AB's Reflections

Category: Work

  • The Task Changed, The Job Didn’t — But Your Org Hasn’t Noticed Yet

    There’s a conversation happening quietly in engineering teams, product orgs, and design studios. It surfaces in Slack DMs and whispered break-room conversations. The question underneath is always the same: If AI can do what I do, what am I for?

    That fear makes sense. Engineers who built their identity around writing clean code watch AI generate entire modules in seconds. Product managers who prided themselves on writing crisp specs see AI agents do the same work overnight. Designers watch their Figma files get autocompleted before they’ve finished thinking through the problem.

    But here’s what’s being missed: the task is changing, the job isn’t.

    Writing code was always a means to an end. The job was shipping features that solve problems. Writing specs was always a means to an end. The job was understanding user needs and deciding what to build. AI automates the means, not the end. The bottleneck was never typing speed — it was clarity of thinking, problem definition, and judgment about what to build.

    Those bottlenecks are still ours.

    The Identity Trap

    Most people in technology define themselves by the task they perform, not the outcome they produce. “I’m a backend engineer” means I write backend code. “I’m a PM” means I write specs and manage tickets. When AI starts doing those tasks faster and arguably better, the identity feels threatened.

    The first response is usually denial: “AI can’t really do what I do — it doesn’t understand context, it makes mistakes, it needs constant supervision.” The second is panic: “I’m about to be replaced by a model that costs pennies per thousand tokens.”

    But the real shift isn’t about automation replacing roles. It’s about what happens when execution becomes nearly free and the entire competitive advantage moves to knowing what to build in the first place.

    From Tasks to Judgment

    When people ask what humans will do in this new world, the answer is usually “taste and judgment.” But that’s abstract. What does judgment actually mean?

    It means knowing what to build, when to say no, and how to spot when AI is heading in the wrong direction. It’s defining the guardrails before you let agents run — test suites, design patterns, architectural constraints. It’s understanding that every line of code is future maintenance burden, which makes the discipline to not build more valuable than the ability to build fast.

    In 2014, Melissa Perri warned about “The Build Trap” — companies stuck measuring success by what they shipped rather than what they learned. “Building is the easy part,” she wrote. “Figuring out what to build and how we are going to build it is the hard part.”

    Most companies ignored that. Now AI makes building trivially easy, and those companies are about to drown in features that solve nothing. The agents don’t get tired. They don’t push back. They’ll happily build everything you point them at, whether or not it should exist.

    The Multi-Hat Convergence

    The expectation is shifting: one person who can think about the problem, design the solution, and use AI to build it. This doesn’t mean everyone becomes a shallow generalist. It means the boundaries between roles blur significantly.

    PMs without a hard skill — design or code — and engineers without product sense are both increasingly vulnerable. The trifecta of product thinking, design sense, and technical execution is becoming the baseline, not the exception.

    For experienced professionals considering independence, this convergence changes the economics dramatically. A single person with AI tools can now deliver what used to require a small team.

    The Org Structure Problem

    Most organizations are still structured around tasks, not outcomes. Teams are organized by function — frontend team, backend team, QA team, design team. Performance is measured by task completion: PRs merged, tickets closed, specs written.

    AI makes task completion trivially fast, which breaks these measurement systems completely. The real metric should be business outcomes, but most orgs aren’t wired to measure or incentivize that way.

    Companies are starting to notice. Last year, the Shopify CEO asked employees to prove why they “cannot get what they want done using AI” before asking for more headcount. Last week, Block laid off 40% of its workforce — more than 4,000 people. Co-founder Jack Dorsey was direct: “A significantly smaller team, using the tools we’re building, can do more and do it better.”

    A startup with great direction and AI agents beats a startup with mediocre direction and the same agents. A company with 10 people who know exactly what to build beats one with 100 people building everything they can think of.

    The companies still hiring for “more hands” are optimizing for the wrong bottleneck.

    What This Means for You

    If you’re an engineer, invest in product sense and domain expertise. Understand why you’re building, not just how. Study the business side of your domain — unit economics, customer behavior, market dynamics.

