53/100
hybrid
You have genuine engineering perspective and solid logic, but you're writing like someone explaining consensus rather than challenging it. The piece lacks specificity (no companies, tools, or data sources), relies on hedged assertions ('often,' 'maybe'), and ends with LinkedIn advice-speak. Your Integrity score (4/20) is critical—the actionability rubric is 2/5 because 'orchestrate AI agents aligned to business objectives' means nothing concrete. You're 53/100: hybrid zone where real expertise exists but substance drowns in generality.
Dimension Breakdown
📊 How CSF Scoring Works
The Content Substance Framework (CSF) evaluates your content across 5 dimensions, each scored 0-20 points (100 points total).
Dimension Score Calculation:
Each dimension score (0-20) is calculated from 5 sub-dimension rubrics (0-5 each):
Dimension Score = (Sum of 5 rubrics ÷ 25) × 20 Example: If rubrics are [2, 1, 4, 3, 2], sum is 12.
Score = (12 ÷ 25) × 20 = 9.6 → rounds to 10/20
Why normalize? The 0-25 rubric range (5 rubrics × 5 max) is scaled to 0-20 to make all 5 dimensions equal weight in the 100-point CSF Total.
Zero named entities (companies, tools, research) and pervasive hedge words ('often,' 'maybe,' 'eventually') that dilute claims
Claims come from assumed expertise ('10 min vs 2 days') without specific projects, teams, or domains that would validate experience
Articulates industry consensus ('AI augments, doesn't replace') without challenging assumptions or providing contrarian insight
Second-order thinking present but lacks empirical grounding and ignores countervailing forces (cost pressures, talent pipeline risks)
Generic future-focused advice ('orchestrate AI agents,' 'understand business context') without actionable specificity—fails actionability rubric (2/5)
🎤 Voice
🎯 Specificity
🧠 Depth
💡 Originality
Priority Fixes
Transformation Examples
So the bar doesn't go down with AI. It goes up. Teams get smaller. And senior density increases.
So the bar goes up—in theory. But watch the incentive mismatch: You predict small, senior-dense teams, but CFOs see 'AI does implementation' and think 'Why pay $200k when a $80k mid-level can orchestrate AI?' I'm seeing the opposite pattern at three enterprise clients: they're expanding cheaper orchestrator roles and shrinking senior architect headcount. The bar goes up only if companies value architecture over cost-cutting. Right now, cost is winning. The real question: Can you prove architectural judgment is non-commodifiable before the market decides it isn't?
How: Explore the incentive misalignment: You predict teams get smaller and senior-dense, but does this match economic reality? Add third-order thinking: If orchestration becomes the valuable skill, what prevents commodification? What stops companies from hiring cheaper orchestrators? Challenge your own prediction with steel-man counterarguments.
The best future engineers will understand business context & be able to orchestrate AI agents aligned to business objectives, software architecture & dependencies.
The engineers who survive won't be the best typists. They'll be the ones who can translate 'we need to reduce customer churn' into a system design with clear constraints—data freshness requirements, acceptable latency, cost per prediction—that AI can actually implement. Orchestration isn't a soft skill. It's rigorous translation from business problem to technical specification. Most engineers can't do it because they learned implementation before strategy.
- Removed future-tense hedging—'will be' becomes 'are'
- Replaced buzzwords with concrete example (customer churn → system design)
- Made 'orchestration' tangible (translation from business to technical specs)
- Added controversial claim (most engineers can't do this) with explanation
- Shifted from describing future engineers to explaining the actual skill
Derivative Area: The core argument that AI shifts developer work from implementation to architecture/judgment mirrors the dominant narrative in tech discourse since 2023
Challenge the 'bar goes up' consensus by arguing the opposite: AI might lower the bar by making orchestration a teachable, commodifiable skill. If you can train someone to write effective prompts and review AI output in 3 months (vs 3 years to become a competent coder), the barrier to entry drops dramatically. The current seniors might be protecting their status by overstating the difficulty of architectural judgment. Evidence: Prompt engineering bootcamps proliferating; companies hiring 'AI wranglers' at mid-level comp.
- The junior developer pipeline crisis: If entry-level work disappears, where do future seniors come from? You can't learn judgment without first learning implementation.
- The offshoring arbitrage: If orchestration is the new skill, companies might realize prompt engineering and AI review can be done remotely by lower-cost talent—fragmenting the labor market rather than elevating it.
