Artificial intelligence is advancing faster than many industries can comfortably handle. While the spotlight is usually on exciting breakthroughs like smart chatbots, instant image creation, and automated tasks, there’s a less visible but much bigger challenge underneath. As more companies start using AI, they’re discovering that the toughest part isn’t always building the model – it’s building the systems that help those models run smoothly, cheaply, and at scale.
Recent industry studies reflect this shift. A 2024 White House AI Adoption report found that almost 60% of companies experimenting with AI said infrastructure management, switching between models and operational complexities were bigger obstacles than improving model accuracy.
The rise of AI middleware
This has brought new attention to what experts describe as the middleware layer of the AI stack. It is the part that connects models to real products and ensures they work consistently in production. Without this layer, prototypes often remain stuck because costs rise unpredictably or systems fail during scaling.
In this transition, engineers who specialise in interoperability, reliability and open infrastructure have become increasingly important. Their work is less visible than model development but often more decisive for real-world deployment. One such engineer is Mihir Ahuja, whose work reflects this shift toward practical and sustainable system design. “Without it, companies often struggle to move beyond prototypes because scaling becomes too costly or unreliable,” Mihir said.
Mihir claims to have started his career designing high-reliability data systems for large enterprises. That experience taught him how even small design decisions can make or break systems handling billions of requests. Later, while working as an early engineer at a Y Combinator–backed startup, he saw firsthand that many of the hardest AI challenges are operational, not mathematical.
Building tools for real-world problems
Mihir further claims that this understanding eventually led him to create a series of open-source tools. One of his better-known projects, Vectorwrap, claims to solve the growing issue of fragmented vector databases.
He believes that developers often struggle when moving between systems like MySQL HeatWave and PostgreSQL pgvector because each requires its own format and code changes. Vectorwrap provides a consistent interface, making those transitions smoother.
He also claims to contribute to broader open-source ecosystems, including Mozilla AI’s AnyLLM initiative. His work aligns with a growing industry belief that open tools are key to avoiding vendor lock-in and making AI infrastructure more accessible.
As this evolution continues, the engineers who work quietly behind the scenes – ensuring reliability, flexibility, and strong infrastructure – may soon become just as influential as those creating the visible AI models.