    If you’re a PM, get your hands dirty with at least one hard skill. Design or code, even at a basic level. The ability to prototype your own ideas or understand technical tradeoffs without waiting for a meeting makes you more effective than you’d expect.

    If you’re a leader, start restructuring teams around outcomes, not functions. Measure business impact, not tickets closed. Reward people for solving problems and learning, not for producing code.

    Stop identifying with your task. Start identifying with the outcomes you produce.

    The people making this shift now are building a compounding advantage. The gap widens every month. Domain expertise becomes your moat. The deeper you understand a specific business problem space, the better you can direct agents toward solving it.

    The execution bottleneck is being solved. The judgment bottleneck requires human capacity, and it’s where the real value lives now.

  • The Dark Factory: Engineering Teams That Run With the Lights Off

    A few engineering organisations are already operating a model most companies haven’t begun to consider. While the typical software team debates whether to adopt AI coding assistants, companies like StrongDM are running fully automated development pipelines where agents handle implementation, testing, review, and deployment. Humans set direction and define constraints. The mechanical work happens without them.

    This isn’t speculative. It’s operational. And the gap between companies working this way and those that aren’t is widening fast.

    What “lights off” actually means

    The term comes from manufacturing — factories that run autonomously, with minimal human presence. In software, it describes engineering organisations where AI agents do the bulk of execution work while humans focus on architecture, constraints, and outcomes.

    StrongDM’s approach is instructive: their benchmark is that if you haven’t spent at least $1,000 on tokens per human engineer per day, your software factory has room for improvement. Agents work in parallel on isolated tasks. Code is written, tested, and reviewed without manual intervention. Tasks assigned Friday evening return results Monday morning.

    The ratio of agents to humans is high and growing. But this isn’t about replacing engineers — it’s about fundamentally changing what engineers do.

    The guardrails are the system

    Dark factories aren’t ungoverned. They’re heavily governed in a different way.

    Linters, formatters, comprehensive test suites, design pattern enforcement — these become pre-conditions rather than suggestions. Agents are configured to seek completion only when all guardrails pass. Code review shifts from line-by-line human inspection to AI review with human spot-checks on critical paths.

    The discipline moves from “write good code” to “design good systems for code to be written in.” That’s a different skill. It requires thinking about constraints, validation, and feedback loops rather than syntax and implementation details.

    Anthropic’s experiment building a C compiler with parallel Claude instances demonstrates this principle. Sixteen agents worked simultaneously on a shared codebase, coordinating through git locks and comprehensive test harnesses. The result: a 100,000-line compiler capable of building the Linux kernel, produced over nearly 2,000 sessions across two weeks for just under $20,000. The project worked because the test infrastructure was rigorous enough to guide autonomous agents toward correctness without human review of every change.

    Cursor’s experiments with scaling agents ran into a different problem. They tried flat coordination first — agents self-organising through a shared file, claiming tasks, updating status. It broke down. Agents held locks too long, became risk-averse, made small safe changes, and nobody took responsibility for hard problems. The fix was introducing hierarchy: planners that explore the codebase and create tasks, workers that grind on assigned work until it’s done. No single agent tries to do everything. The system ran for weeks, writing over a million lines of code. One project improved video rendering performance by 25x and shipped to production. Their takeaway: many of the gains came from removing complexity rather than adding it.

    Digital twins as the enabler

    The biggest blocker to agent autonomy has been the fear of breaking production. Digital twins remove that constraint.

    StrongDM built behavioural replicas of third-party services their software depends on — Okta, Jira, Slack, Google Docs, Google Drive, and Google Sheets. These twins replicate APIs, edge cases, and observable behaviours with sufficient fidelity that agents can test against realistic conditions at volume, without rate limits or production risk.

    Simon Willison’s write-up of StrongDM’s approach highlights how this changed what was economically feasible: “Creating a high fidelity clone of a significant SaaS application was always possible, but never economically feasible. Generations of engineers may have wanted a full in-memory replica of their CRM to test against, but self-censored the proposal to build it.”