- The architectural debt explosion: AI generates code faster than teams can maintain it. Are we creating a future where technical debt accumulates at AI speed?
- The measurement paradox: How do you evaluate 'business context understanding' or 'orchestration ability' in hiring? Without clear metrics, companies default to pedigree, potentially creating a new elite gatekeeping.
30-Day Action Plan
Week 1: Specificity overhaul
Rewrite this piece with specific named examples. For every claim (10 min vs 2 days, 4-5 hours vs 4-5 days), add: the tool used (Copilot/Cursor/etc), the project type (API integration, dashboard, data pipeline), and your role (built it yourself, reviewed team output, consulted). Replace 'in good companies' with 3 named companies or specific sectors (fintech, e-commerce, B2B SaaS).
Success: Final draft includes minimum 5 named tools/companies/frameworks and zero hedge words ('often,' 'maybe,' 'eventually'). Every quantitative claim (time comparisons) includes project context. A reader can verify or challenge your assertions.Week 2: Make the ending actionable
Delete the final two paragraphs. Replace with one concrete practice readers can implement this week. Format: 'Here's what changes Monday: [specific action]. Why it works: [mechanism]. How to measure: [outcome].' Test it by sending to 3 engineers and asking: Can you do this Monday? If they say 'I'm not sure how,' it's not actionable enough.
Success: Three engineers read your new conclusion and can describe exactly what they'll do differently in their next project. They can explain the practice without re-reading your piece.Week 3: Develop original angle through research
Pick one unexplored angle (junior pipeline crisis, offshoring arbitrage, or measurement paradox). Conduct 10 interviews with: 3 hiring managers, 3 junior developers hired in 2023-2024, 3 bootcamp instructors, 1 recruiting firm. Ask about concrete changes in hiring patterns, skill requirements, and compensation. Document quotes and data points.
Success: You have 5 specific data points or quotes that challenge the 'bar goes up' narrative or add nuance. You can write a paragraph that starts 'What everyone's missing is...' and back it with primary research.Week 4: Write high-CSF piece integrating all improvements
Write new 800-word piece incorporating: specific tools/companies from Week 1, actionable practice from Week 2, original research insight from Week 3. Open with specific scenario (not 'panic attack'). Build argument with named examples. Challenge consensus with your research finding. Close with one practice readers can test. Target CSF 70+.
Success: Piece includes 8+ named entities, 3+ specific examples with context, 1 contrarian insight backed by research, 1 testable action. When you ask yourself 'Could ChatGPT have written this?'—the answer is clearly no because of the specific research and examples.Before You Publish, Ask:
Could this have been written by someone without hands-on engineering experience in the AI-augmented era?
Filters for: Depth of experience—Currently: yes, because you use no specific tools, projects, or scenarios that require direct experience. Target: no, because you name the tools, describe specific debugging sessions, and show pattern recognition from actual practice.If I forwarded this to a senior engineer at Google, would they learn something new or just nod along?
Filters for: Originality threshold—Currently: they'd nod along (everyone knows AI is good at syntax, bad at context). Target: they'd stop at one insight and think 'I hadn't considered that angle' (junior pipeline crisis, offshore arbitrage, measurement paradox).Can a reader implement something concrete Monday morning based on this piece?
Filters for: Actionability and integrity—Currently: no, 'orchestrate AI agents' is too abstract. Target: yes, they can write a constraints doc, try a specific prompting structure, or measure AI output quality with your framework.💪 Your Strengths
- Strong opening hook ('panic attack') that creates immediate engagement
- Genuine engineering voice—'The output technically works but doesn't align with architecture' shows real experience
- Clear logical progression from current state to future implications
- Second-order thinking present: you move beyond 'AI replaces devs' to explore value shift upstream
- Conversational asides ('But not yet') create authentic personality
You're writing from genuine expertise but packaging it like consensus content. The difference between 53 and 75+ isn't working harder—it's being specific, challenging assumptions, and making your insights actionable. You have the engineering chops to write authoritatively about what AI actually does in production environments. You have the strategic thinking to explore second and third-order effects. What's missing is the courage to name names, cite specifics, and challenge the narrative everyone's repeating. Your best path forward: Pick one unexplored angle (I'd bet on the junior pipeline crisis or the offshore arbitrage), do the research nobody else is doing, and write from evidence rather than intuition. You could own a contrarian position in this space if you're willing to leave the safety of consensus.
