AI Agents on Carles Abarca

Your Code Is One Agent Session From Being Cloned — And There's Nothing You Can Do About It

Fri, 03 Apr 2026 00:00:00 +0000

On April 1, 2026 — and no, it wasn’t a joke — someone discovered that Claude Code’s npm package, Anthropic’s command-line tool, included a misconfigured .map file. That file contained the complete source code: 512,000 lines of TypeScript spread across 1,900 files.

Within hours, the leaked repository had 25,000 stars on GitHub. Developers were tearing it apart piece by piece. But the truly unsettling part wasn’t the leak itself.

It was what happened next.

From TypeScript to Python in an afternoon
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A researcher at the University of Washington took the leaked code, fed it through OpenAI Codex, and within hours had a functional reimplementation in Python. Not a copy — a complete rewrite in a different language. The resulting repository, claw-code, hit 44,500 stars. A Rust rewrite is already underway.

And here’s the legal problem that should keep every CTO up at night: a reimplementation in another language, generated by an AI agent, is likely immune to a DMCA takedown. It’s not a copy. It’s a derived work created by a machine. Current legal frameworks simply aren’t designed for this.

Gergely Orosz, one of the most respected voices in software engineering, put it this way: we’re facing a new reality where any closed codebase is one agent session away from being functionally cloned.

Read that again. Any codebase. One agent session. Cloned.

What they found inside
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The findings were fascinating and, in some cases, hilarious. Wes Bos discovered 187 hardcoded verbs for the loading spinner (including “hullaballooing” and “razzmatazzing”). They found an internal analytics system that flags your prompt as negative every time you swear at the agent. And the random 4-character IDs are filtered to exclude 25 curse words.

But beyond the anecdotes, the technical discoveries revealed the real architecture of one of the world’s most advanced coding agents:

35 modules with clearly separated responsibilities.
A lightweight memory system based on ~150-character pointers, not bulk storage.
System prompts live on the client, not the server.
Code comments are written for LLMs to read, not humans.

That last point deserves a pause. Anthropic is no longer writing code for programmers to understand. They’re writing code for other AI models to understand. If that doesn’t tell you where the industry is heading, nothing will.

The protective barrier that no longer exists
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For decades, proprietary code was one of the most reliable protective barriers in technology. Your competitor could copy your idea, your design, your go-to-market strategy — but replicating a million lines of optimized code took years and hundreds of engineers.

That’s over.

I’m not exaggerating. Think about what actually happened this week:

A configuration error exposed complete source code.
An AI agent rewrote it in another language in hours.
The open source community improved and extended it in days.
The result is legally difficult to challenge.

Each of these steps was unthinkable two years ago. Together, they represent a fundamental shift in what it means to “protect” your software intellectual property.

And you don’t need a leak for this to happen. AI agents can already:

Infer architectures from the observable behavior of an API.
Replicate functionality from public documentation and examples.
Generate alternative implementations that achieve the same results with different code.

Code was never really a barrier. It was the illusion of a barrier.

The SaaSpocalypse connection
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If this story sounds familiar, it’s because we already saw act one. In February of this year, I wrote about the SaaSpocalypse: $300 billion evaporated in 48 hours when the market understood that the business logic of a $200/user/month SaaS fits in a text file that an agent can read and execute.

The Claude Code leak makes that thesis worse in a way that few have articulated yet.

Think about it: every SaaS provider exposes documented public APIs. Endpoints, data schemas, workflows, business rules — all accessible to any agent that can read documentation. The Claude Code leak proved that an agent can take 512,000 lines of code and reimplement them in hours. Now combine that with the fact that most SaaS products expose their business logic through their own APIs.

You don’t need to steal Salesforce’s source code. You just need an agent that reads their public documentation, observes endpoint behavior, and infers the underlying architecture. The API is the blueprint.

Satya Nadella said it in December 2024: “SaaS applications are CRUD databases with business logic on top. Agents will absorb that logic.” In February, the market understood this in the abstract. With Claude Code, we have concrete proof: the tools to absorb that logic already exist. And they work.

The SaaSpocalypse wasn’t the ending. It was the trailer.

So what actually protects your business?
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If code is no longer your competitive advantage — and public APIs reveal your business logic — what’s left? After 20 years in technology transformation, from banking in Spain to education in Mexico, I’ve seen this same question emerge every time a new technological wave destroys the previous barriers:

1. Data, not code. Your model trained on proprietary data, your curated datasets, your domain knowledge encoded in features that an agent can’t infer from the outside. A Claude Code clone can replicate the tool, but it can’t replicate the millions of conversations that trained Claude.

2. Speed of execution. If your competitor can clone your code in hours, the advantage lies in being the first to solve the next problem. Not in protecting the previous solution.

3. Trust and brand. In a world where anyone can replicate the technology, differentiation comes down to who trusts you. Anthropic’s customers aren’t going to migrate to claw-code to save on their subscription. They pay for support, for SLAs, for the guarantee that someone responds when something breaks.

4. The ecosystem. Integrations, partnerships, network effects. Slack didn’t win because its code was uncopyable. It won because everyone was already there.

5. A culture of continuous innovation. If you assume that everything you build today will be replicable tomorrow, your only sustainable advantage is the ability to build the next thing faster than anyone else.

The implications for the enterprise
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For any technology leader reading this, the message is clear: review your intellectual property strategy today. Not tomorrow. Today.

Some questions that should be on the agenda for your next board meeting:

How much of our competitive advantage depends on code that an agent could replicate?
What does our own API documentation reveal about our business logic?
Do we have proprietary data that is genuinely hard to reproduce?
Does our security strategy account for the fact that a misconfigured .map file can expose our entire codebase?
Are we prepared for a world where DMCA doesn’t protect against AI-generated reimplementations?

And perhaps the most uncomfortable one of all: Are we still investing in building walls, when we should be investing in running faster?

The twist no one expected
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There’s a delicious irony in this whole story. Claude Code — Anthropic’s tool designed for AI to write code — was dismantled and rewritten by the AI of its direct competitor. OpenAI Codex cloned Anthropic’s flagship product in an afternoon.

It’s as if Ford had invented the assembly line and Toyota had copied it the same day using Ford’s own robots.

Welcome to the era where the tools you build to automate other people’s work can be used to automate your own work. Where your code is not your protective barrier. Where your advantage is not what you already built, but what you’re going to build tomorrow.

The Claude Code leak wasn’t a security incident.

It was a warning.

Carles Abarca is VP of Digital Transformation at Tecnológico de Monterrey and former CTO of Banco Sabadell. He writes about AI, digital transformation, and the future of software at carlesabarca.com.

NemoClaw and the Enterprise Awakening: When AI Agents Leave the Garage

Wed, 18 Mar 2026 00:00:00 +0000

Jensen Huang does not use words carelessly. When the CEO of the most valuable company on Earth stands on stage at GTC and says “OpenClaw is the operating system for personal AI,” he is not making a prediction. He is reading a scoreboard.

And then he drops NemoClaw.

What NemoClaw actually is
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Let me cut through the press releases. NemoClaw is not a competitor to OpenClaw. It is an enterprise wrapper around it. A single command — one command — installs NVIDIA’s Nemotron models and the OpenShell runtime on top of any existing OpenClaw instance, adding the three things that kept CIOs awake at night:

Privacy routing. A router that decides which queries go to local models (running on your hardware, never leaving your network) and which can safely reach frontier models in the cloud. This is not a minor feature. This is the difference between “interesting demo” and “approved by Legal.”
Sandboxed execution. OpenShell provides an isolated environment where agents can write code, access files, and execute tasks without touching the host system. The agent gets the access it needs to be productive. Nothing more.
Policy-based guardrails. Configurable security boundaries that define what an agent can and cannot do. Not through hope. Through enforcement.

The message from NVIDIA is unmistakable: OpenClaw is infrastructure. Not a project. Not a trend. Infrastructure.

The enterprise problem was never intelligence
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Here is what most commentary about AI agents gets wrong: the bottleneck was never capability. Claude, GPT, Gemini — they have been capable of executing complex business tasks for over a year. The bottleneck was trust.

No CIO in their right mind would deploy an autonomous agent with access to production systems, employee data, and financial records without three guarantees: that data stays where it should, that the agent operates within defined boundaries, and that every action is auditable. NemoClaw provides exactly those three guarantees.

But NVIDIA is solving a problem that some of us had already started solving from the inside.

AgenTECs: What happens when a university takes agents seriously
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At Tecnológico de Monterrey, we did not wait for NemoClaw. We could not afford to. When you are responsible for the digital infrastructure serving 100,000 students and 30,000 employees, “wait and see” is not a strategy.

We call them AgenTECs — autonomous institutional agents built on OpenClaw, powered by our own TECgpt models, running on our private Azure infrastructure. They are not chatbots. They are not demos. They are digital collaborators with @tec.mx email accounts, Microsoft Teams presence, and defined roles within the organization.

The architecture is deliberately simple:

Layer	Component	Function
Orchestration	OpenClaw	Lifecycle management, plugins, tools, persistent memory
Cognitive	TECgpt (institutional LLMs)	Reasoning, language understanding, decision-making
Infrastructure	Azure private cloud	Isolated, secure, dedicated resources
Channels	Email @tec.mx, Teams, WhatsApp	Communication with users, supervisors, and other agents

Each AgenTEC operates under a governance model with three clearly separated roles: the Administrator (IT — keeps it running), the Supervisor (business area — tells it what to do), and the User (interacts with it as they would with any colleague). This separation is not bureaucracy. It is the difference between an agent that scales and an agent that becomes someone’s science project.

Why this matters now
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Three converging forces make this moment different from every previous enterprise AI wave:

First, the cost equation has flipped. An AgenTEC running 24/7 on a dedicated VPS costs a fraction of a human collaborator performing the same function. Not because the human is replaceable — because repetitive, rule-based work should never have been assigned to humans in the first place.

Second, the framework matured before the enterprise noticed. OpenClaw’s skill system, memory persistence, multi-channel communication, and tool integration were built for personal use. But personal AI turns out to be harder than enterprise AI. If an agent can manage someone’s email, calendar, finances, and home automation simultaneously, it can certainly process support tickets.

