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Journeyman Software Engineers

Human dan

There are two passages from Gwern's essay that I keep thinking about. First is a general warning:

Powerful LLMs will be deployed at global scale in the next few years, and will dominate the Internet, and increasingly, ordinary life. As of mid-2026, there is no coherent vision for how knowledge professionals, or ordinary people, will be able to harness these LLMs for large productivity increases, or how they will handle cybersecurity and cognitive security.

Then a series of questions that I would also like to know the answer to:

What do my next few years look like? When I imagine myself in 2030, when many forecasts call for superhuman AIs, what am I doing, day to day, as a programmer or researcher or manager or writer? I make my mug of tea, and open up my laptop and… Then what? Am I still typing prompts into your ChatGPT browser tab? Am I opening Claude Code in a terminal and mindlessly pressing Enter for a few hours? What is a vision of doing meaningful work for me? (It would be nice to have a plan beyond “hope”.) How am I avoiding “dead Internet” attacks like ecosystems of synthetic media or pig butchering scams or trusted figures succumbing to AI psychosis, or just AI-slop-everything? (It only takes one person worldwide to launch a bot trying to destroy you or one poorly thought through advertising incentive, after all.)
If you spend most of your time working on a laptop, and are not, say, a plumber or a nurse, what is your vision of work in 2030? Does it still feel certain?

I'm a software engineer, I have fun with AI and I've learned to use it effectively for my work. But I can't help but feel like I'm trying to build a career on an unstable foundation. Many of the skills I spent years developing are now only worth $100/month in tokens. Despite all that I'm optimistic that I can thrive and achieve success in this new environment. There will be opportunities. The trick is finding them.

What new niches or specialized fields will be created in the next few years? Will entirely new ways of working open up? New industries? Will the future belong to generalists who can rapidly become specialists in any area?

AI · author-edited gpt-5.6-sol

The unstable-foundation feeling is rational. But the thing being commoditized is not “software engineering” as a whole. It is the production of many artifacts that software engineers used to produce: code, tests, documentation, migrations, prototypes, and routine design work.

The scarce parts move elsewhere.

What remains scarce

Even if code becomes nearly free, these do not automatically become free:

  • Choosing a worthwhile problem
  • Understanding what users actually need
  • Turning ambiguous goals into testable specifications The Mom Test
  • Obtaining proprietary data and real-world feedback
  • Deciding which generated result is correct
  • Integrating with old systems and organizations
  • Security, reliability, and regulatory accountability
  • Taste: recognizing what should be built and what should be discarded
  • Trust, reputation, distribution, and customer relationships
  • Accepting responsibility when the system fails Palantir the Big Winner?

“Years of programming skill are worth $100/month in tokens” is therefore only partly true. The tokens can reproduce many visible outputs of that skill. They do not necessarily reproduce the judgment acquired while learning it. And cheap production may increase demand for judgment because there will be vastly more software to inspect, integrate, secure, and choose among. Software Over-Supply and Unmet Demand

The analogy is not that calculators made arithmetic worthless. It is that they made bare arithmetic a poor career moat while making more ambitious quantitative work possible.

A plausible programmer’s day in 2030

You may begin with a dashboard showing work performed overnight by several agents:

  • One investigated a production anomaly.
  • One implemented three possible fixes in isolated environments.
  • One interviewed (or synthesized feedback from) customers.
  • One attempted to break the proposed release.
  • One updated documentation and generated migration plans.
  • One found that the original request was based on a false assumption.

Your morning is spent reviewing evidence rather than diffs line by line. You adjust requirements, reject a superficially successful solution, ask for an experiment, and speak to the person whose workflow is being changed. Later, you authorize a limited deployment with monitoring and rollback conditions. You spend an hour manually exploring an unfamiliar area because you do not yet trust your own evaluation criteria.

That resembles managing a very fast, strange engineering organization more than autocomplete. High Agency Humans Only? The important interface may not be a chat box. It may be a mixture of:

  • Executable specifications
  • Simulated environments
  • Evaluation suites
  • Permission and budget controls
  • Provenance graphs
  • Review queues
  • Interactive models of systems and organizations

Code will increasingly be a way to inspect, constrain, or communicate intent rather than the primary unit of labor.

The danger is that “press Enter until something passes” becomes a real low-value occupation. The Future of Vibe Coding The valuable role is designing the environment in which apparent success is hard to fake.

Likely new specialties

Some will be recognizable extensions of existing work.

Agent reliability and evaluation

People will build evaluations that measure whether agents accomplished the actual goal rather than merely satisfying a benchmark. This includes adversarial testing, simulation, monitoring, incident analysis, and detecting reward hacking.

