AI has made software creation feel strangely weightless.
A person describes a problem. A large language model produces a prototype. The prototype has a login screen, a database schema, an API, maybe even a dashboard. The emotional effect is powerful: what used to require a vendor evaluation, a budget request, a product team, and months of coordination now appears in minutes.
From there, a seductive conclusion follows:
Why buy standard software at all?
If every company, every department, every team, and eventually every individual can “vibe code” exactly the solution they need, perhaps the age of standard software is over. No vendor dependency. No compromise with a generic product roadmap. No license negotiation. No waiting for the feature request to be prioritized. Everybody creates their own solution for their own problem, custom tailored and immediately available.
It is an attractive image.
It is also incomplete.
The question is not whether AI can generate software. It obviously can. The question is whether generated software can replace the social, economic, operational, and intellectual structure that standard software represents.
I am not so sure.
The Illusion Of The Solved Domain
Large language models are most impressive when the problem is already well represented in their training material. They are strong at generating solutions when the shape of the solution is already known: CRUD applications, integration scripts, reporting views, simple workflows, API wrappers, internal dashboards, boilerplate infrastructure, and countless variations of familiar software patterns.
In those areas, AI is genuinely useful. It compresses access to existing knowledge. It lowers the cost of prototypes. It helps specialists move faster and helps non-specialists explore possibilities that would previously have been locked behind implementation cost.
But this is not the same as replacing the product.
AI performs best where the problem domain has already been made legible by others. Someone wrote the documentation. Someone debugged the edge case. Someone discovered the failure mode. Someone argued with customers. Someone paid for the release engineering, the support burden, the migration path, the security patch, the incident response, the pricing model, and the maintenance of trust.
The LLM can reproduce patterns because the world has already paid to produce them.
This distinction matters. If a generated solution works because the domain is already mature, then AI is not eliminating the need for standard software. It is feeding on the knowledge standard software helped create.
Standard Software Is Not Just Code
The weakest argument for standard software is: “You should buy this because writing code is hard.”
That argument is becoming less true every month.
The stronger argument is different: standard software is not merely code. It is accumulated domain learning turned into a reusable operating model.
A mature product contains:
- decisions about what not to expose
- defaults shaped by many customers, not one internal preference
- failure handling learned through production incidents
- upgrade paths across versions and environments
- compatibility promises
- documentation, training, and support routines
- security response processes
- commercial responsibility
- a roadmap that balances individual demand against collective usefulness
The visible interface is only the surface. Underneath sits a body of judgment.
This is especially obvious in infrastructure and platform engineering. A database self-service product, for example, is not valuable because it can create a database instance. Many tools can do that. The value lies in everything around the instance: binding, backup, restore, upgrades, monitoring, isolation, billing, auditability, support boundaries, and the confidence that the same operating model will still be there when something breaks at 03:17 on a Sunday morning.
What anynines Has Learned The Hard Way
anynines is a useful example because it has lived through several waves of infrastructure change without treating any single wave as the whole truth.
The company has long operated in enterprise platform engineering, managed data services, and cloud-native tooling. Its current business still rests heavily on mature Cloud Foundry-based data services and platform operations. At the same time, anynines is deliberately transitioning toward Kubernetes-native offerings such as Klutch and a9s Hub.
That transition is not a panic move. It is a thoughtful act of stewardship.
Cloud Foundry is no longer the center of gravity for new platform investments, but it remains important for many conservative and regulated enterprise environments. Customers still depend on it. Employees have built real expertise around it. Revenue and responsibility still exist there. A wise company does not pretend that a new paradigm deletes old commitments.
anynines’ north star is explicit: preserve the company, honor commitments to employees and customers beyond pure economics, maintain Cloud Foundry-based revenue while transitioning toward Kubernetes-native offerings, and preserve hard-won operational knowledge instead of discarding it.
This is what benevolent technology companies are supposed to do. They do not chase every hype cycle as if history were disposable. They carry knowledge forward.
The a9s Data Services story shows why this matters. These services are not merely scripts that provision PostgreSQL, MongoDB, Redis, RabbitMQ, Elasticsearch, MariaDB, Prometheus, and related systems. They represent years of production-grade lifecycle automation: installation, configuration, updates, backup and restore, monitoring, self-healing, support, and operational discipline for enterprise environments.
That knowledge cannot be regenerated responsibly by asking a model to “build me a database platform.”
An LLM can produce a plausible operator, a Terraform module, or a control-plane sketch. It cannot magically reproduce years of operational scars. It cannot know which default became dangerous after the third enterprise rollout. It cannot feel the cost of a failed restore. It cannot take commercial responsibility for a customer whose business depends on the system.
Klutch and a9s Hub continue the same logic in a Kubernetes world. Klutch is not interesting because it produces yet another YAML abstraction. It is interesting because it tries to preserve the developer self-service lessons of the Cloud Foundry era while adapting them to Kubernetes, heterogeneous automation backends, hyperscaler APIs, and multi-cluster governance.
The real product is not “database by prompt.” The real product is a trustworthy control plane where application teams can consume data services consistently while platform teams retain governance, visibility, auditability, and operational control.
That is standard software at its best: individual teams get freedom because someone has done the difficult standardization work on their behalf.
The Knowledge Ecology Problem
There is another uncomfortable point.
LLM providers do not create the world’s software knowledge from nothing. They package, compress, and resell patterns derived from human intellectual output: code, documentation, books, articles, tickets, tutorials, examples, architecture discussions, and product knowledge created by individuals, communities, open-source projects, and commercial vendors.