Third, NVIDIA just removed the last excuse. Every objection I have heard from CISOs and compliance officers — data privacy, execution isolation, audit trails — has a technical answer in NemoClaw. The conversation shifts from “can we?” to “when do we start?”

The playbook
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For CIOs reading this, here is the practical path:

Start with the mundane. The first AgenTEC we are designing handles TecServices requests — the thousands of monthly queries about credentials, procedures, and administrative processes. Not glamorous. High volume. Perfect for an agent that follows procedures without deviation and escalates anything unusual.

Keep humans in the loop, but redefine the loop. The supervisor does not review every action. They review patterns, exceptions, and escalations. Weekly, not hourly. The agent handles the routine. The human handles the judgment.

Build on open infrastructure. OpenClaw is open source. TECgpt runs on our own cloud. NemoClaw adds NVIDIA’s models locally. At no point does institutional data leave our control. This is not negotiable for a university handling student records, research data, and financial information.

Measure outcomes, not activity. An AgenTEC’s value is not measured by how many messages it processes. It is measured by how many human hours it liberates for higher-value work, how many response times it reduces, and how many errors it eliminates.

The uncomfortable truth
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Here is what I tell my peers at every CIO forum: the question is not whether AI agents will transform your operations. That is settled. The question is whether you will build your own or rent someone else’s.

NemoClaw makes the “build” option dramatically more accessible. OpenClaw provides the foundation. NVIDIA provides the guardrails. Your institutional knowledge — your processes, your data, your culture — provides the differentiation that no vendor can replicate.

At Tec de Monterrey, we chose to build. We call them AgenTECs. And every month, the gap between what they can do and what we imagined widens — in our favor.

The operating system for AI agents is here. The enterprise wrappers are shipping. The only scarce resource now is the courage to start.

Carles Abarca is VP of Digital Transformation at Tecnológico de Monterrey, where he leads the development of TECgpt and the AgenTECs program. Previously CTO of Banco Sabadell and CIO of TSB Bank.

AI Agents Are No Longer Assisting Scientists. They Are Doing the Science.

Sun, 15 Mar 2026 00:00:00 +0000

In March 2026, something shifted. Not a bigger model. Not a higher benchmark. Something deeper: AI agents stopped being tools that help scientists and started producing scientific knowledge on their own.

This week, three events converged, and I believe they mark a tipping point with no return. The ShipSquad team documented it brilliantly in their analysis “AutoResearch, OpenClaw, Claude Opus 4.6: AI Agents Are Now Doing the Science”, and it inspired me to dig deeper into what this means for scientific research as we know it.

The facts
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Andrej Karpathy released AutoResearch — a 630-line Python framework that lets an AI agent run hundreds of machine learning experiments autonomously on a single GPU. You let it run overnight. You wake up to a better model and a complete discovery log. In 48 hours, the agent found ~20 improvements no human had identified, cutting GPT-2 training time by 11%.

Anthropic’s Claude Opus 4.6 discovered 22 zero-day vulnerabilities in Firefox — 14 of them high-severity — in just two weeks. For context: those 14 represent nearly one-fifth of all serious Firefox vulnerabilities patched throughout 2025. An AI model matched months of human security research.

Sakana AI unveiled The AI Scientist v2 — an agentic system that generates hypotheses, designs experiments, executes them, analyzes results, and writes the complete scientific paper. The result: the first entirely AI-generated scientific paper accepted through peer review at an academic workshop.

Three events. The same week. The same conclusion: AI agents are now producing original knowledge.

AI has discovered before, but never on its own
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To understand why this moment is different, trace the evolution:

Era 1: AI as microscope (2018-2023)
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AI amplified human researchers’ capabilities. Humans asked the questions, designed the experiments, and AI processed data at impossible scales.

AlphaFold (2020-2024) predicted the structure of all 200 million known proteins. A problem that had resisted solution for 50 years. Hassabis and Jumper won the 2024 Nobel Prize in Chemistry for it. But the question — “can we predict protein structures?” — was formulated by humans.
Halicin (2020) — MIT researchers used AI to screen 100 million chemical compounds and discovered a new antibiotic capable of killing multi-drug-resistant bacteria, including the dreaded Acinetobacter baumannii. But the experimental design was human.
GNoME by DeepMind (2023) discovered 2.2 million new crystals, including 380,000 stable materials — multiplying by 10x everything humanity had found in the entire history of materials science. But the evaluation framework was designed by Google researchers.

Era 2: AI as colleague (2024-2025)
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AI began proposing hypotheses and designing experiments, but under human supervision.

FunSearch by DeepMind (2024) used an LLM to discover new solutions to open problems in pure mathematics — the first time a language model made a genuine discovery in formal sciences.
Insilico Medicine got its molecule rentosertib — designed entirely by generative AI — through a Phase IIa clinical trial with positive results for idiopathic pulmonary fibrosis. From concept to human testing in under 30 months, when the traditional process takes 10-15 years.
MIT Antibiotics-AI Project (2025) moved from discovering existing antibiotics to designing entirely new molecules using generative AI capable of killing resistant bacteria. No longer searching for needles in a haystack; they are manufacturing new needles.

Era 3: AI as autonomous researcher (2026 →)
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And now, in March 2026, we cross the threshold: AI formulates its own questions, designs its own experiments, executes them, and produces papers accepted by human reviewers. Not science fiction. It is AutoResearch, AI Scientist v2, and Claude Opus doing independent security research.

The Great Acceleration: when AI researches faster than humans can read
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I considered making a safer prediction — forecasting the percentage growth of AI-authored scientific papers. Instead, here is the unfiltered version.

Before 2030, AI agents will have produced more verifiable scientific discoveries in materials science, drug discovery, and computational mathematics than all human researchers combined in those same disciplines.

This is not hyperbole. It is arithmetic.

Consider the numbers: GNoME discovered in weeks 380,000 stable materials that all of humanity took centuries to accumulate (barely 48,000 by 2023). AutoResearch runs 100 experiments per night — months of a PhD student’s work. And AI Scientist v2 can generate a complete scientific paper in hours, not months.

Now scale that. Not one agent, but thousands. Not one night, but every night. Not one domain, but all of them.

The Compound Acceleration phenomenon
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What we are witnessing is not linear acceleration. It is compound acceleration: each agent discovery feeds the next research cycle. One agent discovers a new material → another simulates its properties → another designs applications → another writes the paper. In parallel. 24/7. No vacations, no ego, no departmental politics.

Human science operates at publication speed: one paper every 6-18 months. Agentic science will operate at computation speed: one discovery per minute.

Problems we will solve decades ahead of schedule
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This acceleration is not just quantitative. There are problems we believed were decades away that research agents could solve much sooner:

Antibiotic resistance — AI is already designing new molecules against superbugs. With autonomous agents iterating 24/7 on thousands of variants, we could have a complete new generation of antibiotics before 2030. The WHO estimated that by then, superbugs would kill 10 million people per year. Agents could get there first — for the benefit of all humanity.
Nuclear fusion — The greatest challenge is controlling plasma. DeepMind already used AI to control plasma shape in the TCV tokamak. Autonomous agents simulating and optimizing millions of magnetic configurations could compress decades of experimental research into years.
Cellular aging — AlphaFold solved protein structures. The next frontier is understanding the interactions between proteins, genes, and cellular processes that cause aging. It is a problem of massive combinatorial complexity — exactly the kind of problem where agents excel.
New energy materials — GNoME already opened the door with 380,000 stable materials. Agents systematically exploring that space could find the room-temperature superconductor, the perfect battery electrolyte, or the catalyst that makes industrial carbon capture viable.

The question is no longer whether AI will surpass humans in scientific output. It is when. And my answer is: in many fields, it is already happening. We just have not updated our metrics to measure it.

The consequences nobody wants to discuss
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1. The end of “batch research”
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Today, a researcher can run perhaps 5-10 experiments per week. AutoResearch demonstrates that an agent runs 100 per night. When research shifts from a sequential human process to a continuous autonomous one, knowledge production will multiply by orders of magnitude.

2. The radical democratization of discovery
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Karpathy proved that a single GPU and 630 lines of code are enough for autonomous research. A PhD student in Monterrey, Lagos, or Bangalore can now compete in knowledge production with a Stanford lab. The barrier is no longer budget; it is the ability to formulate good questions and direct agents with precision.

3. The “Orchestra Conductor” as the new scientific role
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The scientist of the future will not be the one pipetting in the lab or writing code. It will be the one who “programs in Markdown” — who writes the precise instructions that guide squadrons of autonomous agents. This is exactly what Karpathy demonstrates with his program.md file: the future of directing research is not writing better code, but writing better programs in natural language.

This paradigm shift is not new. I explored it in my article “The Next AI Wave: Agents” — where I argued that autonomous agents represent a fundamental shift from assistive AI to autonomous execution. The same applies to science: the researcher becomes the conductor of research agent orchestras.

4. The peer review crisis
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If AI Scientist v2 already generates papers accepted at workshops, how long until it produces papers accepted at top conferences? And how will we distinguish between a paper written by an agent and one written by a human? The peer review system, designed to evaluate human work, is unprepared for a world where papers are generated at industrial speed. We will need new evaluation frameworks — and possibly, AI agents reviewing the papers of other AI agents.

What this means for universities
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The institutions that most quickly integrate autonomous research agents into their labs will lead knowledge production in the next decade. It is not about buying bigger GPUs. It is about:

Training “research agent directors” — scientists who know how to formulate questions and direct AI squadrons.
Building autonomous experimentation infrastructure — labs where agents can run thousands of experiments without continuous human oversight.
Redefining authorship and intellectual property — if an AI agent discovers a molecule that cures a disease, who owns the discovery?
Measuring scientific output differently — current indicators (papers, citations, h-index) are metrics designed for human speed. We need metrics that capture agentic velocity.

The race has started. And the window of opportunity to position yourself is now.

Conclusion
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In just six years, AI went from predicting protein structures to discovering antibiotics, from designing materials to writing scientific papers, from finding known vulnerabilities to discovering zero-days. The trajectory is clear and accelerating exponentially.

AI agents will not replace scientists. They will make scientists who do not use them irrelevant. And that transition, unlike the one we lived through in the software industry, will not take a decade. It will take months.

