GenAI is unleashing a Cambrian Explosion of new technologies that may be unparalleled from previous tech eras: More than 360 GenAI-centric startups were created since Dec 2022. Yet whether it was the internet in 1999, cloud’s emergence in 2006, mobile computing in 2007, or the AI boom in the late 80’s, all of these tech eras followed a similar pattern. Each began with a big bang proliferation of new, fresh and quickly evolving programming languages, tooling, standards, software and hardware. Massive communities of builders rapidly experimented and dissected, tried new approaches and threw others away, building out a massive marketplace of ideas and implementations based on what was possible.
GenAI is still experimental because it should be. OpenAI launched ChatGPT barely 18 months ago. The pace of adoption is also faster than that from previous eras: 1 out of 3 companies are already using GenAI in at least one business function. Yet what’s different with GenAI now versus previous eras is the role of the developer as part of this experiment phase.
GenAI Visionaries are Technical by Necessity
Many innovations are vision-driven. Apple founder Steve Jobs had a clear top-down vision and commanded distributed teams within Apple to make it happen. This way of innovating continues with Tim Cook, who in Apple’s recent earnings call teased up its own GenAI efforts for later this year.
Yet innovation can also drive vision when developers are involved. LangChain began in 2022 as an open-source project by Harrison Chase while he was CEO of Robust Intelligence. The project underwent rapid change from its inception as hundreds of developers contributed to LangChain’s improvements. From it emerged LangChain Expression Language, an easy declarative way to streamline text processing and interaction with LLMs. LangChain was officially incorporated as a company in Jan 2023. Today, it empowers data engineers with a comprehensive set of tools for using LLMs in different applications. LangChain’s vision – to simplify the creation and production of LLM applications – came out of these innovations. Developers continue to experiment and feed more changes into the system.
Many horizontal technologies often grow this way. Their power comes by putting them in the hands of those who can explore and discover their possibilities. Yet GenAI stands out because it is both deeply technical and rapidly evolving. Builders must be deeply technical by necessity to unlock their potential. We can dream about GenAI’s possibilities, but making them happen requires an understanding of what’s actually possible and why.
GenAI Game Changers: Retrieval Augmented Generation and Open Source Models
The second reason why GenAI innovation is now being driven by developers is because of the emergence of retrieval augmented generation (RAG) and of open source models.
Large language models (LLMs) are nearly useless for most enterprise use cases without RAG. RAG is a process that retrieves data from external sources of knowledge to improve the quality of responses. This technique makes the LLMs that power ChatGPT, Google Gemini and other chatbots more accurate, reliable and up-to-date for tasks involving an organization’s own data. RAG has become the killer enterprise use case for GenAI because it gives LLMs a pragmatic way to access enterprise data. That’s huge.
RAG achieves something even bigger. It enables developers to treat LLMs as one (or more commonly more than one) part in a larger GenAI pipeline that builders can talk to and wire together. GenAI technology may be new, but its components-based approach is something developers intuitively understand more than anyone.
The rise of open source GenAI models is similarly empowering developers. They further invite the possibility of breaking up GenAI problems into component parts for better and cheaper problem-solving, where each model can play a specific role inside larger sets of activities. Developers, as just one example, are already using one model to help write code, and another model to help write tests for training GenAI systems. And they’re only just getting started.
Developers Number in the Millions – and are Growing
The final reason lies in the sheer number of developers. AI’s early drivers were enterprise researchers and data scientists, who number in the thousands. Developers however number in the millions – 28.7 million of them in fact, and growing by millions each year. It’s no coincidence that more top-performing CEOs have engineering degrees than MBAs.
That’s not to deny the importance of automation. Tools like GitHub Copilot and Amazon CodeWhisperer are already increasing, as are the growing wave of software development automation startups such as Laredo and Refact. Developers themselves acknowledge the changes occurring in their profession: 82% of them in a recent HackerRank poll believe that AI will redefine the future of coding and software development. It’s worth noting that in the end, the ones doing the redefining will be the developers.
Developers are vital barometers for GenAI’s technical viability and value within their organizations. GenAI is also expensive now and will be for a while – but its costs are already starting to drop. For example, the rise of smaller models specialized for specific tasks and domains is beginning to lower barriers to access for countless organizations seeking to tap its potential. The result is creating more developers, not less. The US Bureau of Labor agrees: It predicts that developer-driven jobs will grow by 25% through 2032, much faster than the average for all other occupations.
Developers are essential builders and important partners in the GenAI journey. They drive GenAI’s advances, create new solutions and sit at the forefront of its responsible and ethical development. The opportunity is enormous. CIOs should take note!
