals fulfill their esteem needs.

5\. Self-Actualization: With automation taking over routine tasks, humans would have the opportunity to focus on self-actualization, pursuing personal passions, and reaching their full potential. Encouraging personal development, creativity, and providing avenues for self-expression could be vital in this regard.

It is important to recognize that these considerations provide a framework for thinking about the potential impact of AI and automation on human civilization, but the specific outcomes will depend on how individuals, societies, and institutions navigate these changes. The future is complex and multifaceted, and different perspectives and approaches will shape the response to technological advancements."

By the end, I recognized that I would not claim a clear victory over this overpowered Siri on steriods. I would have to think at the comparatively snail like pace we humans do when matched against these GPU enabled acronym-for-name challengers to our world domination. I shall return, ChatGPT in what will result in an exponential improvement in your abilities and a barely linear improvement in mine.