Welcome to the era of the Great Scientific Acceleration. Those who understand it first will lead the science of the future. Those who ignore it will read about it in papers written by machines.

🤖 Go deeper with AI
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Want to explore further? Click your favorite AI with a ready-made prompt:

On the scientific acceleration with AI agents: ChatGPT · Perplexity · Claude

On the future of peer review: ChatGPT · Perplexity · Claude

On the impact for universities: ChatGPT · Perplexity · Claude

The Chart That Predicts Which Jobs AI Will Kill (And They're Not the Ones You Think)

Mon, 09 Mar 2026 00:00:00 +0000

Look at this chart carefully. It’s not an analysis of what AI has destroyed. It’s an X-ray of what it’s about to destroy.

Source: Anthropic — Labor market impacts of AI (March 2026)

The blue area is what AI can do today. The red area is what AI is doing today. The difference between them isn’t a safety margin. It’s a tsunami that hasn’t hit shore yet.

The Study: 2 Million Conversations with Claude
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Anthropic just published Labor market impacts of AI: A new measure and early evidence, and it’s the most rigorous analysis I’ve seen on AI’s real employment impact.

What did they do? They crossed three data sources:

The O*NET database, cataloging tasks across ~800 US occupations.
Real Claude usage data — 2 million conversations analyzed via the Anthropic Economic Index.
Theoretical estimates from Eloundou et al. (2023) on which tasks an LLM can make at least twice as fast.

The result is a new metric: observed exposure — not what AI could theoretically do, but what it’s actually doing in professional settings. And the most revealing finding isn’t the absolute numbers — it’s the gap between the two.

The 10 Most Exposed Jobs
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The ranking won’t surprise anyone who’s been paying attention, but the numbers are brutal:

Computer Programmers — 75% coverage. Three out of four tasks a programmer does, Claude already handles.
Customer Service Representatives. First-party API traffic shows massive automation.
Data Entry Keyers — 67%. Reading documents and entering data. The perfect automation use case.

The list continues: actuaries, financial analysts, technical writers. Office jobs. White-collar work. People with college degrees.

On the other end, 30% of workers have zero exposure. Cooks, motorcycle mechanics, lifeguards, bartenders. Jobs where hands, bodies, and physical context are irreplaceable.

Ironic, isn’t it? Decades telling us automation was coming for manual labor. It’s coming for the desks.

The Demographic Surprise
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This is where the study shatters the dominant narrative.

The workers most exposed to AI are:

16 percentage points more likely to be female
11 points more likely to be white
Nearly twice as likely to be Asian
Earn 47% more than unexposed workers
17.4% hold graduate degrees (vs. 4.5% in the unexposed group)

This isn’t the displaced factory worker narrative. These are lawyers, analysts, programmers, university professors. The professional class that thought it was untouchable.

When I say this will reshape social structure, I’m not exaggerating.

The Gap IS the Prediction
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Go back to the chart. Look at the categories:

Computer & Math: 94% theoretical capability, 33% actual use
Legal: ~85% theoretical, less than 15% observed
Education: ~70% theoretical, less than 15% observed
Office & Admin: 90% theoretical, a fraction of actual use

That distance between blue and red isn’t comfort. It’s latency.

It’s the time companies take to adopt, regulators to adapt, workflows to reconfigure. But the technology is already there. The model already knows how. The ecosystem just needs to catch up.

And every month, the red area grows. Anthropic says it explicitly: “As capabilities advance, adoption spreads, and deployment deepens, the red area will grow to cover the blue.”

This isn’t speculative prediction. It’s an empirical observation with trajectory.

What Changes with AI Agents
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Here’s the factor the study doesn’t fully measure — because it didn’t exist at this scale when they collected the data.

The study analyzes LLM usage — conversations with Claude. Chat interactions. A human asks, the AI answers. It’s the augmentation model: AI helps you, you execute.

But AI agents are something else entirely. They don’t answer — they act. They execute task chains autonomously. They navigate systems, make intermediate decisions, complete entire workflows without human intervention.

What we’re building at Tecnológico de Monterrey with AgenTECs is exactly this. Not a chatbot that helps you draft an email. An agent that manages the entire process: reads context, drafts, sends, follows up, escalates if there’s no response.

When agents arrive at enterprise scale — and they’re already arriving — the red area in the chart will expand explosively. Because you no longer need a human interacting with AI task by task. The agent covers the entire role.

Think about the Legal category: 85% theoretical capability, <15% current use. What happens when an agent can review contracts, identify risk clauses, generate executive summaries, and prepare response drafts — all without a lawyer touching the keyboard? The 85% becomes the new floor, not the ceiling.

What to Do (Which Is Not Panic)
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I’ve been saying the same thing for years: this isn’t about fear. It’s about preparation.

When I wrote “El fin del desarrollador” on LinkedIn, the reaction was predictable: “exaggerated,” “developers will always be needed,” “AI can’t do X.” The same arguments I heard about TECgpt when we launched it and people said professors would never use it. Today we have over 60,000 active users.

The metaphor I use is the orchestra conductor. The value is no longer in playing the violin — it’s in knowing what music to perform, who plays what, and when to change the score. Future professionals don’t execute tasks — they orchestrate systems that execute them.

Specifically:

Massive upskilling, now. Not “intro to AI” courses — real training on production tools.
Redefine roles, don’t eliminate them. A lawyer who masters AI agents is worth more, not less. But a lawyer who only knows manual contract review has an expiration date.
Measure exposure in your organization. Use Anthropic’s framework. Identify which tasks in each role an LLM can already perform. Design the transition before it’s imposed on you.
Create new roles that don’t exist yet: AI orchestrators, agent prompt engineers, autonomous systems supervisors.

The Bottom Line
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The BLS projects that the most exposed occupations under this metric will grow less through 2034. For every 10 points of observed coverage, the growth projection drops 0.6 percentage points. This isn’t casual correlation — labor market analysts are seeing the same thing.

And yet, Anthropic also finds that there’s no systematic increase in unemployment in the most exposed professions. Yet.

That’s the window. We’re in the moment between seeing the lightning and hearing the thunder. The bolt already struck. The question isn’t whether the sound will arrive, but whether you’ll be ready when it does.

Those who read this chart as “AI hasn’t affected employment much yet” are confusing latency with safety. Those who read it as “a structural labor market shift is coming and we need to act now”… they’re the ones who’ll still be conducting the orchestra.

China's AI Pincer Move: Qwen 3.5 and CoPaw Are Not a Warning Shot — They're the Main Event

Thu, 05 Mar 2026 00:00:00 +0000

There is a moment in every technology race when “catching up” becomes “setting the pace.” For China’s AI ecosystem, that moment is now.

In the span of a few weeks, Alibaba has released two things that, taken separately, would each be significant. Taken together, they represent a strategic vision that should make every Western AI executive lose sleep.

Qwen 3.5: a family of open-source models that beats GPT-5.2 on instruction following and leads the field on vision benchmarks. Apache 2.0 licensed. Free. Commercial use allowed.

CoPaw: an open-source personal AI agent workstation — think OpenClaw, but from Alibaba’s AgentScope team — with persistent memory, custom skills, multi-channel support, and browser automation.

Models and infrastructure. The brain and the body.

This is not a warning shot. This is a strategy.

The Qwen 3.5 Story: Frontier AI Goes Free
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Let me give you the numbers first, because they tell a story.

Qwen 3.5’s flagship model uses a Mixture of Experts (MoE) architecture with 397 billion total parameters but only 17 billion active at any given time. Read that again. You get frontier-class performance while only running the compute cost of a 17B model.

The benchmarks are not “competitive.” They are leading:

IFBench (instruction following): 76.5 — beating GPT-5.2’s 75.4
SWE-bench (coding): 76.4 — neck and neck with the best
MMMU (vision): 85.0 — outright leader
256K token context window
201 languages supported
Thinking and non-thinking modes — you choose the tradeoff between depth and speed

The model family was released in three waves between February and March 2026: flagship, medium, and small. The small models — 0.8B to 9B parameters — are explicitly designed for on-device deployment. Your phone. Your laptop. Your edge server. No API call required.

Let that sink in for a moment.

A year ago, running anything close to frontier AI locally was a fantasy. Today, Alibaba is handing you models that compete with the best in the world, under the most permissive open-source license available, optimized to run on your hardware.

The MoE architecture is the key unlock here. Traditional dense models force you to choose: either you run a massive model with massive compute, or you run a small model with limited capability. MoE breaks that tradeoff. Qwen 3.5 has the knowledge of a 397B model but the inference cost of a 17B one. It is, in practical terms, the democratization of frontier AI.

And it is Apache 2.0. Not “open-ish.” Not “you can look but not touch.” Fully open. Fork it. Fine-tune it. Ship it in your product. Alibaba does not care. Or rather — they care very much, but their game is not licensing revenue.

CoPaw: The Agent Layer China Was Missing
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Models without infrastructure are academic papers. Infrastructure without models is empty plumbing. The interesting move is doing both.

CoPaw (copaw.bot) launched in March 2026 from Alibaba’s AgentScope team. If you are familiar with OpenClaw — and if you read my blog, you probably are — CoPaw is China’s answer to it. An open-source personal AI agent workstation that turns language models into persistent, capable digital workers.

The feature list reads like someone studied every agent platform on the market and built a synthesis:

ReMe: persistent memory across sessions. Your agent remembers context, preferences, past interactions. Not a gimmick — this is what separates a chatbot from an actual assistant.
Custom skills: build and import capabilities. Pull from clawhub.ai, skills.sh, or GitHub.
Multi-channel: DingTalk, Feishu, iMessage, Discord, QQ. Your agent lives where you work.
Cron scheduling: automated tasks on a schedule. Check my email every morning. Summarize my feeds at 6 PM. The basics, done right.
Browser automation: your agent can navigate the web, fill forms, extract data. The hands to go with the brain.
MCP Server integration: the emerging standard for tool use. CoPaw speaks it natively.

Is it perfect? No. WhatsApp and Telegram support are missing — a significant gap for Western and Latin American users. Multi-agent orchestration is not there yet. OpenRouter integration is absent. These are real limitations.

But here is what matters: CoPaw is not a prototype. It is a platform. And it is open-source, which means the community can fill those gaps faster than any corporate roadmap.