Call it extraction, appropriation, or, in the angry shorthand, “stealing.” The legal and moral debates will continue. But the economic risk is already visible as a thought experiment.
What happens if the commercial structures that create high-quality software knowledge are damaged too deeply?
If product companies cannot finance the work, fewer products are built. If maintainers cannot afford to maintain, fewer libraries remain healthy. If experts cannot sell expertise, fewer experts spend years developing it. If everyone only consumes the compressed output of previous human work, the knowledge base begins to decay.
At first, the system still looks magical because it is living off accumulated capital. Then the inputs weaken. Fewer deep manuals are written. Fewer production lessons are documented. Fewer domain specialists can afford to publish what they know. Fewer companies can justify investing in boring, hard, reusable product quality.
Eventually the model has less genuine new material to absorb.
No more high-quality human input means no more high-quality synthetic output. The providers that seemed to replace the knowledge economy would have undermined the very source they depended on. Everybody loses: vendors, customers, developers, communities, and the environment around them.
This system cannot scale if it destroys its own substrate.
The Waste Of Everyone Building The Same Thing
The dream of universal custom software also has a hidden waste problem.
When every organization believes it can generate its own perfect solution, everyone repeats the same lessons. The same security mistakes. The same data-model mistakes. The same operational blind spots. The same misjudgment of domain complexity. The same slow discovery that the “simple internal tool” has become a production dependency with users, permissions, migrations, audit logs, support expectations, and compliance risk.
This is hubris disguised as empowerment.
Standard software exists partly because individual organizations are not wise enough, patient enough, or economically positioned to rediscover every lesson alone. A product vendor amortizes learning across many customers. A good open-source project amortizes learning across a community. A healthy ecosystem lets the same hard-earned lesson benefit many users.
That is not dependency for its own sake. It is civilization at software scale.
The alternative is not freedom. Often it is duplication.
Duplication consumes money. It consumes engineering attention. It consumes GPU cycles. It consumes cloud resources. It consumes energy. It creates abandoned internal systems that nobody wants to own and nobody can safely remove. It turns every team into a small software vendor without the structures, incentives, and responsibilities of a real software vendor.
From a societal perspective, that is not progress. It is waste with a futuristic user interface.
What AI Should Change
None of this means standard software should remain untouched.
AI will change standard software profoundly. It should.
Bad standard software that survives only because customization is expensive will become harder to defend. Products with poor APIs, weak documentation, rigid workflows, and slow support will face pressure from AI-assisted alternatives. Customers will expect more adaptability. Developers will expect better automation. Product teams will use AI to generate tests, documentation, migration helpers, support summaries, sample integrations, and user-specific configuration paths.
AI should reduce friction around standard software. It should make products easier to evaluate, integrate, operate, and extend. It should help vendors learn from customer usage faster. It should help customers express their intent more clearly. It should make the boundary between product and customization more productive.
But the sustainable path is integration, not destruction.
AI should amplify the ecosystem that produces software knowledge. It should not cannibalize it for a short-term valuation story.
The healthy pattern looks like this:
- vendors use AI to improve products, support, documentation, and migration paths
- customers use AI to adapt standard software safely at the edges
- open-source communities use AI to reduce maintenance burden without erasing maintainer credit
- commercial models keep funding the hard work of productization
- legal and licensing structures evolve so that knowledge creation remains economically viable
- society benefits from less duplication, not more
This is where anynines’ posture is instructive. The company prefers automation that reduces waste, standardization that preserves operational truth, and product decisions that keep responsibility clear. It avoids custom development wherever possible and prefers sponsored feature work only when it remains compatible with the product vision and useful beyond one customer.
That is a humane and economically sane principle: individual needs matter, but productized learning should compound.
Does AI Mean The End Of Standard Software?
It may mean the end of lazy standard software.
It may mean the end of vendors that hide behind lock-in while delivering little accumulated wisdom. It may mean the end of products that are little more than forms over databases with weak workflows and no operational depth. It may force standard software companies to expose better interfaces, support more flexible configuration, and prove that their product contains knowledge worth paying for.
Good.
But it does not mean the end of standard software itself.
The deeper the domain, the more standard software matters. The more regulated the environment, the more responsibility matters. The more operational the product, the more accumulated lessons matter. The more many customers share the same hard problem, the more wasteful it becomes for each of them to generate a local imitation.
AI will not abolish the need for shared products. It will raise the bar for them.
The winning products will combine standardization and adaptability. They will use AI where AI is strong: interpretation, acceleration, summarization, scaffolding, support, and guided configuration. But they will still be grounded in human-made domain expertise, production experience, and commercial accountability.
That is the wise path.
Not blind resistance to AI.
But a sustainable integration of AI into the existing software ecosystem, so that human knowledge continues to be created, funded, refined, and shared.
The alternative is a quick cash grab: break the ecosystem, harvest the residue, celebrate a few more hyper-IPO billionaires, and leave everyone else with duplicated tools, weakened vendors, exhausted maintainers, higher energy consumption, and less real knowledge than before.
That would not be technological progress.
It would be a failure of stewardship.
anynines’ best contribution is to stand for the opposite: thoughtful automation, honest abstractions, durable commitments, and software that reduces waste instead of multiplying it. In an AI-shaped software economy, that kind of standard software is not obsolete.
It is more necessary than ever.
