Agile reshaped software engineering over the last two decades. It responded to the failure of large-scale Waterfall programs that collapsed under the weight of slow requirements cycles and heavy documentation. Iterative sprints, cross-team collaborations, and “working software over documentation” were the big three tenets of the software industry. The same popularity enjoyed by Agile has masked its inefficiencies, such as exterminating ceremonies without any end in sight, the spiraling overheads of role specialization, and a business culture that increasingly lives with constant context-switching to the detriment of accountability.
The arrival of large language models (LLMs) has triggered a re-examination. With AI capable of taking on backlog grooming, task decomposition, test generation, documentation, and even architectural drafting, the assumptions that kept Agile in place no longer hold. Structured development is back on the table, not as the rigid Waterfall of the past, but as a new, AI-augmented model where linear phases once again make sense because the handoffs that used to cause breakdowns are now automated. This is “Waterfall 2.0.”
One developer, many roles
In the traditional landscape, it was not feasible for one developer to cover the entire lifecycle at scale. A single engineer could not reasonably perform business analysis, architecture, implementation, testing, and documentation without delays and errors. That constraint is now gone.
With LLMs, analysis can be accelerated by turning ambiguous requirements into structured assumptions. An LLM can take a set of user interviews or market notes and draft user stories that would otherwise require a business analyst. During the design phase, the model can assist in proposing architecture patterns, comparing trade-offs, and even generating UML diagrams. During implementation, AI-assisted IDEs help to scaffold code, enforce style guides, and carry out context-aware refactoring across files. Testing can be the LLM, paradoxically, that wrote the logic itself, outputting unit, integration, and property-based tests aligned with the documented test strategy. During deployment, one would be able to use the AI to generate CI/CD pipelines, IaC templates, and observability dashboards.
This would create a workflow in which a solo developer is prevented from becoming a generalist who, by virtue of needing to know everything, is inevitably spread thin. Productivity-wise, the difference is not just marginal—it is time compressing through layers from months to weeks. One engineer could set up a complete microservices, documentation, and monitoring system. The bottleneck is no longer execution but decision-making: what to build and why.
Real-world examples already show this shift in action. Products that once required teams are now being launched by single developers using AI. SiteGPT, an AI chatbot builder, was coded in a single weekend and went on to generate significant monthly revenue. Writesonic, which grew into a multi-million-dollar AI writing platform, began as a solo project. In another case, a developer built Reeli, a Persian-language AI voice assistant, in just 15 days by combining tools like Cursor and Claude. These are not isolated cases, but proof points that solo, AI-augmented development can scale into million-dollar businesses.
Workflows reimagined
In teams, the effect is even sharper. Traditional Scrum distributes accountability across roles—product owners refine the backlog, Scrum masters coordinate ceremonies, testers validate features, and designers create mockups. But in practice, this distribution creates overhead. Agile promised speed but often delivered meetings.
AI compresses this. A product owner’s backlog refinement becomes a near-instant transformation of raw meeting notes into structured epics and stories, leaving only prioritization for human judgment. The Scrum Master’s function of facilitating standups and retrospectives is automated by AI that consolidates daily updates and detects blockers from commit patterns. Designers find early-stage prototyping handled by tools that translate text prompts into wireframes, which developers can iterate on directly. Testers see their work shift from authoring test cases to validating AI-generated ones.
Ceremonies shrink or disappear. Standups become asynchronous reports digested by AI into a team-wide dashboard. Sprint planning reduces to approving AI-generated task breakdowns. Retrospectives are data-driven, with AI pointing out hotspots in commit logs, code churn, and bug clusters. The net effect is not chaos but clarity. Teams now operate in structured flows that look like linear requirements: design, build, test, and release, but retain the agility to revise each step instantly because the AI handles the repetition and rework.
Artifacts in the AI era
Artifacts, once the Achilles heel of Waterfall, are redefined in this new model. The classic problem was that documents aged faster than they could be updated. Architecture decisions written once rarely reflected reality six months later. Onboarding manuals were outdated the day they were published. Testing strategies were vague, and CI/CD pipelines became brittle scripts that no one maintained.