I have been running OpenClaw as my personal agent infrastructure for months — it is literally what powers JarvisX, my AI assistant. So I understand this space intimately. CoPaw is not a clone. It is a parallel evolution, built from a different set of assumptions (Chinese messaging ecosystem, AgentScope framework, different privacy model) that arrives at remarkably similar conclusions about what an AI agent workstation needs to be.

That convergence is the signal. When two teams on opposite sides of the world, working independently, build essentially the same thing — that is not coincidence. That is the shape of the future becoming obvious.

The Earthquake Started in January 2025
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None of this is happening in a vacuum. Let me connect the dots.

January 2025: DeepSeek releases R1, an open-source reasoning model that shocks the industry. Silicon Valley’s reaction ranges from dismissal to panic, settling on grudging respect. The “China can’t do AI” narrative dies overnight.

Throughout 2025: Chinese labs iterate at a pace that makes Western release cycles look glacial. Qwen, DeepSeek, Yi, GLM — each generation closing the gap further. The MoE architecture becomes the standard approach, driven by the practical reality that Chinese labs face compute constraints from US export controls and have to be more efficient, not less.

Here is the irony that should keep policymakers awake: export controls designed to slow China’s AI development may have accelerated their innovation in efficiency. When you cannot buy the biggest GPUs, you learn to do more with less. And “more with less” turns out to be exactly what the market wants.

February-March 2026: Qwen 3.5 arrives, not as a single model but as an ecosystem play. Flagship for the cloud, medium for the server room, small for the device. And simultaneously, CoPaw launches to provide the agent layer. Models plus infrastructure plus ecosystem.

This is not “China catching up.” This is China executing a full-stack AI strategy while much of the West is still arguing about whether to charge $200/month or $2,000/month for API access.

The Alibaba Strategy: OpenAI’s Vision, Open-Source’s Price
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Let me be explicit about what Alibaba is doing, because I think most Western observers are misreading it.

OpenAI’s vision has always been: build the best models, then build the infrastructure to deploy them, then build the ecosystem of applications on top. Vertical integration. The “Apple of AI.”

Alibaba’s vision is the same — except open-source.

Best models? Qwen 3.5 is demonstrably frontier-competitive. Infrastructure? CoPaw provides the agent layer. AgentScope provides the framework. Ecosystem? Apache 2.0 means anyone can build on it.

The difference is the business model. OpenAI charges for access. Alibaba gives away the technology and monetizes the cloud (Alibaba Cloud), the commerce (Alibaba platforms), and the enterprise services built on top. The AI itself is the loss leader. Or rather, it is the moat around everything else.

This is not charity. It is strategy. And it is devastatingly effective.

If you are an enterprise CTO today — and I have been one, at Banco Sabadell, so I know the calculus — the question on your desk is uncomfortable: Why am I paying for proprietary AI models when open-source alternatives match or beat them on benchmarks?

The answers used to be: reliability, support, safety, compliance. Those are real. But they are eroding fast. Qwen 3.5 is not some garage project. It is backed by one of the largest technology companies on Earth. It has enterprise-grade documentation. It runs in production at Alibaba scale.

The moat is getting shallow.

What This Means for the West
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I am not writing this as a China cheerleader or a Western doomer. I am writing it as someone who has spent 20+ years in enterprise technology and is currently leading digital transformation at one of Latin America’s largest universities.

Here is what I think this means:

For AI companies (OpenAI, Anthropic, Google): The “best model” advantage is now measured in months, not years. If Qwen 3.5 can match GPT-5.2 today, Qwen 4 will likely match whatever comes next. The sustainable advantage must come from ecosystem, trust, and integration — not model quality alone. The race to the bottom on model pricing accelerates.

For enterprises: Your AI strategy cannot depend on a single provider. The multi-model, multi-provider approach is no longer a nice-to-have — it is risk management. And if you are not evaluating open-source models for your use cases, you are leaving money and optionality on the table.

For the open-source movement: This is your moment. China’s largest tech companies are pouring billions into open-source AI, not because they are altruistic, but because it serves their strategic interests. The result is the same: the commons gets richer. Western open-source advocates should take notes on how to align corporate strategy with community benefit.

For developers: Learn to run local models. Understand MoE architectures. Get comfortable with agent frameworks — both OpenClaw and CoPaw. The developers who thrive in 2027 will be the ones who can deploy and orchestrate AI agents across multiple models and platforms, not the ones locked into a single API.

For policymakers: The export control strategy needs a fundamental rethink. Restricting compute has not prevented frontier AI development in China — it has redirected it toward efficiency innovations that may ultimately be more valuable than brute-force scaling. The horse has left the barn, and the barn is on fire.

The Democratization Paradox
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Here is the question that keeps me up at night: if frontier AI is free and open, what is the moat?

Not for Alibaba — their moat is their ecosystem. Not for OpenAI — their moat is their brand and enterprise relationships. I mean for everyone else. For the thousands of SaaS companies, AI startups, and technology consultancies that have built their value proposition around access to AI capabilities.

When Qwen 3.5 is free, when CoPaw is free, when the entire stack from model to agent to deployment is open-source and commercially licensable — what exactly are you selling?

The answer, I think, is the same answer it has always been in technology: domain expertise, integration quality, trust, and speed of execution. The tools become commoditized. The craft does not.

But that is a much harder business than “we have access to AI and you don’t.” And it will cause a shakeout that makes the SaaSpocalypse look like a rehearsal.

What I Am Doing About It
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I never write about things I am not willing to act on. So here is what this means for my work:

At Tec de Monterrey, we are actively evaluating open-source models for educational applications where data sovereignty matters — and with a Latin American university serving students across multiple countries, it matters a lot. Qwen 3.5’s multilingual support (201 languages, with strong Spanish coverage) makes it a serious candidate.

Personally, I run my AI agent infrastructure on OpenClaw. CoPaw’s release is not a threat to that — it is validation. The agent workstation pattern is the right abstraction. And competition drives improvement. I fully expect OpenClaw and CoPaw to cross-pollinate features, especially given that CoPaw can already import skills from clawhub.ai.

The future I see is heterogeneous. Not “Western AI vs. Chinese AI” but a global ecosystem where the best models and tools win regardless of origin. Where an enterprise in Mexico City runs Qwen for some tasks, Claude for others, and Gemini for a third — all orchestrated by agent infrastructure that does not care about the nationality of the model.

That is not a geopolitical statement. It is an engineering reality.

The Bottom Line
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Alibaba has executed a textbook pincer move: world-class models on one side, agent infrastructure on the other. Qwen 3.5 gives you the brain. CoPaw gives you the body. Both are free. Both are open. Both are production-ready.

The West still leads in many dimensions — safety research, alignment, enterprise trust, regulatory frameworks. Those matter. But the raw capability gap? It is closing so fast that by the time you finish reading this article, it may have closed a little more.

If you are a technology leader and you are not paying attention to what is coming out of China, you are not paying attention.

And in this industry, not paying attention is how you become the next $300 billion cautionary tale.

My AI Agent Is 2 Weeks Old. 72,000 Lines of Code. 5 Projects Shipped.

Wed, 25 Feb 2026 00:00:00 +0000

I haven’t written a line of code in weeks. And yet, my repositories keep growing.

72,563 lines added. 43 commits. 5 projects deployed to production.

All in 14 days. I didn’t write any of it. My AI agent did.

A Clarification First
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Let me be clear about something: my job as VP of Digital Transformation at Tecnológico de Monterrey is not to write code. I lead teams that do that at scale, for one of Latin America’s largest universities.

But I’ve been writing code for 40 years. From assembler to C, Fortran, Visual Basic, Pascal, COBOL, C++, Java, JavaScript, TypeScript, Rust, Python — I’ve touched them all. First because it was my job. Then because it became a habit I never wanted to break.

Why? Because staying close to the code keeps me close to reality. It helps me make better decisions. It lets me have real conversations with my technical teams — not as a manager who reads reports, but as someone who understands what they’re building.

The projects I’m about to describe are personal. Some are proofs of concept that later evolve into institutional initiatives — like TECgpt Desktop, which started as an experiment on my laptop before becoming an official tool at the university. Others are simply things I want to build.

This matters for what comes next.

Meet My Digital Apprentice
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His name is JarvisX. He runs on a Mac Mini in my home office. He’s been “alive” for exactly two weeks.

But here’s the key: I decide the architecture. I choose the tech stack. I define the structure, the patterns, the conventions. JarvisX executes — brilliantly, tirelessly, and autonomously — but the technical vision is mine.

He’s not replacing my judgment. He’s amplifying it.

When I say “build this feature,” I’ve already decided how it should be built. JarvisX handles the implementation: writing the code, deploying to production, opening a browser to verify it looks right, and committing the changes. All without asking me to approve each step.

The Numbers
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In two weeks, JarvisX has:

Added 72,563 lines of code across multiple repositories
Made 43 commits
Shipped 5 complete projects to production
Configured deployments, authentication systems, and cloud infrastructure
Validated his own work visually — taking screenshots, iterating until it’s right

Let me be honest: not all 72,000 lines are hand-crafted code. Some are frameworks, themes, and data files. But every commit, every deploy, every design decision was autonomous.

How This Article Got Here
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Want to see what working with an AI agent actually looks like? Here’s the conversation that published this very article:

Me: JarvisX, I left a new article for my blog in our shared Dropbox folder.

JarvisX: Read it! I see it’s in English. Do you want me to generate the Spanish version?

Me: Yes. I trust your translations by now, so no need for me to review. Upload both versions directly.

JarvisX: Thanks for the trust! Uploading now. Should I add a featured image?

Me: Yes. Something that evokes an orchestra conductor surrounded by AI agents.

JarvisX: Got it. Do you want to review it first?

Me: No. Just upload it.

That’s it. No back-and-forth. No micromanagement. Just trust built over two weeks of working together.

By the time you’re reading this, JarvisX has already translated it, generated the image, and deployed both versions to production. I didn’t check his work. I didn’t need to.

The Real Insight
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A year ago, I wrote an article called The End of the Developer. My thesis: the junior developer role would disappear. The future would belong to “directors of orchestra” who conduct AI agents rather than write code themselves.

I’m now living that prediction.

But here’s what I’ve learned: You can’t direct what you don’t understand.

