#Anthropic demo created fire storm. But is the doomsday prediction of software career. Is it real ?
Project team reduction will never match the effort reduction due to AI.
Why?
And yes — some jobs will struggle initially.
That truth is uncomfortable, but ignoring it helps no one. Based on real delivery models and active project planning, it is reasonable to expect around 30% role compression in certain software teams, particularly in execution-heavy roles. But this is only one part of the story.
To understand the bigger picture, let’s start with a real project scenario.
A Real Project Example
Consider a banking SaaS project delivered using Agile practices.
Ten years ago:
- A team of five engineers
- A delivery timeline of about one year
- Multiple feature-based releases
- Significant manual effort in coding, testing, documentation, and coordination
Today, using AI-assisted development:
- A team of three engineers
- The same one-year timeline (kept intentionally conservative)
- AI used for coding, test generation, refactoring, and documentation
- Humans responsible for architecture, compliance, decision-making, and delivery
Yes, fewer people are writing code.
But the project does not become “AI-only,” because software is more than code.
This distinction becomes even more important in enterprise environments.
Enterprise Reality: Why “AI-Assisted” Matters
In one recent enterprise RFP, we had a technically strong AI solution that worked at product-grade, not just as a demo or prototype. From a pure capability standpoint, the system could operate with far less human involvement.
However, we made a deliberate choice to play down the autonomy.
We reframed the solution as an AI-assisted system with mandatory human supervision, even though that reduced how “impressive” the AI sounded. The reason was simple: fully autonomous claims significantly increase legal exposure, audit scrutiny, and compliance cost—for both the vendor and the customer.
By positioning it as AI-assisted, we:
- Reduced legal and liability risk
- Made procurement and compliance teams more comfortable
- Increased the likelihood of enterprise adoption
- Aligned accountability clearly with human owners
Enterprise success, in practice, comes not from maximum AI capability, but from controlled AI responsibility.
Established Corporates and Market Leaders
Large, established companies operate under constraints that startups often underestimate. They have proven products, existing customers, and regulatory obligations that cannot be automated away.
In these organizations:
- Every critical role needs a succession plan
-
Humans remain accountable for:
- Compliance
- Security
- Auditability
- Customer commitments
-
Delivery passes through stage gates:
- Architecture reviews
- Security and compliance sign-offs
- Release approvals
These checks and balances are not overhead. They are what back the credibility of the product being sold.
From the customer’s perspective, trust still matters deeply. If a sales pitch says, “This product was coded by 35 AI agents over the weekend,” most serious buyers hesitate. What they really want to know is:
- Who will support this system long-term?
- Who takes responsibility when something breaks?
- Who understands the system deeply enough to fix it?
A visible, accountable human team remains essential.
Acceptance Still Takes Time
Think about joining a new company. No matter how capable you are, there is always a period of proving and establishing yourself.
Software products follow the same path:
- Initial skepticism
- Gradual adoption
- Trust built over time
- Operational acceptance
AI can accelerate prototypes and demos, but it does not shortcut organizational trust. Quick POCs may be automated, but someone still needs to move across teams, answer concerns, and align multiple stakeholders.
There is no automation for that work.
Smaller Companies and Startups
Smaller companies often explore many directions before finding clarity. This experimentation is necessary and healthy.
With AI:
- More POCs are built
- More hypotheses are tested
- Feedback loops shorten
But once a direction is chosen, success still depends on:
- A committed team
- Operational readiness
- Support and delivery reliability
AI helps teams search faster, but humans still commit and execute.
Freelancers and Service Providers
This transition closely mirrors the WordPress moment.
WordPress simplified website creation, leading to an explosion of sites. The challenge then shifted from creation to visibility, giving rise to the SEO industry.
AI is driving a similar shift:
- Building becomes easier
- Supply increases
- Differentiation moves upstream
Value now comes from framing problems correctly, positioning solutions, and delivering measurable outcomes—not just writing code.
Non-Tech Businesses and Software Investment
Many non-tech leaders have historically viewed software as risky. Large budgets, long timelines, and uncertain outcomes created hesitation.
AI changes that equation:
- Advanced defects surface earlier
- Technical feasibility becomes clearer sooner
- Bad ideas fail faster and cheaper
As a result, confidence in software investment improves, benefiting both enterprises and solution providers.
The Short-Term Reality
In the short run:
- Some roles will struggle
- Career paths will feel uncertain
- Teams will need to adapt
But this is not the first transition of this kind.
This too shall pass.
What remains constant is the need for:
- Trust
- Accountability
- Human judgment
- Clear ownership of outcomes
AI changes how software is built.
It does not remove the human responsibility to stand behind it.