The enterprise technology stack has evolved through three distinct eras, each fundamentally reshaping not just our tools, but how we organize and the very nature of work itself.

  • IT 1.0 was about building custom in-house software to improve company productivity and operations. Hierarchies were traditional, IT staff were core, and most jobs tied directly to developing or operating the systems that mapped human-centric processes to software workflows.
  • IT 2.0 outsourced that intelligence to hundreds of Software-as-a-Service (SaaS) solutions delivered over the Internet. The result was a sprawling modular stack that needed humans in between – SaaS system administrators, implementation engineers, system integrators, IT project managers, data analysts – doing “glue work” to reconcile mismatched systems. These jobs often fell into what anthropologist David Graeber called “bullshit jobs”: work that exists mainly because systems can’t talk to each other and users cannot self-serve their needs.
  • IT 3.0 is dissolving this glue with AI-native systems and agents. AI systems can draft, coordinate, and produce outcomes without predefined workflows. The bullshit jobs of IT 2.0 are first on the chopping block. Just as CAD tools erased armies of draftsmen, or UBS’s trading algorithms emptied an entire trading floor in Stamford, today’s AI agents are already hollowing out sales ops, recruiting coordinators, junior devs, and SaaS admins.

Builders and leaders now need to rethink what’s worth building, which roles to preserve, and how to design products for a world where the “information organization tax” is no longer paid by humans.

IT 1.0 – Building and Owning the Stack

Before the Internet and cloud computing, enterprises staffed full IT departments and procured from software vendors like IBM, Oracle, and SAP to build and run in-house systems. These were expensive and specialized, but tightly integrated with the business. This software translated existing human processes into workflows defined in bespoke software living on top of a database.

IT departments were core strategic assets, staffed with programmers, system operators, and managers who created bespoke systems. Jobs in this era were directly tied to creating or maintaining foundational business infrastructure. This period gave us foundational software development methodologies like Agile, born from projects like Chrysler’s internal payroll system. The work was complex and expensive, but it was essential.

IT 2.0 – SaaS and the Bullshit Job Boom

The 2000s marked the era of SaaS, with software now delivered over the Internet. Companies shifted from building to subscribing, democratizing access to powerful tools like cloud-based CRMs, HR suites, and ERPs. This enabled consumption-based pricing and product-led growth business models. At the same time, it created a new systemic problem: a fragmented modular stack of hundreds of applications that could not seamlessly communicate. Data silos and operational silos emerged everywhere, with Excel files pushed via email to glue everything together.

This fragmentation gave birth to a massive ecosystem of what David Graeber termed “bullshit jobs” – roles that exist primarily to service the friction between systems. These aren’t jobs that create direct value; they exist to pay the “information organization tax.” Duct tapers patch half-working systems together with shoddy code or send Excel files via email. Box tickers send Excel sheets to half a dozen folks for check-off, creating the appearance that something productive is being done when it is not.

This boom in “glue work” included:

  • System Administrators: Entire careers built around configuring, managing, and patching platforms like Salesforce or Workday.
  • Implementation Engineers: Specialists whose job was to connect one SaaS tool to another and migrate data between them.
  • Data Entry & Junior Analysts: Armies of people hired to manually move data from spreadsheets and PDFs into rigid SaaS formats.
  • Operations Roles (Sales Ops, HR Ops, Rev Ops): Professionals who spend their days coordinating approvals, managing handoffs, and bridging gaps that APIs and integrations never quite solved.

Graeber’s notion of bullshit jobs became reality: people spending careers moving data from one rectangle on their monitor to another. This wasn’t work in the economic sense of creating value – it was a side effect of SaaS modularity and weak interoperability.

The information organization tax was massive. Meetings, cross-departmental handoffs, redundant reporting – all to coordinate intelligence scattered across dozens of silos.

This era also saw the rise of IT Consulting and Outsourcing, with inherent misaligned incentives. Shoddy software was developed to maximize overall contract value, including system integration, data migration, and ongoing maintenance contract renewals. The now-hollowed-out IT departments were no longer technical enough to ensure quality of work. Boeing’s outsourcing of its software design through layers of sub-contracting was the biggest showcase of this failure.

IT 3.0 – AI Dissolves the Glue

The AI-native wave is fundamentally different. Instead of creating more silos, AI agents and copilots are dissolving the “glue” that holds the fragmented IT 2.0 stack together. These systems can draft workflows, translate data between formats, and execute complex processes across multiple tools without human intervention or mediation.

Just as CAD tools turned hundreds of draftsmen into a handful of designers with software, or as UBS shuttered its massive Stamford, Connecticut trading floor in 2012 after algorithmic trading made human traders redundant, AI is now dismantling the SaaS-created glue layer. All the workflow definitions and playbooks are becoming obsolete and will be codified within the model and tools.

UBS Stamford

IT 3.0 software now requires environments and infrastructure for agents to operate, instead of translating human-centric processes from the 1900s into workflows – the era of agent-computer interfaces.

Click Ops

As artificial intelligence improves exponentially, entire job categories vanish. AI is destroying them at scale:

  • SaaS admins → AI copilots can auto-generate workflows, reports, and integrations. Admin-heavy orgs will shrink fast.
  • Recruiting coordinators → Chatbots already schedule interviews and screen resumes.
  • Entry-level developers → Code assistants can handle the glue code and CRUD apps.
  • Sales ops & BDRs → AI can personalize outreach and process leads at volume.
  • Finance & HR ops → AI reconciles invoices, updates HR records, and generates compliance docs.