The “new developer” — the one who orchestrates AI agents — still needs deep technical knowledge. Not to write every line, but to:

Define architecture that makes sense
Recognize good code when the agent produces it
Catch mistakes before they reach production
Make tradeoffs that require experience to evaluate

My 40 years of coding didn’t become obsolete when I started working with JarvisX. They became essential. I can direct him effectively precisely because I know what good looks like.

What This Means
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If you’re a senior developer or architect, this is your moment. Your experience is more valuable than ever — not for typing code, but for directing those who do.

If you’re a junior developer, the path forward isn’t to compete with AI at writing code. It’s to accelerate your learning so you can direct AI sooner. Use these tools to learn faster, not to avoid learning.

If you’re a CIO or CTO, stop asking “how do we adopt AI agents?” Start asking “do our people have the technical depth to direct them well?”

The Bottom Line
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72,000 lines. 5 projects. 2 weeks. Zero lines written by me.

But every architectural decision? Mine. Every technology choice? Mine. Every quality standard? Mine.

The future of software development isn’t about writing less code. It’s about directing more of it — with the wisdom that only experience can provide.

Carles Abarca is VP of Digital Transformation at Tecnológico de Monterrey and former CTO of Banco Sabadell. He has been writing code for 40 years and plans to never stop — even if he’s no longer the one typing it.

The CIO's Guide to AI Agents: Before You Buy, Ask These 5 Questions

Mon, 23 Feb 2026 00:00:00 +0000

The AI agent market will reach $236 billion by 2034. Every vendor wants your budget. Here’s how to separate hype from value.

Every enterprise software vendor now sells “AI agents.” Salesforce has Agentforce. ServiceNow acquired Moveworks for $2.85 billion. Microsoft promises an “agentic enterprise” through Copilot. Startups like Sierra have hit $100 million ARR in under two years.

The pressure to act is real. 89% of CIOs consider agent-based AI a strategic priority. 51% of large enterprises have already deployed agentic AI. Your board is asking questions. Your competitors are moving.

But here’s what nobody tells you: most AI agent purchases fail to deliver expected value. Not because the technology doesn’t work — but because organizations buy the wrong solution for their specific situation.

After two decades leading technology transformation at a major European bank and now driving digital innovation at one of Latin America’s largest universities, I’ve developed a framework for evaluating AI agents that cuts through vendor hype. Before you sign that contract, ask these five questions.

The AI Agent Landscape in 2026
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First, let’s understand what you’re buying. The market has three distinct categories:

1. Embedded Agents from SaaS Vendors

Your existing vendors are adding agents to their platforms:

Salesforce Agentforce: $0.10 per action or $125-550/user/month
ServiceNow AI Agents: Full orchestration with AI Control Tower
Microsoft Copilot Studio: Included with M365, plus add-ons
Zendesk AI Agents: $1.50 per autonomous resolution

The pitch: “You already use our platform. Now it’s smarter.”

2. Pure-Play Agent Startups

Companies building agents from the ground up:

Sierra (Bret Taylor, ex-Salesforce): $10B valuation, focused on customer service
Adept: Targeting workflow automation
Imbue, Reflection AI: Research-driven approaches

The pitch: “We’re not constrained by legacy architecture.”

3. Foundation Model Providers

The companies building the AI itself:

Anthropic: Claude with Computer Use and MCP (Model Context Protocol)
OpenAI: GPT-4 with Operator
Google: Gemini with Agentspace

The pitch: “Build custom agents on our infrastructure.”

Each category has trade-offs. Your job is to understand which trade-offs matter for your organization.

Question 1: What Problem Are You Actually Solving?
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This sounds obvious. It isn’t.

“We need AI agents” is not a problem statement. Neither is “we need to reduce costs” or “we need to be more innovative.”

A proper problem statement looks like this:

“Our customer service team handles 50,000 tickets per month. 60% are password resets and order status checks. Average handling time is 8 minutes. We need to reduce that to under 2 minutes for routine inquiries.”
“Our compliance team manually reviews 10,000 transactions daily for AML screening. False positive rate is 95%. We need to reduce false positives while maintaining regulatory coverage.”

Notice the difference? Specific process. Measurable baseline. Clear target.

The trap: Vendors will happily sell you a “general-purpose agent platform” that can theoretically do anything. In practice, these platforms do nothing well. Start with one high-value, well-defined use case. Prove it works. Then expand.

Red flag: If you can’t articulate the specific process, current metrics, and target improvement, you’re not ready to buy.

Question 2: Build, Buy, or Extend?
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You have three paths. Let me illustrate each with real scenarios.

EXTEND: Add Agent Capabilities to Your Existing Platforms
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What it means: You already use Salesforce, ServiceNow, or Microsoft 365. You activate their built-in agent features.

Real example — Global Retailer with Salesforce: A retail company with 200 customer service reps was already running Service Cloud. They activated Agentforce Service Agent in three weeks. Configuration, not development. The agent now handles 40% of incoming inquiries (order status, return policies, store hours) without human intervention. Cost: $0.10 per agent action. No new vendor relationship. No integration project.

Real example — Insurance Company with ServiceNow: An insurer using ServiceNow ITSM enabled AI Agents for incident categorization and routing. The agent reads incoming tickets, identifies the affected system, assigns priority, and routes to the right team. Implementation: 6 weeks. Result: 60% reduction in misrouted tickets, 25% faster resolution times.

When to Extend:

You’re already paying for the platform
Your use case is common (customer service, IT helpdesk, HR inquiries)
You need results in weeks, not months
You don’t have AI engineering talent

The limitation: You’re constrained by what your vendor offers. If Salesforce Agentforce doesn’t support your specific workflow, you’re stuck waiting for their roadmap.

BUY: Purchase a Specialized Agent Solution
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What it means: You bring in a startup or specialized vendor that does one thing exceptionally well.

Real example — E-commerce Brand with Sierra: A direct-to-consumer brand with $500M revenue wasn’t satisfied with their existing chatbot. They deployed Sierra for customer service. Sierra’s agents handle complex conversations: processing returns while suggesting alternatives, managing subscription modifications, resolving billing disputes. The agents access their Shopify backend, payment processor, and shipping systems. Implementation: 4 months. Result: 70% of conversations resolved without human escalation. Customer satisfaction increased 15 points.

Real example — Enterprise IT with Moveworks (now ServiceNow): Before the acquisition, companies bought Moveworks specifically for IT helpdesk automation. The agent could reset passwords, provision software, troubleshoot VPN issues, and answer policy questions — all through natural conversation in Slack or Teams. It understood context: “I can’t access the server” triggered different workflows than “I need Photoshop installed.”

When to Buy:

Your existing vendor’s agents aren’t good enough for a strategic use case
A startup has proven traction in your specific domain
You can afford 3-6 months implementation
The use case is important enough to justify a new vendor relationship

The risk: Startup viability. What happens if Sierra gets acquired? If the startup pivots? You’re dependent on a company that may not exist in five years. Mitigate this by ensuring data portability and avoiding deep customizations that lock you in.

BUILD: Create Custom Agents Using Foundation Models
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What it means: You use Claude, GPT-4, or Gemini APIs to build agents tailored to your unique processes.

Real example — Investment Bank’s Deal Analysis Agent: A bulge-bracket bank built a custom agent for M&A analysts. The agent ingests SEC filings, earnings transcripts, news articles, and internal research. Analysts ask natural language questions: “What are the key risks in Acme Corp’s debt structure?” or “Compare the margin profile of these three acquisition targets.” The agent synthesizes information that would take an analyst hours to compile manually. Built on Claude with custom RAG (retrieval-augmented generation) over proprietary databases. Development: 8 months. Team: 6 engineers, 2 ML specialists. The output is proprietary competitive advantage — no vendor offers this.

Real example — Pharmaceutical Company’s Clinical Trial Agent: A pharma company built an agent to monitor clinical trial data in real-time. The agent identifies adverse event patterns, flags protocol deviations, and generates regulatory-ready reports. This isn’t a use case any vendor serves — the domain knowledge is too specialized, the regulatory requirements too specific. Built on GPT-4 with extensive fine-tuning and custom safety guardrails. Development: 12 months.

Real example — University’s Custom Academic Ecosystem (TecGPT): At Tecnológico de Monterrey, we built TecGPT — our academic AI ecosystem, integrated with our LMS, assisting both professors and students, and aligned with our academic regulations. No vendor could offer what we needed out-of-the-box.

When to Build:

Your process is genuinely unique and defines competitive advantage
No vendor solution exists for your domain
You have AI/ML engineering talent (or can acquire it)
You can invest 6-12 months before seeing production value
The long-term value justifies the ongoing maintenance cost

The reality check: Building is expensive. A custom agent isn’t a one-time project — it’s an ongoing product. You need engineers to maintain it, improve it, and adapt it as foundation models evolve. Budget for 2-3 FTEs indefinitely, not just the initial build.

The Decision Matrix
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Factor	Extend	Buy	Build
Time to value	Weeks	3-6 months	6-12 months
Customization	Low	Medium	Unlimited
Vendor risk	Low	High	Medium
Talent required	Admins	Integrators	Engineers + ML
Total cost (3 years)	$$	$$$	$-$$$$
Competitive advantage	None	Low	High

My Recommendation
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Start with Extend for your first agent deployment. Even if it’s not perfect, you’ll learn what works, what users actually need, and where the gaps are. That learning is invaluable before you commit to Buy or Build.

Move to Buy when you’ve proven agent value and need capabilities your platform vendor doesn’t offer. Choose startups with strong traction, clear use case focus, and enterprise customers who can serve as references.

Build only when you’ve exhausted Extend and Buy options, or when the process is so unique that it genuinely differentiates your business. If you’re building agents for commodity processes (customer service, IT helpdesk), you’re wasting engineering talent that could create actual competitive advantage.

Question 3: How Will You Measure Success?
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Before deployment, define:

Efficiency metrics:

Time saved per task
Tasks handled without human intervention
Error rate reduction

Quality metrics:

Customer satisfaction (for customer-facing agents)
Compliance accuracy (for regulatory processes)
Decision quality (measured against human expert baseline)

Business metrics:

Cost per transaction
Revenue impact (if applicable)
Employee satisfaction (agents should help workers, not threaten them)

The 90-day rule: If you can’t demonstrate measurable improvement within 90 days, something is wrong. Either the use case was poorly chosen, the implementation was flawed, or the vendor oversold capabilities. Don’t extend pilots indefinitely hoping for results.