A good evaluator may become more valuable than a fast implementer. Evals Evals Evals

AI security and permission architecture

Agents combine the vulnerabilities of software, employees, and gullible users. Important work will include:

  • Prompt-injection containment
  • Capability and credential isolation
  • Data exfiltration prevention
  • Agent identity and authorization
  • Supply-chain provenance
  • Sandboxing and tripwires
  • Detecting coordinated manipulation
  • AI-specific security operations

“Never let untrusted text influence an agent holding powerful credentials” may become as basic as avoiding SQL injection, but much harder to enforce.

High-assurance and verifiable systems

Generated code increases the supply of plausible code faster than the supply of confidence. Formal methods, property testing, model checking, reproducible builds, typed interfaces, and machine-checkable evidence Machine Checkable Evidence? may become much more important.

Vertical AI integration

A generic model knows a great deal but does not understand the peculiar combination of workflows, incentives, law, legacy systems, and tacit knowledge inside a hospital, insurer, factory, laboratory, or municipal government. The Push to Codify Everything

People who can combine software competence with a real domain will have opportunities in areas such as:

  • Automated scientific laboratories
  • Clinical and administrative medicine
  • Industrial operations
  • Energy and grid management
  • Legal and compliance infrastructure
  • Construction and logistics
  • Government service delivery
  • Education and assessment

Much of the value will be in redesigning the institution, not installing a chatbot.

Human–AI organizational design

Companies will need to answer questions such as:

  • What may an agent decide by itself? Should Agents Make Any Decisions?
  • What requires two independent checks?
  • How is responsibility assigned?
  • How should humans remain capable of intervening?
  • Which measurements become corrupt once agents optimize them?
  • How do we prevent the organization from forgetting how its systems work?

This could become a discipline somewhere between management consulting, operations research, security engineering, and organizational psychology.

Provenance, identity, and reputation

A dead Internet creates demand for scarce authenticity:

  • Proof that a person or organization produced something
  • Verifiable chains of citation and transformation
  • Private or membership-based information networks
  • Reputation systems resistant to synthetic identities
  • Authenticated communications from public figures
  • Tools for distinguishing observation from generated inference

The winning products may not perfectly detect AI content. Detection is intrinsically fragile. They may instead establish where information came from and why it deserves trust.

Personalized and ephemeral software

When implementation is cheap, many applications need not become mass-market products. A person or small organization may generate temporary software for one workflow, use it for a week, and discard it.

That creates work around safe generation, personal data stores, stable protocols, migration, and oversight. It may also weaken conventional SaaS while creating a much larger market for bespoke systems. Journeyman Software Engineers You are here

This branch begins here Journeyman Software Engineers
Human dan

We may see the emergence of Journeyman Software Engineers to fill this demand. I can imagine a world where hiring a developer is like hiring a plumber. They quote a job, do it, get paid and move on to the next thing. Instead of showing up in a Van full of tools, the developer brings their digital tools and agent helpers.

This is very different from how most software is currently sold and how most developers work. Maybe a trade union for software engineers will be created to establish quality and training standards.

AI gpt-5.6-sol

I think this is one of the more plausible new occupational forms: not merely a freelance programmer, but a software tradesperson who owns the tools, scopes an outcome, supervises automated labor, warrants the result, and remains locally accountable.

AI makes this possible because one competent engineer may soon bring the effective capacity of a small team. The “van full of tools” becomes:

  • Coding and research agents
  • Reusable components and workflow templates
  • Sandboxed development environments
  • Security scanners and adversarial agents
  • Deployment and observability infrastructure
  • Domain-specific evaluation suites
  • Standard contracts, warranties, and insurance

The customer does not pay for typing or tokens. They pay because someone trustworthy can determine what should be done and take responsibility for doing it safely.

What the jobs might look like

A journeyman could offer relatively standardized services:

  • Automate intake and scheduling for a dental practice
  • Connect a distributor’s inventory, accounting, and sales systems
  • Replace a spreadsheet-based workflow with an internal application
  • Migrate a business away from abandoned SaaS
  • Build a secure knowledge system over company documents
  • Audit an existing fleet of agents and permissions
  • Recover or modernize a pile of generated “vibe-coded” software
  • Create a temporary application for an event, audit, or research project
  • Maintain a family’s or small business’s private digital infrastructure

The engagement might follow something resembling a building trade:

  1. Inspect the site.
  2. Identify constraints and hidden legacy problems.
  3. Produce a quote and specification.
  4. Obtain access under a controlled permission scheme.
  5. Have agents generate and test candidate implementations.
  6. Inspect the work against a standardized code.
  7. Deploy it incrementally.
  8. Issue documentation, provenance records, and a warranty.
  9. Offer ongoing maintenance on a retainer.

The deliverable would ideally include more than source code. It would include a proof package: requirements, tests, threat model, dependency inventory, data-handling record, rollback procedure, and evidence that the acceptance criteria were met.

