I’ve experienced the future of software development over the last few days: vibe coding with foundation models like Claude Opus 4.5.

These models are now delivering code that, on many real tasks, feels as good as or better than strong human coders—at roughly 20x the speed for the coding portion of the work. While the end‑to‑end delivery speedup is naturally smaller once you factor in review, integration, and deployment, the acceleration in the "build" phase is undeniable.

The Economics of Intelligence

Right now, building a low-to-medium complexity business-ready app runs between $50–$200 using these models, assuming you’re reasonably disciplined with context and prompt design.

It’s not “cheap” in absolute terms, but the cost curve is brutal. Anthropic has aggressively reduced pricing with Opus 4.5.

Cost Reduction per Million Tokens

Model Version Input Cost Output Cost Reduction
Claude Opus (Legacy) $15.00 $75.00 -
Claude Opus 4.5 $5.00 $25.00 ~67%

This is a roughly 67% reduction on raw pricing, before you even apply caching or batching. In practice, Opus 4.5 also uses fewer tokens to achieve the same outcome, so the effective cost-per-feature is dropping even faster than the headline prices suggest.

If this trend of lowering model cost and escalating sophistication holds, this will soon become the default way to build functionality.

The Paradigm Shift: From Autocomplete to Oracle

The mental shift for me has been moving from “LLM as autocomplete” to “LLM as primary implementation engine”, with humans operating as reviewers, product thinkers, and safety rails.

Benchmarks like SWE-bench Verified scoring in the 80%+ range back up the sense that these models can handle non‑trivial, long‑horizon coding tasks, not just toy examples.

However, humans will remain in the loop, acting more like overseers—the Oracles in the Matrix. LLMs still:

  • Lack a robust world model.
  • Struggle with subtle domain context.
  • Can’t own accountability for the consequences of shipping bad code.

Coders, especially juniors, need to rethink their role. Oversight, system-level thinking, and the ability to interrogate and constrain an AI collaborator will matter more than being the fastest person on the keyboard.

My Experience with Claude Opus 4.5

My take after using the model extensively:

  1. Massive Context Retention: Multi-file, full-stack projects genuinely sit in a single context window—including backend, frontend, infrastructure glue, and tests. The model can reason across them without feeling like it’s “paging out” earlier files.
  2. Semantic Fluency: Semantic instruction following is noticeably better. You can talk to it like a senior engineer and spend less time prompt-hacking, and more time stating product intent, constraints, and edge cases.
  3. The "Hands-Off" Partner: When paired with GitHub integrations, it turns into a hands-off coding partner. Diff review, refactor proposals, migration plans, and test generation happen within minutes.
  4. Product Thinking: Subjectively, it “thinks” more like a product builder than a code generator. It proposes architectures, flags missing requirements, and often pre-empts integration pitfalls you haven’t yet mentioned.

The Productivity Paradox

This is expensive for now, but it is only heading in one direction: lower marginal cost per unit of working software.

Controlled studies still show average productivity gains more in the 20–40% range across broad developer populations—not the meme‑ified “10x.” Yet, in the hands of a power user who is willing to redesign their workflow, the practical speedup on greenfield projects feels much closer to that 10–20x zone.

The paradox is that organisations won’t see those gains unless they also change processes, code review norms, and expectations about what engineers actually do day-to-day.

The New Career Moat

The real career moat is shifting away from typing speed and local API recall toward three things:

  1. Problem Framing
  2. System Design
  3. Judgment Under Uncertainty

Models like Opus 4.5 are starting to dominate at "given a well-specified change, implement it cleanly across the codebase."

But they are still weak at: "What should we build? What are the second-order effects? How does this play with messy socio‑technical constraints?"

That gap is where experienced engineers, tech leads, and architect-types will live: shaping the problem, curating constraints, evaluating trade-offs, and treating the model as an extremely capable but fundamentally non‑accountable collaborator.

Conclusion

If you’re not experimenting, you’re already playing catch-up—not just in terms of tool fluency, but in understanding how your own role needs to evolve.

The people who will thrive are not the ones fighting to prove that “real engineers don’t use AI,” but the ones who quietly build a new muscle: orchestrating agents, enforcing standards, and using models like Opus 4.5 as leverage to move entire product surfaces in days instead of quarters.

References & Further Reading