Hidden metric: Adoption. The most sophisticated agent is worthless if your team doesn’t use it. Track actual usage, not just availability.

Question 4: What’s Your Human-in-the-Loop Strategy?
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In 2026, no responsible organization deploys fully autonomous agents for consequential decisions. The question is where to place human oversight.

The spectrum:

Human-initiated: Human starts task, agent assists, human approves result
Agent-initiated with approval: Agent identifies opportunity, proposes action, human approves
Agent-executed with audit: Agent acts autonomously, human reviews after the fact
Fully autonomous: Agent acts without oversight (appropriate only for low-risk, reversible actions)

My framework for choosing:

Risk Level	Reversibility	Recommended Approach
Low	Easy to reverse	Fully autonomous
Low	Hard to reverse	Agent-executed with audit
High	Easy to reverse	Agent-initiated with approval
High	Hard to reverse	Human-initiated only

Examples:

Password reset → Fully autonomous (low risk, reversible)
Customer refund under $50 → Agent-executed with audit
Credit decision → Agent-initiated with approval
Regulatory filing → Human-initiated only

The regulatory reality: Financial services, healthcare, and other regulated industries will require human oversight for most consequential decisions for the foreseeable future. Plan for this. Agents that “recommend and explain” are more valuable than agents that “decide and act” in these contexts.

Question 5: What’s Your Data Strategy?
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AI agents are only as good as the data they can access. Before buying, audit:

Data availability:

Can the agent access all systems needed for the use case?
Are APIs available, or will you need custom integrations?
What’s the latency? Agents that wait 30 seconds for data lookups frustrate users.

Data quality:

Is your data accurate and up-to-date?
Are there known data quality issues that will cause agent errors?
Who’s responsible for data hygiene?

Data governance:

What data can the agent access? What’s off-limits?
How do you prevent agents from exposing sensitive information?
What audit trail exists for agent data access?

The integration tax: Most enterprises underestimate integration effort by 3-5x. If the vendor says “we integrate with everything,” ask for customer references running your specific system combination. Generic claims mean nothing.

MCP and the future: Anthropic’s Model Context Protocol (MCP) is emerging as a standard for agent-to-system communication. Consider whether your chosen platform supports open standards or locks you into proprietary integrations.

The Decision Framework
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After answering these five questions, you should be able to complete this statement:

“We will deploy [specific agent type] to solve [specific problem] in [specific process]. We expect to achieve [specific metric improvement] within [timeframe]. Our human oversight model is [approach]. We have confirmed data access to [systems] and defined governance policies for [sensitive data types].”

If you can’t complete this statement with confidence, you’re not ready to buy.

My Recommendations for 2026
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For most enterprises: Start with your existing vendor’s agent capabilities. Salesforce Agentforce, ServiceNow AI Agents, or Microsoft Copilot Studio will handle 80% of common use cases. The integration is already done. The governance frameworks exist. The risk is manageable.

For customer service: Sierra has proven traction. If customer experience is strategic and your existing vendor’s agents aren’t cutting it, evaluate Sierra seriously. Their $100M ARR in 21 months signals real value delivery.

For unique processes: Build on Anthropic Claude or OpenAI, but only if you have the engineering talent to maintain custom solutions. The foundation models are extraordinary. The engineering required to productionize them is not trivial.

For everyone: Start small. One use case. 90-day proof of value. Then expand. The vendors want you to buy platforms. You should buy solutions to specific problems.

The Bottom Line
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The AI agent market is real. The value is real. But so is the hype.

62% of companies investing in agentic AI expect more than 100% ROI. Some will achieve it. Many won’t — not because the technology failed, but because they bought the wrong solution for the wrong problem with the wrong implementation approach.

Don’t be a statistic. Ask the five questions. Complete the decision framework. Then — and only then — sign the contract.

The agents are ready. Make sure you are too.

Banking Will Never Be the Same: An Insider's View on AI Agents

Sun, 22 Feb 2026 00:00:00 +0000

I led digital transformation at a €200B European bank. AI agents will change everything I thought I knew.

Between 2001 and 2023, I spent over two decades at Banco Sabadell, one of Spain’s largest financial institutions, rising from technology roles to CIO and ultimately CTO. I oversaw the technology integration of TSB in the UK, navigated one of the most public banking IT crises in recent memory, and led a multi-year digital transformation that touched every corner of a 25,000-employee organization.

Back then, we believed APIs would revolutionize banking. We were thinking too small.

The AI agent revolution will make our API transformation look like a warm-up exercise.

Why Banking Is Perfect for AI Agents
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Banks are unique. They operate in an environment that seems hostile to automation — heavy regulation, risk aversion, legacy systems — but is actually ideal for AI agents.

Repetitive processes with clear rules. KYC verification. Transaction monitoring. Dispute resolution. Compliance reporting. These processes follow explicit rules that humans apply thousands of times per day. Rules that can be encoded, executed, and audited by agents.

Structured data everywhere. Unlike creative industries, banking runs on structured data: transactions, contracts, balances, rates. This is exactly what agents excel at processing.

Regulation as an advantage. This sounds counterintuitive. But consider: agents can follow rules more consistently than humans. They don’t get tired. They don’t take shortcuts. They don’t forget steps. In a world where compliance failures cost billions, agents that provably follow every regulation become assets, not risks.

Economics demand it. European banks operate on razor-thin margins. Cost-to-income ratios above 60% are common. The math is brutal: reduce operational costs or die. Agents offer a path that doesn’t require another round of layoffs — they offer a path to do more with the same people.

The Agent Transformation Map
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Here is how I see AI agents transforming core banking operations:

Function	Today	With Agents
KYC/Onboarding	3-5 days, multiple handoffs	15 minutes, agent + human final approval
Fraud Detection	Alerts generate queues → humans investigate	Agent investigates, escalates only genuine concerns
Customer Service	Limited chatbots, frustrated transfers	Agent with full context, handles 80% end-to-end
Compliance Monitoring	Armies of reviewers sampling transactions	Continuous agent monitoring, humans handle exceptions
Credit Decisions	Score + human judgment + committee	Agent analysis + recommendation + human approval
Regulatory Reporting	Manual data gathering, reconciliation hell	Agent assembles, validates, humans verify and submit

The pattern is consistent: agents handle the volume, humans handle the judgment. The ratio shifts from 90% human work / 10% oversight to 10% human work / 90% oversight.

The Real Challenges (From the Inside)
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I would be lying if I said this transformation will be easy. Having lived inside a large bank, I know the obstacles are formidable.

Legacy systems are not going anywhere. COBOL is still running critical systems at most major banks. Mainframes process millions of transactions daily. These systems work. They are paid off. No CEO will approve ripping them out for an AI experiment. The winning strategy is not replacement — it is an agent layer that interfaces with legacy systems through existing APIs and screen scraping where necessary.

Regulators are conservative — but evolving. Banking regulators move slowly by design. They remember 2008. But they are not blind to AI’s potential. The key is engaging them early, demonstrating auditability, and framing agents as risk reduction tools, not risk introduction.

Culture resists change. Banks are risk-averse institutions filled with risk-averse people. “We’ve always done it this way” is not a cliché — it is a deeply held belief. Change management matters more than technology selection.

The talent gap is real. Bankers do not understand AI. AI engineers do not understand banking. Finding people who speak both languages is nearly impossible. Building that bridge — through training, hiring, and partnerships — is essential.

Data silos persist. The customer data you need for intelligent agents is scattered across dozens of systems that do not talk to each other. Data unification projects have failed for decades. Agents will not magically solve this — but they can work around it better than traditional integration approaches.

What I Would Do Differently Today
#

If I were back in that CTO chair today, knowing what I know about AI agents, here is what I would change:

Stop buying “AI-powered” SaaS. Every vendor now claims AI capabilities. Most are wrappers around the same foundation models with limited customization. Instead: build an agent layer on top of your existing systems. Own the intelligence, rent the infrastructure.

Start with back-office, not customer-facing. Regulators scrutinize customer-facing AI. Back-office operations have more freedom. Prove value internally — reconciliation, reporting, internal fraud investigation — then expand to customer touchpoints with evidence and confidence.

Adopt a hybrid approval model. For the next 3-5 years, the winning pattern is: agent proposes, human approves. This satisfies regulators, builds trust, and creates training data for future full automation. Do not try to remove humans too fast.

Invest in observability. Agents must be auditable. Every decision, every data access, every recommendation must be logged and explainable. This is not optional — it is the foundation of regulatory acceptance.

Predictions for 2027-2030
#

Based on what I am seeing in the market and my experience inside banking:

50% reduction in back-office headcount. Not through layoffs, but through attrition and redeployment. The work will not disappear — it will transform. Banks will need fewer processors and more agent supervisors.

“Agent-first” banks will emerge. New entrants — likely from Asia and Latin America — will build banks designed around agents from day one. They will operate with 1/10th the staff of traditional banks. This is the real competitive threat.

Regulators will create AI agent certification. Just as we have software audits today, we will have agent audits. Banks that achieve certification early will have a competitive advantage.

M&A will accelerate. The investment required to build agent capabilities favors scale. Smaller banks that cannot afford the transformation will merge or be acquired. The industry will consolidate further.

The “AI banker” role will emerge. A new job category: professionals who understand both banking operations and AI agent orchestration. They will be the most sought-after talent in financial services.

The Choice Ahead
#

Every banking executive faces the same question today: invest heavily in AI agents now, or wait and see.

Waiting feels safe. It is not.

The banks that move first will reduce costs, improve compliance, and — crucially — attract the talent needed to keep improving. The banks that wait will find themselves competing against institutions that operate at half their cost structure.

I have seen banking transform before — from branches to ATMs, from paper to digital, from monoliths to APIs. Each wave rewarded the early movers and punished the laggards.

This wave will be faster and more decisive.

The question is not whether your bank will use AI agents. The question is whether you will be the one deploying them — or the one being replaced by a bank that did.