    What makes this rigorous rather than just better staging is how they handle validation. Test scenarios are stored outside the codebase — separate from where the coding agents can see them — functioning like holdout sets in machine learning. Agents can’t overfit to the tests because they don’t have access to them. The QA team is also agents, running thousands of scenarios per hour without hitting rate limits or accumulating API costs.

    The structural advantage of starting fresh

    Startups and SMBs have a material advantage here. No legacy organisational structure to dismantle. No 500-person engineering floor with stakeholders defending headcount. No 18-month procurement cycles.

    Capital efficiency becomes native. A three-person team with agents can produce output that previously required twenty people. The cost of compute is a fraction of equivalent human labour and falling rapidly.

    This creates an asymmetric advantage. If your competitor ships in days what takes you months, no amount of talent closes that gap. And the competitive pressure isn’t just on speed — it’s on the ability to attract talent that wants to work this way. Senior engineers who’ve experienced agent-driven development don’t want to go back to manual workflows.

    The gap between adopters and laggards

    Companies operating this way are shipping at a fundamentally different pace. The difference isn’t incremental — it’s orders of magnitude in output per person.

    Block’s recent announcement of a near-50% reduction in headcount offers a data point. The company is reducing its organization from over 10,000 people to just under 6,000. Jack Dorsey stated “we’re not making this decision because we’re in trouble. our business is strong” but noted that “the intelligence tools we’re creating and using, paired with smaller and flatter teams, are enabling a new way of working which fundamentally changes what it means to build and run a company.”

    Cursor’s data shows the same pattern. 35% of pull requests merged internally at Cursor are now created by agents operating autonomously in cloud VMs. The developers adopting this approach write almost no code themselves. They spend their time breaking down problems, reviewing artifacts, and giving feedback. They spin up multiple agents simultaneously instead of guiding one to completion.

    The laggards aren’t just slower. They’re increasingly unable to compete for talent, capital, or market position against organisations that have made this transition.

    You don’t need a corporate budget to start

    The dark factory model scales down. A single developer with a Claude Code subscription and well-structured GitHub workflows can run a lightweight version of the same approach.

    Start with one workflow. Pick a repetitive part of your development or business process, establish the guardrails, and let agents handle it. The key investment isn’t in compute — it’s in guardrails and context. Linters, test suites, good documentation, and clear specifications matter more than token budget.

    For SMBs and founders, this is the most asymmetric advantage available. You can operate at a scale that was previously only accessible with significant headcount. The learning curve is steep but short. Within 30 days of serious experimentation, most people develop the intuition for what agents can and can’t handle.

    Projects like OpenClaw — an open-source autonomous agent that executes tasks across messaging platforms and services — demonstrate that the tooling for this approach is increasingly accessible. The software runs locally, integrates with multiple LLM providers, and requires no enterprise licensing. The barrier isn’t access to technology. It’s willingness to change how work gets done.

    What this means beyond software

    Software is where this pattern is playing out first, but the model applies wherever knowledge work is structured and repeatable.

    Audit processes. Compliance checks. Report generation. Data analysis. Document review. These are all candidates for the same approach: clear specifications, comprehensive validation, and autonomous execution within defined guardrails.

    Most traditional industries haven’t started thinking about this. They’re still debating whether to use ChatGPT for email drafts. The firms that figure out how to apply dark factory principles to their domain will have an enormous advantage over those still operating with manual workflows.

    The lights are already off in some factories. The question isn’t whether this approach will spread. It’s how quickly your organisation recognises that the game has changed.

  • From Clicks to Conversations: How AI Agents Are Revolutionizing Business

    From Clicks to Conversations: How AI Agents Are Revolutionizing Business

    For the last decade, businesses have invested heavily in “Digital Transformation,” building powerful digital tools and processes to modernize their operations. While this era brought significant progress, it also created a persistent challenge. The tools we built—from CRMs to ERPs—were largely dependent on structured data: the neat, organized numbers and categories found in a spreadsheet or database. Computers excel at processing this kind of information.

    The problem is that the most valuable business intelligence isn’t structured. The context behind a business plan locked in a 100-slide presentation, the nuance of a customer relationship captured in a rep’s notes, or the true objective of a strategy discussed in a meeting—this is all unstructured data. This divide has created a major hurdle for business efficiency, as great ideas often get lost when people try to translate them into the rigid, structured systems that computers understand.

