Vibe Coding Surges Amid AI Talent Shortages, Reveals Georgian’s Latest AI Report

Comprehensive Global AI Benchmark

Georgian Partners, in collaboration with NewtonX and a global consortium of 11 partners, has published the AI, Applied Benchmark Report, offering an extensive view of AI's impact on B2B software and enterprise companies worldwide. This second wave study surveyed 612 executives equally divided between R&D and go-to-market leaders, spanning 10 countries and 15 industries, representing companies with revenues from $5 million to over $200 million annually.

The involvement of respected institutions such as the Alberta Machine Intelligence Institute and Startup Nation Central contributed to a diverse and international dataset, enabling the report to provide robust benchmarks across sectors.

AI as a Strategic Priority

Artificial intelligence has transitioned from an optional tool to a core strategic priority, with 83% of B2B and enterprise companies ranking AI among their top five priorities. The main drivers for AI adoption include enhancing internal productivity, gaining competitive advantage, and improving cost efficiency and revenue growth. Notably, competitive differentiation has surpassed cost savings and revenue growth as a key motivator, highlighting AI’s role as a market leadership weapon.

The Rise of Vibe Coding

A remarkable highlight is the emergence of Vibe Coding — automated code generation and debugging powered by AI models. It stands as the third most common R&D use case in production, adopted by 37% of companies, with 40% piloting it. This trend addresses the acute shortage of AI technical talent, cited by 45% of R&D leaders as the top barrier to scaling AI, even above model development costs.

Vibe Coding enables smaller engineering teams to accelerate delivery, debug more efficiently, and produce higher quality, documented code with less manual effort, reducing workload across QA, infrastructure, and deployment.

Productivity Gains and Challenges

AI adoption in development pipelines has yielded significant benefits: 70% of R&D respondents report faster development velocity, 63% note improved code quality and documentation, and over half have increased deployment frequency. However, metrics related to system stability such as mean time to restore and change failure rate remain challenging, indicating human involvement is still essential for resilience.

Infrastructure and Data Expansion

The report highlights major infrastructure investments powering AI capabilities:

• 53% of companies have integrated LLM observability platforms
• 51% use data orchestration tools like Dagster and Airflow
• Vector databases, cron jobs, and durable workflow engines support scalability and reliability

Data sourcing has expanded with 94% of companies using owned data and 80% utilizing public data. Additionally, over half employ synthetic data and about a quarter use dark data, marking a shift toward diverse data inputs.

Diversified Large Language Model Usage

OpenAI leads with 85% production usage, but adoption of other models grows rapidly: Google Gemini (41%), Anthropic Claude (31%), Meta’s Llama 3 (28%), and reasoning models such as OpenAI’s o1-mini (35%) and DeepSeek (18%). This diversification reflects a trend towards multi-model AI architectures tailored to specific use cases.

Uneven AI Maturity Progress

According to Georgian’s Crawl, Walk, Run AI maturity model, more companies are advancing from beginner to intermediate maturity. However, the top-tier “Runner” group holds steady at 11%, highlighting challenges in scaling AI initiatives linked directly to revenue or cost outcomes.

ROI Measurement Challenges

More than half of R&D teams struggle to connect AI projects to measurable KPIs. Only 25% link AI to new revenue and 24% report positive effects on customer acquisition costs. Despite this, over 50% observe improvements in customer satisfaction and long-term value, though financial justification remains unclear, especially for mid-maturity companies.

Improving Cost Management

Talent shortage remains the primary obstacle, but cost management shows progress:

• A 9-point increase in stable or reduced data storage costs
• Declining expenses in software maintenance, labor, and operations
• Less dependency on strict project cost-cutting

Furthermore, 68% of companies leverage third-party AI solutions to manage complexity and cost, especially as AI integrates into go-to-market software and internal platforms.

Future Outlook

The report underscores a transformative phase where AI is integral to software creation, deployment, and maintenance. Vibe Coding emerges not just as a productivity enhancer but as a foundational technology enabling companies to overcome talent shortages, accelerate time-to-market, and improve code quality without proportionally increasing headcount.

Companies advancing AI maturity tend to embed AI deeply into their workflows, operationalizing automation to amplify developer capabilities rather than replace them. Strategic investment in Vibe Coding and supporting infrastructure will define the next wave of enterprise innovation.