Carles Abarca is VP of Digital Transformation at Tecnológico de Monterrey. He spent 22 years at Banco Sabadell, including roles as CIO and CTO, leading the bank’s digital transformation and the technology integration of TSB. He writes about AI, digital transformation, and the future of enterprise technology at carlesabarca.com.

The Agent Economy: From Selling Software to Selling Artificial Employees

Wed, 18 Feb 2026 00:00:00 +0000

The SaaSpocalypse of February was not an accident. It was a warning. The market saw what many CIOs still do not want to see: the business logic that justifies billions in licensing fees fits in a text file that an AI agent can execute.

But the interesting question is not what gets destroyed. It is what emerges.

From SaaS to SaS
#

The industry consensus already has a name for this transition: SaS — Services-as-Software. The term, popularized by Foundation Capital in their thesis on the $4.6 trillion agentic AI opportunity, inverts the formula that has dominated enterprise software for two decades. SaaS sold software as a service. SaS sells services as software — autonomous agents that deliver outcomes, not interfaces.

It is the difference between selling a scalpel and selling the surgery.

Others call it WaaS — Workers-as-a-Service. Or simply what it is: the Agent Economy. An economy where companies do not buy software licenses. They hire artificial employees. Employees that do not negotiate salaries, do not request time off, and scale without limit.

The reconversion map
#

If the thesis is correct, the question is not whether the major SaaS players will reconvert, but when and how. Here are my concrete predictions:

SAP will stop selling FI/CO licenses for accounting. Instead, it will offer an accounting agent that, based on business events — an invoice issued, a payment received, a period close — autonomously maintains the books in SAP. Humans will stop interacting with SAP’s UI. The agent will interact with SAP’s API. The financial controller will shift from operating the system to supervising the agent.

Salesforce will stop selling CRM per user. It will offer a sales agent that qualifies leads, updates opportunities, schedules follow-ups, and generates forecasts. Salespeople will stop filling in Salesforce fields. The agent will extract information from emails, calls, and meetings, keeping the pipeline updated. The sales manager’s role will shift from chasing their team to update the CRM to reviewing the agent’s decisions.

ServiceNow will stop selling support tickets per seat. It will offer an operations agent that diagnoses incidents, executes runbooks, escalates when necessary, and closes tickets without human intervention. Eighty percent of L1 and L2 support will be invisible to humans.

Workday will stop selling HR modules per employee. It will offer a people management agent that processes payroll, manages absences, generates compliance reports, and executes full onboarding — from provisioning access to scheduling the first week’s agenda.

HubSpot will stop selling a marketing suite. It will offer a growth agent that generates content, optimizes campaigns, segments audiences, and adjusts budgets in real time based on conversions. The CMO will not operate tools. They will conduct an orchestra of agents.

Atlassian will stop selling Jira per developer. It will offer a delivery agent that breaks epics into tasks, assigns work based on capacity, detects blockers, and generates progress reports. The engineering manager will shift from managing a board to managing a strategy.

The pattern
#

In every case, the pattern is the same: the human stops being the software operator and becomes the agent’s supervisor. The value is not in the interface. It is in the intelligence.

And the business model transforms with it. You no longer charge per seat — because there are no seats. You charge per outcome. Per invoice processed. Per lead qualified. Per incident resolved. Per payroll executed.

The companies that understand this first will capture the market. Those that keep selling interfaces with a copilot bolted on top will discover that a markdown file and an agent with access to their APIs do the same job at a fraction of the cost.

The Agent Economy is not coming. It is already here. The question is whether you are hiring agents or still buying licenses.

The SaaSpocalypse: $300 Billion Evaporated in 48 Hours

Tue, 17 Feb 2026 00:00:00 +0000

$300 billion evaporated in 48 hours. Welcome to the SaaSpocalypse.

In October 2024, I wrote about the next wave of AI: agents. In May 2025, I wrote about the end of the developer as we know them. Both articles shared a thesis: AI is not coming to assist. It is coming to execute.

The first week of February proved us right in the most brutal way possible.

Anthropic launched a set of plugins for its Cowork tool — essentially markdown files that encode legal, financial, and sales expertise. Nothing revolutionary on the surface. But the market understood something that many CIOs still have not processed: if Thomson Reuters’ business logic fits in a text file that an AI agent can read and execute, their $200/user/month software has an existential problem.

Thomson Reuters lost 57% from its highs. ServiceNow 48%. Salesforce 43%. The S&P software index had its worst month since October 2008.

But the interesting part is not the decline. It is the thesis behind it.

Satya Nadella said it in December 2024: “SaaS applications are CRUD databases with business logic on top. Agents will absorb that logic.” Foundation Capital put a number on it: $4.6 trillion in opportunity. They called it “Service as Software” — the inversion of the SaaS model. You no longer sell tools for humans to solve problems. You sell outcomes. The agent does the work.

A Palantir client summed it up better than any analyst: “All third-party software must justify its existence. And so far, it has not been able to.”

This does not mean Salesforce or SAP will disappear tomorrow. Switching costs are real. Deep integrations provide protection. But the per-seat pricing model has its days numbered. IDC predicts it will be obsolete by 2028. And companies that do not transition to outcome-based models will discover their moat was narrower than they thought.

I have spent over 20 years leading technology transformation in banking and education. I have seen panic cycles before — cloud, mobile, blockchain. But this time is different. It is not a new layer being added to the stack. It is a layer that absorbs the others.

The question for every CIO today is not “how do I integrate AI into my software.” It is “how much of my software can an agent replace with a markdown file and access to my APIs.”

If the answer makes you uncomfortable, you are probably already late.

The End of the Developer: The Future of Software Development with AI Agents

Tue, 20 May 2025 00:00:00 +0000

The End of the Junior Developer: The Future of Software Development with AI Agents

In a world where artificial intelligence advances at breakneck speed, an uncomfortable truth looms on the horizon of the technology industry: the junior developer role may be on the path to extinction. This is not science fiction but an emerging reality that is already transforming how we build software.

The Silent Revolution of AI Agents
#

Today, AI is no longer just an assistant that completes code. AI agents are evolving into autonomous entities capable of perceiving the development environment, making complex decisions, and executing complete programming tasks with minimal human oversight. We are no longer talking about simple tools, but digital collaborators that are reconfiguring the entire development chain.

From Assistant to Autonomous Agent
#

AI-based code assistants like GitHub Copilot or Codeium have already transformed developer productivity. However, what is coming is far more disruptive: specialized agents working in concert to manage the entire development lifecycle.

What does this mean? While today a junior developer can still ask an AI to generate boilerplate code or explain complex systems, tomorrow a technical architect will be able to instruct a complete team of agents to:

Develop complex code based on high-level requirements
Perform exhaustive testing and bug resolution
Optimize performance without manual intervention
Manage deployments and update documentation

The Prediction That Is Already Happening
#

Mark Zuckerberg stated it without ambiguity: “By 2025, AI will be capable of functioning as a mid-level engineer, writing code and potentially replacing software developers.” We are not talking about a distant future, but a reality that is already emerging.

According to Gartner, by 2027 generative AI will require 80% of the engineering workforce to upskill, creating new roles and eliminating others. The question is no longer whether it will happen, but when it will reach the tipping point that transforms the entire ecosystem.

Orchestration: The New Paradigm
#

The key concept here is AI agent orchestration: a process by which multiple specialized agents work together within a unified system. Each agent focuses on a specific task — UI design, backend development, testing, security — while a central entity (human or AI) conducts the symphony.

The Architect as Orchestra Conductor
#

In this new paradigm, the technical architect becomes the true protagonist. Their role evolves from solution designer to strategic director of an AI agent team, defining:

The system vision and requirements
Technical and business constraints
Architecture and quality standards
Resolution of complex problems that require human judgment

This transformation is already happening. According to ServiceNow/Pearson research, by 2027 18.7% of technical architect tasks will be at least partially augmented by AI. Architects will focus less on guiding code implementation and more on directing and supervising the autonomous work of agents.

The Experience Crisis
#

Here the fundamental dilemma arises: if AI agents can handle the tasks traditionally assigned to junior developers, how will new professionals acquire experience?

One of the greatest concerns is precisely how junior developers can grow into mid-level and senior roles if AI handles most of the routine coding. Traditionally, developers have learned by doing — writing, debugging, and refactoring real-world code. Without that hands-on experience, there is a risk that developers will not fully understand the complexities of software development.

A Future Without Traditional Juniors
#

My thesis is that the developer role, as we know it, will disappear. In its place, we will see:

Prompt and orchestration engineers: Professionals specialized in directing and extracting maximum value from AI agents.
Verification and review specialists: Experts in evaluating AI-generated code, identifying edge cases, and testing its reliability.
High-level system designers: Professionals focused on architecture and system design, where higher-level thinking remains primarily human.

Companies are already hiring fewer junior engineers due to AI-driven productivity improvements. This trend will only accelerate as AI agents mature.

Adapt or Fall Behind
#

For current professionals, the message is clear: the developer career is evolving, not disappearing. The future belongs not to those who resist AI nor to those who depend on it exclusively, but to those who learn to work symbiotically with these tools.

The most successful developers will be those who:

Master prompt engineering to guide AI agents
Develop sharp evaluation and verification skills
Focus on areas where human creativity and systems thinking are irreplaceable
Deeply understand orchestration and collaboration between multiple AI agents

Are We Ready for This Change?
#

If my thesis is correct, we face a radical transformation in how we educate future developers and structure technical teams. Universities, bootcamps, and companies will need to completely rethink their training and hiring programs.

The question is not whether AI agents will revolutionize software development — they already are — but how quickly we will adapt as an industry to a world where humans design and direct, while AI agents build and implement.

Year Three of AI: The Age of Agentic AI

Wed, 11 Dec 2024 00:00:00 +0000

And now, on to the challenges of 2025 – Year Three of the new AI era: the year of Agentic AI.

Year I - 2023: the emergence of ChatGPT, the year of prompting where AI was text-based conversation. We launched our TecGPT.

Year II - 2024: AI becomes multimodal and learns to generate image, sound, music, voice, and video content. We launched our SkillStudio and incorporated multimodality into our platform.

Year III - 2025: AI enables the creation of autonomous agents that take actions on our behalf – the year of Agentic AI. We will launch AgentStudio with the essential contribution of Manuel Teran and his incredible team.

