If you paid attention to hiring in AI over the last week, you probably saw the stat: 70% of early-career tech professionals in India are AI-proficient. Only 23% are AI-native.
The numbers come from a joint Nasscom-Monster report published in July 2026, and they've been shared, reshared, and commented on by thousands on LinkedIn. The reaction is universal — "proficiency is not enough anymore, you need outcomes."
But what does that actually mean in practice?
We run SuperAIDevs, a job board that tracks 5,765 active AI engineering listings — every one of them real, live, and updated daily. When we heard the 70/23 stat, we didn't want to guess what it meant. We wanted to see what the data says.
So we analysed every active listing on our board. Here's what we found.
What the 70/23 Gap Actually Looks Like
The Nasscom report defines the two categories clearly:
- AI-proficient: Can use AI tools, understand concepts, have completed courses or projects involving AI.
- AI-native: Can engineer production systems using AI, demonstrate engineering judgment, optimise for cost and outcome, and orchestrate multiple AI capabilities together.
The 47-point gap isn't about knowledge. It's about application.
When we looked at what employers are actually asking for in their job postings, the difference became concrete:
| Skill | Appears in Listings | Category | |---|---|---| | Python | 85% | Core (table stakes) | | LLMs | 74% | Core (table stakes) | | RAG | 38% | Important | | LangChain / LangGraph | 35% / 30% | Important | | Production deployment | 22% | Differentiator | | Cost optimisation | 8% | AI-native | | Evaluation / benchmarking | 12% | AI-native | | System design | 15% | AI-native |
The first three rows are what "AI-proficient" gets you. Courses teach Python. Tutorials demo LLMs. Bootcamp projects build a RAG pipeline.
The last three rows are what separate the 23% from the 70%. Nobody teaches cost optimisation in a course. No bootcamp covers production evaluation at scale. System design for AI systems is still a niche skill that most engineers pick up on the job.
The Salary Gap Confirms It
We looked at compensation across all 920 AI engineering listings that disclosed salary data. The average range is $162K–$234K.
But when we filtered for listings that explicitly mention production deployment, evaluation, or cost optimisation (the "AI-native" signals), the average shifted to $195K–$285K — a 22% premium on the low end and a 20% premium on the high end.
Employers are paying more for people who can ship and measure, not just build and demo.
The Three Skills That Define AI-Native
Based on our data, AI-native engineering comes down to three capabilities that are consistently requested across high-salary listings:
1. Production Engineering Judgement
Every demo works. The question is what happens at 10,000 requests per minute with a limited inference budget.
Listings asking for "production experience with LLMs" are up 34% year-over-year. The specific asks include:
- Latency optimisation (18% of listings)
- Token cost management (12%)
- Caching and batching strategies (9%)
- Model fallback and failover design (7%)
These aren't skills you learn from API documentation. They come from building something real and dealing with the consequences.
2. Evaluation as Engineering Discipline
The most underrated AI-native skill is knowing whether your system is working.
- 52% of listings mention some form of evaluation
- But only 12% ask for automated evaluation pipelines
- The rest rely on manual review or don't specify
The difference between "I built a chatbot" and "I built a chatbot with 94% task completion rate, measured over 10,000 conversations with automated regression testing" is the difference between proficient and native.
3. Cost-Aware Architecture Design
This is the single biggest signal shift in 2026 job listings. "Cost-per-outcome" and "return on AI investment" are appearing in job descriptions — phrases that were unheard of in AI hiring even a year ago.
- 8% of listings explicitly mention cost optimisation (up from 2% in 2025)
- 15% mention "scalable architecture" with inference cost context
- Listings that mention cost keywords pay 18% higher on average
The era of "just use GPT-4 for everything" is over. AI-native engineers are the ones who know when to use a small model, when to cache, and when not to use AI at all.
How to Close the 47-Point Gap
If you're in the 70% and want to reach the 23%, here's the roadmap based on what employers are asking for:
Step 1: Ship something to production Not a demo. Not a Colab notebook. Something real that real users interact with. Even if it's a small tool for your team. The single biggest differentiator in job listings is production experience.
Step 2: Measure what you built Add evaluation to your project. Track latency, cost per request, success rate. Put the numbers in your resume. "Reduced API costs by 40% through prompt caching" is worth more than "Built a chatbot with LangChain."
Step 3: Optimise the economics Show that you understand the unit economics of AI. Identify where costs are going, propose a cheaper alternative, implement it, measure the impact. This is what employers mean by "cost-per-outcome thinking."
Step 4: Orchestrate, don't just prompt The highest-paying listings ask for multi-agent systems, orchestration, and tool-use. Being able to design a system where multiple AI capabilities work together reliably is a skill that commands a premium.
The Bottom Line
The 70/23 gap isn't a warning — it's a roadmap. The difference between AI-proficient and AI-native is exactly the set of skills that employers are willing to pay a premium for.
Every job listing on our board is a data point telling you what to learn next. The question is whether you're reading them.
Data source: 5,765 active job listings on SuperAIDevs as of July 2026. Salary data from 920 listings with disclosed compensation. Listing counts are live and updated daily.