With AI in the loop, artifacts become living inputs. An architecture decision record can be generated with alternatives, consequences, and recommendations, refined by the architect, and stored in version control. This record is context consumed by the AI itself, guiding consistent generation later in the project. Style guides stored as plain text become not just references but enforceable rules because LLM-powered linters apply them automatically.
Onboarding documentation is continuously updated. Instead of a human maintaining FAQs, the AI scrapes repositories, issue trackers, and commit histories to refresh guides. A new hire does not read a static PDF but queries an onboarding assistant that contextualizes the answers against the latest codebase. Test strategies, once a human responsibility, evolve into a set of high-level coverage goals, while the AI generates the concrete test suites. Pipelines are no longer hand-coded YAML files but AI-suggested configurations that evolve with the project. Security checklists, once buried in compliance documents, become live prompts against which the AI continuously audits code.
The outcome is a closed feedback loop: artifacts are produced faster, stay accurate longer, and serve as direct instructions for both human developers and AI systems.
Balancing speed with risks
The efficiency gains are undeniable, but the risks are structural and require deliberate management. AI-generated code, while fast, is prone to subtle flaws. The drafts highlight a higher rate of errors and vulnerabilities when LLMs are unchecked. This is compounded by verbosity and duplication, which increase technical debt and complicate maintenance. LLMs also suffer from context window limitations, meaning they cannot hold an entire large-scale system in scope. This leads to inconsistencies across modules and fragmented architectural coherence.
There is also the cognitive risk: over-reliance. If developers accept AI-generated artifacts without review, they gradually lose the mental model of their own system. Comprehension atrophies, and what remains is orchestration rather than engineering. The short-term gain in velocity becomes a long-term liability in resilience.
The correct response is not to pull back but to layer in engineering discipline. Code review regains importance, not as a ritual but as a guardrail against AI’s confident errors. Automated testing must expand beyond correctness into security and performance validation. Architecture oversight is critical to ensure that AI-generated micro-decisions still serve the larger system design. In effect, AI must be treated like a junior developer who never tires but always requires review. The senior developer’s role shifts from execution to direction, quality assurance, and strategic design.
The future of education and skills
Shifting waves from these changes will continue to impact all of education and workforce preparation. Universities that carry on teaching programming as if AI did not exist will graduate students behind time. On the other side, relying too much on AI in introductory courses may risk creating engineers unable to code without a crutch. The balance is thin.
This suggests delaying AI-heavy integration until students grasp the fundamentals of algorithms, debugging, and system design. Only then should AI be introduced as a collaborator. Already, some programs are experimenting with dual assignments: one set completed without AI, another with it, to instill both independence and augmentation skills.
The skills that matter most are changing. Prompt construction is not trivial; knowing how to frame requirements in precise terms is now a core engineering task. Critical evaluation becomes a survival skill, as engineers must habitually distrust and verify AI output. DevOps expands into AI-augmented pipelines, where engineers must integrate, monitor, and retrain AI modules. Ethics and compliance move to the foreground because AI-driven systems amplify the risks of bias and privacy breaches.
The labor market is already shifting. New titles like “LLM Engineer” or “AI-Augmented Developer” signal demand for professionals who combine technical depth with the ability to direct AI across the lifecycle. Those who are fluent in both fundamentals and AI collaboration will define the next decade of software engineering.
Conclusion
Waterfall 2.0 is not nostalgia. It is the rational outcome of a new equilibrium: when AI erases the bottlenecks of handoffs, structured phases regain their value. Solo developers now achieve what once required teams. Teams collapse roles and ceremonies into streamlined flows. Artifacts evolve into living inputs that drive both human collaboration and AI generation. Education is retooling for a future where fluency with AI is as important as fluency with code.
The risks, from technical debt to loss of comprehension, are real, but they are not showstoppers. They demand discipline, oversight, and a clear understanding of where human judgment remains irreplaceable.
The software industry is entering a phase where the decisive skill is not just coding or managing but orchestrating AI within structured systems. Those who master this balance will build faster, more consistent, and more resilient software, not because they abandoned the lessons of the past, but because they understood how to carry them forward into the age of AI.