The changes are accelerating. Previously I’ve pointed out that moats in enterprise software are defined by Cost to Implement, Time to Implement, and Time to Utility. From IT 1.0 to 3.0, the cost and time to implement are reducing significantly, and time to utility is nearly immediate. It takes almost no time to issue commands to ChatGPT instead of needing months-long training processes for super users to learn PeopleSoft or EPIC and propagate change management downwards.

Which IT 2.0 Tools Are Most at Risk?

AI will hit hardest where SaaS tools created clerical overhead:

  • CRM (Salesforce, HubSpot) – lead enrichment, pipeline updates, report generation: all ripe for AI automation.
  • ATS & HR platforms (Workday, Greenhouse) – resume parsing, candidate scheduling, payroll entry: trivial for AI.
  • Customer support platforms (Zendesk, ServiceNow) – tier-1 support is already being offloaded to LLM agents.
  • Project management (Asana, Jira, Monday) – task creation, updates, and cross-tool syncing will be handled by AI copilots, reducing the need for ops roles.
  • Finance/ERP (NetSuite, SAP) – invoice matching, expense categorization, forecasting: automatable.

The SaaS platforms may survive, but the job ecosystems around them will not.

Strategic Implications for Builders and Leaders

The transition to IT 3.0 requires fundamentally rethinking how we create value and organize work:

B2B SaaS Product Engineering

  1. Design AI-native products – Deliver Service as Software where customers will subscribe to agents for outcomes. Build loosely coupled tools that can be utilized by AI agents to deliver outcomes, not human-centric click-ops interfaces. The winning systems will be those that agents can operate autonomously, not those requiring human clicking and configuration.
  2. Eliminate fake work for your users – If your product requires dedicated admins or coordinators, you’re vulnerable. Every manual handoff is a target for AI automation.

For Job Transformation

  1. Restructure teams around high-leverage roles – Expect thinner middle layers. Ops-heavy roles will contract while roles that define operations architecture, strategy, handle exceptions, and provide creative direction will expand. Invest in “AI-literate” staff who can architect and supervise AI systems, not shuffle spreadsheets. This accelerates the “shift left” movement from DevOps, except now we will have more specialized engineers doing platform engineering to enable AI Agents to operate on the left.

Shifting responsibility to the Left for AI Agents

  1. Create new value categories – As AI handles execution, human roles shift toward guardrailing, operations, judgment, and relationship building. The opportunities lie in work that requires deep technical expertise, deep context, reasoning, or genuine human connection.

For Business Strategy

  1. The Model is the Product. The Distribution is the Moat – Own the AI modeling layer. Control your destiny. Generalist models will not understand specific business context. Even the smartest talents take time to be trained and onboarded to individual businesses, and AI is no different. Hiring and recruiting in this area is extremely challenging due to intense competition for talent, but more talent will become available as we mature in IT 3.0.

The Irony: AI’s New Bullshit Jobs

While AI eliminates IT 2.0’s glue work, it’s creating its own bullshit jobs: AI Slop Janitors. Writers who once led creative teams now edit ChatGPT’s robotic prose for 1-5 cents per word (versus 10+ cents for original writing). They fix the same formulaic mistakes daily – removing “delve” and “nevertheless,” fact-checking hallucinations, making text sound less awkward.

The absurdity peaks when freelance platforms use AI detectors while simultaneously hiring people to make AI text undetectable. Writers get paid to fool the very systems their employers use to catch AI content. Human workers are being brought to fix what AI gets wrong.

This pattern extends beyond writing:

These aren’t valuable human-in-the-loop systems – they’re low-paid workers cleaning up AI’s mess.

Conclusion: Beyond the Bullshit Layer

Each IT wave created and destroyed jobs. IT 1.0 built careers around creating systems. IT 2.0 spawned an entire ecosystem of SaaS glue workers. IT 3.0 is sweeping those away while creating new, often worse, forms of algorithmic busywork.

The cruel twist: AI’s current limitations create transitional bullshit jobs even worse than their predecessors. At least Salesforce admins mastered complex systems and could progress to solution architecture. Today’s AI slop janitors just fix the same robotic mistakes, over and over, for pennies – a dead-end role with no career progression.

But there’s also unprecedented opportunity. As AI eliminates the information organization tax, it frees humans to focus on genuinely creative and strategic work. The winners in IT 3.0 won’t be those who resist this change, but those who embrace it – building AI-native systems, creating new categories of value, and defining roles that amplify human judgment with machine execution.

The bullshit era isn’t ending – it’s evolving. The question isn’t whether we’ll transcend this cycle, but who will seize the opportunities in the transformation. Those who understand this shift and position themselves accordingly will thrive. Those who cling to IT 2.0 paradigms will find themselves managing the new bullshit jobs or, worse, replaced by the very agents they refused to embrace.

References

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@article{
    leehanchung_bullshit_jobs,
    author = {Lee, Hanchung},
    title = {The End of "Bullshit Jobs": From IT 1.0 to the AI-Powered 3.0 Era},
    year = {2025},
    month = {09},
    day = {19},
    howpublished = {\url{https://leehanchung.github.io}},
    url = {https://leehanchung.github.io/blogs/2025/09/19/bullshit-jobs/}
}