Detailed Analysis
Rubric Breakdown
Overall Assessment
Strong authentic voice with genuine personality breaking through. Opening panic attack hook and conversational asides ('But not yet') feel natural. Minor over-explanation in middle section dilutes energy. The closing reframes the problem confidently without hedging. Voice stays grounded in real engineering experience.
- • Strong opening establishes credibility through vulnerability—reader trusts a real engineer wrote this
- • Confident conclusion with concrete reframing (bar goes up, not down) avoids the wimpy AI equivocation pattern
- • Specific, measurable examples (10 mins vs 2 days; 4-5 days vs 4-5 hours) feel earned, not invented
- • Middle section (paragraphs 4-6) slides into explain-everything mode, diluting the punchy opening energy
- • Occasional over-listing ('defining guardrails, enforcing architectural alignment, pruning complexity') feels like checking boxes
- • Final insight 'The bar doesn't go down...it goes up' is good but could land harder with more specificity
Rubric Breakdown
Concrete/Vague Ratio: 1:2.25
The piece uses time comparisons and workflow scenarios to ground arguments, but relies heavily on hedge words ('often,' 'eventually') and broad claims about AI capabilities. No named companies, products, or research cited. The argument structure is strong, but specificity suffers from abstract declarations about future AI without concrete data or examples.
Rubric Breakdown
Thinking Level: Second-order with emerging third-order elements
This piece demonstrates solid second-order thinking about AI's impact on engineering roles, moving beyond "AI replaces developers" to explore how value shifts upstream. However, it lacks empirical grounding and doesn't explore countervailing forces or implementation challenges that could constrain the predicted trajectory.
- • Reframes the conversation from 'AI replaces developers' to 'where does value accrue' - genuine insight shift
- • Recognizes that constraint satisfaction and architectural judgment aren't easily automatable - shows understanding of tacit knowledge
- • Connects micro-level change (typing efficiency) to macro-level team composition shift - demonstrates systems thinking
- • Acknowledges the 'not yet' caveat while still making predictions - shows epistemic humility
Rubric Breakdown
The piece articulates a widely-held industry consensus with competent framing but limited originality. The core argument—that AI augments rather than replaces developers, shifting focus to architecture and judgment—dominates current tech discourse. The execution is clear but lacks surprising insights or data-driven evidence.
- • Framing developers as 'AI orchestrators' aligned to business objectives rather than syntax writers—operationally underexplored
- • The implicit claim that architectural judgment and business context become *more* scarce/valuable (vs. the safer narrative that they remain constant)
- • The distinction between 'implementation-level work' becoming automated vs. strategic/design-level work—rarely quantified in current discourse
Original Post
The panic attack is starting again. Will AI soon write 100% of code? Eventually AI will go beyond being a tool that's only as good as the person using it, and will evolve to making sophisticated decisions on its own. But not yet. Right now, even in good companies the state of affairs is more like: a software engineer spends 10 minutes writing the code with AI & then 2 days fixing all the little things. The output is often overly complex, verbose, and poorly structured. It technically works, but it doesn’t align cleanly with the system architecture or long-term maintainability. That’s because today’s models are great at generating syntax but they don’t truly understand context, tradeoffs, or constraints. So senior devs are spending time defining guardrails, enforcing architectural alignment, pruning complexity, and making judgment calls about how the pieces should actually fit together. But its also true that a simple project that might’ve taken 4 to 5 days to code 10 years ago is now a 4 to 5 hour job for the engineer who knows how to manage AI agents. So, yes, coding (like literally the syntax & the typing of it) is becoming less and less the bottleneck in a product/engineering team. It’s much more about what to build and why, the architecture, the decisions about coding strategy, how components fit together, key constraints, etc. So, maybe the question and the worry shouldn’t be if AI is writing 100% of code. AI will eventually handle most of the implementation-level work. Instead, what happens to the dev role when typing isn’t the scarce skill anymore & value moves upstream? i.e. syntax is now cheap, but architecture & judgement is still expensive. The best future engineers will understand business context & be able to orchestrate AI agents aligned to business objectives, software architecture & dependencies. So the bar doesn’t go down with AI. It goes up. Teams get smaller. And senior density increases.