    The Old Way: The Limits of Traditional Digital Tools

    The first wave of digital tools, from customer relationship management (CRM) software to accounting platforms, were designed for humans to operate. Their critical limitation was their reliance on structured data, which forced people to act as human translators. A brilliant, nuanced strategy conceived in conversations and documents had to be manually broken down and entered into rigid forms and fields.

    This created a significant “gap between business strategy and execution,” where high-level vision was lost during implementation. The result was heavy “change management overheads,” not just because teams needed training on new software, but because of the cognitive friction involved. People are used to working with the unstructured information in their heads; these tools forced them to constantly translate their natural way of thinking into structured processes the software could understand.

    Information TypeBusiness Example
    StructuredEntries in a CRM database, financial data in an accounting platform, inventory numbers in an ERP system.
    UnstructuredA 100-slide brand plan document, a sales rep’s recorded notes describing a doctor they just met, emails discussing a new brand strategy.

    This reliance on structured systems meant that the tools, while digital, couldn’t fully grasp the human context of the work they were supposed to support. A new approach was needed—one that could understand information more like a person does.

    A Smarter Way: Introducing AI Agents

    Welcome to the era of “AI Transformation.” At the heart of this new wave are AI Agents: specialized digital team members that can augment a human workforce. Think of them as a dedicated marketing agent, a sales agent, or a data analyst agent, each designed to perform specific business functions.

    The single most important capability of AI agents is their ability to work with both structured and unstructured information. You can communicate a plan to an agent by typing a message, speaking, or providing a document—just as you would with a human colleague. This fundamental shift from clicking buttons to holding conversations unlocks three profound benefits:

    • Bridging the Strategy-to-Execution Gap: AI agents can understand the nuance of an unstructured plan—the “why” behind the “what”—and help execute it without critical information getting lost in translation.
    • Handling All Information Seamlessly: They can process natural language from documents, presentations, or conversations and transform it into the actionable, structured data that existing digital tools need to function.
    • Reducing Change Management: Because agents understand human language, the need for extensive training on rigid software interfaces is significantly reduced. People can work more naturally, supervising the agents as they handle the tedious, structured tasks.

    To see how this works in practice, let’s walk through how a team of AI agents can help plan and execute a marketing campaign from start to finish.

    AI Agents in Action: Launching a Marketing Campaign

    This step-by-step walkthrough shows how AI agents can take a high-level marketing plan from a simple idea to a fully executed campaign, seamlessly connecting unstructured strategy with structured execution.

    1. The Starting Point: The Marketing Brief – The process begins when a brand manager provides a marketing brief. This brief is pure unstructured information—it could be a presentation, a document, or even the transcript of a planning conversation. It contains the high-level goals and vision for the campaign.
    2. Deconstructing the Brief: The Brand Manager Agent – A specialized “Brand Manager” agent analyzes the unstructured brief and extracts the core business context elements. It identifies key information such as:
      • Business objectives
      • Target audience definitions
      • Key messages
      • Brands in focus
      • Timelines and milestones
    3. The agent then organizes this information into structured, machine-readable “context blocks,” creating a clear, logical foundation that other systems and agents can use.
    4. Understanding the Customer: The Digital Sales Agent – Next, a “Digital Sales” agent contributes by performing customer profiling. It can take unstructured, natural language descriptions of customers (for instance, from a sales rep’s recorded notes) and map them to formal, structured customer segments and personas. This builds a richer, more accurate customer profile than a simple survey could provide.
    5. Creating the Content: The Content Writer Agent – Using the structured business context from the Brand Manager agent, a “Content Writer” agent assembles personalized content. It can reuse and repurpose existing content from a library of approved modules, accelerating content creation while ensuring brand compliance.
    6. Executing the Plan: The Next Best Action (NBA) Engine – Finally, the system brings everything together to recommend the “Next Best Action.” This engine synthesizes the campaign’s business context, the customer’s profile, the available content, and their recent engagement history to suggest the perfect next step for each customer. It recommends precisely what content to send and which channel to use, turning high-level strategy into a concrete, personalized action.

