State of AI

OpenAI is leading model adoption, but competition is catching up

While OpenAI remains the primary choice with 88% adoption, developers maintain relationships with multiple providers — two on average. As providers race to complete, developer loyalty is tested with 65% switching providers within six months.

Start small, stay flexible

Architect your systems for provider mobility and model portability. Small teams are leading AI implementation, proving you don't need massive resources to succeed. The winning strategy is to build a multi-provider foundation that allows you to quickly pivot to the best solutions for your use cases as new features and capabilities emerge.

We are past the phase of adding AI for hype

As AI adoption matures, there's a shift from basic AI integrations to real value creation. A majority of teams are using vector databases, building strong foundations for more sophisticated applications. They're prioritizing core features over chatbots, and making AI an essential part of their product's DNA.

Focus on how to solve fundamental product challenges with AI

While the market is young with plenty of untapped opportunities, the era of quick wins is over. Users expect more from AI now. Ask deeper questions about how AI can enhance every aspect of user experience, and make it central to your product development, not just an add-on.

Today's teams build high-demand AI systems through smart technical choices, not big budgets

AI teams build powerful systems on lean budgets, spending under $1,000/month. They skip costly training by using RAG, smart data sourcing, and cloud platforms to ship fast, reliable models without heavy infrastructure.

Leverage existing resources to ship quickly and efficiently

The winning pattern combines cloud platforms for deployment speed, existing models for reliability, and modern frameworks for rapid development—letting teams ship AI without infrastructure overhead.

Tools over hiring

Many dedicate meaningful tech budget (over 15%) to AI, but aren't planning to grow dedicated AI teams. They're finding ways to build sophisticated AI features by empowering existing teams with better tools and clear objectives.

AI development is entering a pragmatic phase

Building successful AI features doesn't necessarily require a specialized department. Focus on high-impact use cases while keeping team structure lean. The opportunity lies in finding where AI adds real value and executing efficiently with available resources.

The AI market has found its sweet spot between hype and real impact

Teams think current AI tools are overhyped, but they expect AI to significantly impact their industry within 12 months. They're excited about the future but grounded in the present.

Build with what works now, but design for what's coming

Big changes are ahead, even if we're not there yet. Be aware of the current limitations and challenges, while remaining optimistic about the transformative potential of AI.

The AI landscape is evolving fast

There is strong belief in AI’s potential. While OpenAI is the leading provider, developers are actively testing alternatives and focusing on real-world value. Priorities are shifting toward customer-facing features, but challenges like model accuracy and cost remain key concerns. Success requires careful evaluation, strategic planning, and a flexible implementation to adapt to changes. We look forward to continued innovations this year and beyond.