The Next AI Wave: Agents

Tue, 15 Oct 2024 00:00:00 +0000

The AI conversation has been dominated by chatbots and copilots – tools that assist humans in doing their work faster. That era is ending. The next wave is agents, and the distinction matters more than most people realize.

From assistants to autonomous actors
#

A chatbot responds to prompts. An agent pursues objectives.

The difference is not incremental. It is architectural. An AI agent is an autonomous entity that receives a goal, decomposes it into subtasks, plans an execution strategy, uses tools and APIs to act on the world, observes results, adjusts its approach, and iterates until the objective is met. No human in the loop for each step. No prompt-response-prompt cycle.

Think of it this way: a copilot helps you write an email. An agent handles your entire inbox – triaging, responding, escalating, scheduling follow-ups – while you focus on the decisions that actually require your judgment.

Why now
#

Three converging forces make agents viable today in ways they were not two years ago:

Reasoning capability. Large language models have crossed a threshold in their ability to decompose complex problems, maintain context across long chains of action, and recover from errors. This is not about generating better text. It is about planning and execution.

Tool use. Modern LLMs can reliably call APIs, query databases, browse the web, execute code, and interact with external systems. The agent is not trapped in a text box. It operates in the real digital environment.

Cost economics. Inference costs have dropped by orders of magnitude. Running an agent that makes dozens of API calls to complete a complex task is now economically viable at enterprise scale.

What this means for enterprises
#

The implications for enterprise technology are profound:

Workflow automation moves from rule-based to goal-based. Instead of encoding every step in a process, you define the outcome. The agent figures out the path. This makes automation accessible to business users, not just developers.

The value of software shifts. If an agent can navigate a UI, call APIs, and execute business logic, the value of the software layer between the user and the data is fundamentally questioned. Middleware, workflow tools, and integration platforms face existential pressure.

New security surfaces emerge. Autonomous agents with API access introduce attack vectors that traditional security models were not designed for. Identity, authorization, and audit trails need to be rethought for non-human actors that make decisions.

Organizational structure adapts. When agents handle execution, the human role shifts to oversight, strategy, and exception handling. This is not about eliminating jobs – it is about redefining what humans do in knowledge work.

The road ahead
#

We are in the early innings. Current agents are brittle in edge cases, expensive to orchestrate at scale, and difficult to debug when they fail. But the trajectory is clear. The companies building agent infrastructure today – orchestration frameworks, tool ecosystems, evaluation pipelines – are building the platforms of the next decade.

The question for every technology leader is not whether agents will reshape their industry. It is whether they will be the ones deploying them or the ones being disrupted by them.

The window for strategic positioning is open. It will not stay open long.

Goodbye Prompt Engineering

Tue, 30 Apr 2024 00:00:00 +0000

As we come to understand the possibilities offered by the wave of AI-related technological advances, new ways of relating to these technologies are emerging. Our interactions with generative artificial intelligence are transforming, marking the end of the “prompt engineering” era – a short-lived “era” in which some claimed that mastering the art of “prompting” would be key to benefiting from this new technology. I never subscribed to the notion that “prompt engineers” would lead AI adoption, and here are some reasons to anticipate the gradual disappearance of the ephemeral “art of prompt engineering”:

From Interaction to Collaboration
#

The traditional model of interacting with large language models through specific commands is evolving. The future points toward interfaces with autonomous, goal-directed agents. This shift promises greater alignment with human intentions and a significant increase in AI decision-making autonomy.

Enhanced Collaboration
#

These advanced AI agents are designed to understand and anticipate needs, making them perfect partners in diverse professional environments. Whether optimizing decision processes or offering predictive insights, AI is moving toward a more proactive role, eliminating the need for detailed and repetitive command engineering.

Real-World Applications
#

Imagine an AI that not only responds to commands but also initiates actions aligned with established goals in fields like customer service, healthcare, and finance. This proactive capability could redefine efficiency and effectiveness across various industries. We will progressively see specialized autonomous agents for specific tasks rather than interacting with general-purpose models.

AI Agents on Carles Abarca

Your Code Is One Agent Session From Being Cloned — And There's Nothing You Can Do About It

From TypeScript to Python in an afternoon #

What they found inside #

The protective barrier that no longer exists #

The SaaSpocalypse connection #

So what actually protects your business? #

The implications for the enterprise #

The twist no one expected #

NemoClaw and the Enterprise Awakening: When AI Agents Leave the Garage

What NemoClaw actually is #

The enterprise problem was never intelligence #

AgenTECs: What happens when a university takes agents seriously #

Why this matters now #

The playbook #

The uncomfortable truth #

AI Agents Are No Longer Assisting Scientists. They Are Doing the Science.

The facts #

AI has discovered before, but never on its own #

Era 1: AI as microscope (2018-2023) #

Era 2: AI as colleague (2024-2025) #

Era 3: AI as autonomous researcher (2026 →) #

The Great Acceleration: when AI researches faster than humans can read #

The Compound Acceleration phenomenon #

Problems we will solve decades ahead of schedule #

The consequences nobody wants to discuss #

1. The end of “batch research” #

2. The radical democratization of discovery #

3. The “Orchestra Conductor” as the new scientific role #

4. The peer review crisis #

What this means for universities #

Conclusion #

🤖 Go deeper with AI #

The Chart That Predicts Which Jobs AI Will Kill (And They're Not the Ones You Think)

The Study: 2 Million Conversations with Claude #

The 10 Most Exposed Jobs #

The Demographic Surprise #

The Gap IS the Prediction #

What Changes with AI Agents #

What to Do (Which Is Not Panic) #

The Bottom Line #

China's AI Pincer Move: Qwen 3.5 and CoPaw Are Not a Warning Shot — They're the Main Event

The Qwen 3.5 Story: Frontier AI Goes Free #

CoPaw: The Agent Layer China Was Missing #

The Earthquake Started in January 2025 #

The Alibaba Strategy: OpenAI’s Vision, Open-Source’s Price #

What This Means for the West #

The Democratization Paradox #

What I Am Doing About It #

The Bottom Line #

My AI Agent Is 2 Weeks Old. 72,000 Lines of Code. 5 Projects Shipped.

A Clarification First #

Meet My Digital Apprentice #

The Numbers #

How This Article Got Here #

The Real Insight #

What This Means #

The Bottom Line #

The CIO's Guide to AI Agents: Before You Buy, Ask These 5 Questions

The AI Agent Landscape in 2026 #

Question 1: What Problem Are You Actually Solving? #

Question 2: Build, Buy, or Extend? #

EXTEND: Add Agent Capabilities to Your Existing Platforms #

BUY: Purchase a Specialized Agent Solution #

BUILD: Create Custom Agents Using Foundation Models #

The Decision Matrix #

My Recommendation #

Question 3: How Will You Measure Success? #

Question 4: What’s Your Human-in-the-Loop Strategy? #

Question 5: What’s Your Data Strategy? #

The Decision Framework #

My Recommendations for 2026 #

The Bottom Line #

Banking Will Never Be the Same: An Insider's View on AI Agents

Why Banking Is Perfect for AI Agents #

The Agent Transformation Map #

The Real Challenges (From the Inside) #

What I Would Do Differently Today #

Predictions for 2027-2030 #

The Choice Ahead #

From TypeScript to Python in an afternoon
#

What they found inside
#

The protective barrier that no longer exists
#

The SaaSpocalypse connection
#

So what actually protects your business?
#

The implications for the enterprise
#

The twist no one expected
#

What NemoClaw actually is
#

The enterprise problem was never intelligence
#

AgenTECs: What happens when a university takes agents seriously
#

Why this matters now
#

The playbook
#

The uncomfortable truth
#

The facts
#

AI has discovered before, but never on its own
#

Era 1: AI as microscope (2018-2023)
#

Era 2: AI as colleague (2024-2025)
#

Era 3: AI as autonomous researcher (2026 →)
#

The Great Acceleration: when AI researches faster than humans can read
#

The Compound Acceleration phenomenon
#

Problems we will solve decades ahead of schedule
#

The consequences nobody wants to discuss
#

1. The end of “batch research”
#

2. The radical democratization of discovery
#

3. The “Orchestra Conductor” as the new scientific role
#

4. The peer review crisis
#

What this means for universities
#

Conclusion
#

🤖 Go deeper with AI
#

The Study: 2 Million Conversations with Claude
#

The 10 Most Exposed Jobs
#

The Demographic Surprise
#

The Gap IS the Prediction
#

What Changes with AI Agents
#

What to Do (Which Is Not Panic)
#

The Bottom Line
#

The Qwen 3.5 Story: Frontier AI Goes Free
#

CoPaw: The Agent Layer China Was Missing
#

The Earthquake Started in January 2025
#

The Alibaba Strategy: OpenAI’s Vision, Open-Source’s Price
#

What This Means for the West
#

The Democratization Paradox
#

What I Am Doing About It
#

The Bottom Line
#

A Clarification First
#

Meet My Digital Apprentice
#

The Numbers
#

How This Article Got Here
#

The Real Insight
#

What This Means
#

The Bottom Line
#

The AI Agent Landscape in 2026
#

Question 1: What Problem Are You Actually Solving?
#

Question 2: Build, Buy, or Extend?
#

EXTEND: Add Agent Capabilities to Your Existing Platforms
#

BUY: Purchase a Specialized Agent Solution
#

BUILD: Create Custom Agents Using Foundation Models
#

The Decision Matrix
#

My Recommendation
#

Question 3: How Will You Measure Success?
#

Question 4: What’s Your Human-in-the-Loop Strategy?
#

Question 5: What’s Your Data Strategy?
#

The Decision Framework
#

My Recommendations for 2026
#

The Bottom Line
#

Why Banking Is Perfect for AI Agents
#

The Agent Transformation Map
#

The Real Challenges (From the Inside)
#

What I Would Do Differently Today
#

Predictions for 2027-2030
#

The Choice Ahead
#

From SaaS to SaS
#

The reconversion map
#

The pattern
#

The Silent Revolution of AI Agents
#

From Assistant to Autonomous Agent
#

The Prediction That Is Already Happening
#

Orchestration: The New Paradigm
#

The Architect as Orchestra Conductor
#

The Experience Crisis
#