    This orchestrated workflow makes the entire process smoother, faster, and far more intelligent. It creates a virtuous cycle, where the system learns from every interaction to continuously improve the overall strategy and execution over time.

    The Future of Work is Collaborative

    The rise of AI agents marks a fundamental shift in how we work with technology. We are moving from a world where humans must adapt to operate digital tools to one where humans supervise intelligent AI agents that use those tools on our behalf.

    This new wave of AI transformation is not about replacing people, but about augmenting their human workforce without adding headcount. By handling the translation between unstructured human ideas and structured digital processes, AI agents help businesses reduce friction, cut down on turnaround times, and finally bridge the long-standing gap between their biggest strategies and their real-world execution.

  • Shadow AI, Friction Fatigue & the Flexibility Gap: 5 Lessons from the Ivanti Tech at Work 2025 Report

    Shadow AI, Friction Fatigue & the Flexibility Gap: 5 Lessons from the Ivanti Tech at Work 2025 Report

    The Ivanti Tech at Work 2025 report isn’t just a workplace tech survey — it’s a mirror to how modern organizations are struggling (and sometimes succeeding) to adapt to the realities of hybrid work, AI adoption, and employee expectations.

    Here are 5 insights that stood out — and why they matter for anyone building teams, tools, or trust in the modern workplace.

    🔗 Read the full report

    1. Shadow AI Is a Trust Problem, Not Just a Tech One

    Nearly 1 in 3 workers admit they use AI tools like ChatGPT in secret at work.

    Why the secrecy?

    According to the report:

    • 36% want a competitive edge
    • 30% fear job cuts or extra scrutiny
    • 30% say there’s no clear AI usage policy
    • 27% don’t want their abilities questioned

    This is more than a governance issue. It’s a cultural signal.
    Employees are turning to AI to be more productive — but doing so under the radar signals a trust deficit and policy vacuum.

    💡 What to do:
    Leaders need to replace silence with structure — with clear, enabling policies that promote responsible AI use, and an environment where value creation matters more than screen time.

    2. The Flexibility Paradox: High Demand, Low Supply

    83% of IT professionals and 73% of office workers value flexibility highly — but only ~25% say they actually have it.

    Even as companies trumpet hybrid work, asynchronous enablement, autonomy, and outcome-based work norms haven’t caught up. The result? Disengagement and frustration.

    💡 What to do:
    Revisit what flexibility really means. It’s not just about where people work — it’s how they work.
    That means:

    • Tools for async collaboration
    • Decision-making frameworks for remote teams
    • Leaders modeling flexible behaviors

    3. Presenteeism Is the New Fatigue

    The report highlights “digital presenteeism”: workers pretending to be active — jiggling mice, logging in early — to appear productive.

    • 48% say they dislike their job but stay
    • 37% admit to showing up without doing meaningful work

    These are signs of unclear expectations and poor workflow design — not disengagement alone.

    💡 What to do:
    Audit for friction, not just lag.
    Look at your workflows, KPIs, and culture. Are people forced to perform busyness instead of real value?

    4. The Digital Experience Gap Is Real

    While flexible work is valued, many workers find it harder to work outside the office. The report notes:

    • 44–49% say collaboration is easier in-office
    • 36–48% say manager access is better in-office
    • 16–26% say apps are easier to use from the office

    💡 What to do:
    Enable remote-first experience, not just policy:

    • Seamless access to tools and systems
    • Integrated collaboration platforms
    • AI-powered support and IT workflows

    5. Redesign for Trust, Not Just Tools

    The big takeaway?

    Workers don’t just need better AI — they need clarity on what’s allowed
    They don’t just need more flexibility — they need workflows that enable it
    They don’t just need faster tools — they need a culture that values trust over control

    Final Thoughts

    The Ivanti Tech at Work 2025 report is a diagnostic — revealing what happens when new tools are bolted onto outdated operating models.

    For leaders, the message is clear:

    We need to evolve not just our tech stack, but our trust stack.

    🔗 Read the full report