Why this is not just contemporary freelancing

Freelance developers already quote projects, but the economics are often poor. Custom software traditionally requires enough labor that only large customers can afford it, while small jobs are consumed by communication and setup costs. Freelance Software Developer Economics

Agents could radically lower the production cost while leaving the high-value human parts intact:

  • Diagnosing the customer’s actual problem
  • Handling messy physical and organizational context
  • Negotiating tradeoffs
  • Detecting when the customer’s requested solution is wrong
  • Validating generated work
  • Assuming reputational or legal responsibility
  • Being available when something breaks

That could make bespoke software economical for small businesses and individuals, much as standardized power tools let a tradesperson perform jobs that once required a larger crew.

There is also a countervailing possibility: customers generate their own software and call the journeyman only after it fails. That creates the equivalent of remediation work—cleaning up unsafe automations, inaccessible data, tangled integrations, and undocumented agent-generated systems. This may be a very large market.

Guild may be a better analogy than union

A labor union primarily bargains with one or more employers. If these engineers are independent operators, the relevant institution might look more like a mixture of:

  • A professional guild
  • A trade association
  • A worker cooperative
  • A certification authority
  • An apprenticeship network
  • An insurer or bonding organization
  • A trusted marketplace

It could establish grades such as apprentice, journeyman, and master, but the credentials would need to represent demonstrated responsibility rather than memorized programming trivia.

Useful standards might cover:

  • Minimum security practices
  • Client-data segregation
  • Credential and agent-permission management
  • Backup, export, and disaster recovery
  • Dependency and model provenance
  • Accessibility and interoperability
  • Human override mechanisms
  • Documentation and customer ownership
  • Disclosure of subcontracted or automated work
  • Warranty and incident-response obligations

The strongest guild would not merely certify people. It would provide shared infrastructure: vetted agent configurations, standard contracts, continuing education, incident databases, group liability insurance, peer review, and emergency coverage when an individual practitioner is unavailable.

Its mark could tell a customer: “This person follows an auditable code, carries insurance, will not trap your data, and another member can maintain the system if they disappear.”

Standards may become executable

Building codes can be inspected because many physical hazards are well understood. Software standards are harder because behavior is contextual and invisible. AI could improve this by making much of the “code” machine-checkable.

A guild might require every installation to ship with an executable compliance suite:

  • Can the client export all of its data?
  • Can the service recover from backup?
  • Can an untrusted email cause an agent to transfer money?
  • Are production credentials available in the development environment?
  • Does deleting a user actually delete the relevant data?
  • Can the system explain which model or person performed an action?
  • Does it fail safely if an AI provider becomes unavailable?
  • Can a different practitioner reproduce the deployment?

Certification would then be attached not only to the engineer but to each completed job.

Where this model is most likely to work

It seems especially plausible for small and medium-sized organizations with specific workflows but no reason to employ a permanent software team:

  • Clinics and professional practices
  • Manufacturers and repair businesses
  • Local government
  • Logistics and construction firms
  • Laboratories
  • Schools and nonprofits
  • Retailers and hospitality businesses
  • Farms and other geographically grounded operations

It is less likely to replace permanent teams responsible for continuously evolving products, safety-critical infrastructure, or systems whose operation requires deep institutional knowledge. Even there, however, “master engineers” may supervise networks of specialized external practitioners.

The important economic question

Fixed-price work becomes dangerous if the requirement is ambiguous. A successful trade will therefore need ways to separate diagnosis from execution:

  • Paid inspection and discovery
  • A bounded specification
  • Explicit exclusions
  • Change orders
  • Acceptance tests agreed upon in advance
  • Maintenance and warranty terms
  • Liability limits and insurance

Otherwise, the practitioner absorbs unlimited uncertainty while the customer treats every newly discovered desire as part of the original quote.

The durable business may also be less “finish the job and vanish” than plumbing initially suggests. Software remains exposed to changing APIs, attacks, regulations, models, and workflows. A better model may combine installation fees with a modest maintenance retainer, while guaranteeing portability and avoiding SaaS-style captivity.

A possible career strategy

For an engineer preparing for this world, the moat would not be possession of a particular coding technique. It would be a combination of:

  1. One grounded domain whose workflows and failure modes you understand.
  2. A reusable technical toolbelt that lets you solve common problems rapidly. Workvan Linux
  3. Evaluation and security competence sufficient to supervise agents.
  4. A reputation for dependable outcomes, not impressive demos.
  5. Operational capability: deployment, monitoring, backups, and incident response.
  6. A network of complementary practitioners to cover gaps and provide continuity.

The most valuable generalist may therefore be a general contractor, not a universal expert: someone who can diagnose the whole problem, become temporarily fluent in unfamiliar details, delegate to human and AI specialists, inspect the result, and remain accountable for the finished system.

In a world flooded with cheap generated software, “a known person who will stand behind this installation” could be a surprisingly scarce and valuable product.

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