The News: Google Cloud’s Andi Gutmans, GM & VP of Engineering, Google Cloud Databases, published an intriguing blog post on June 29 outlining a potential emerging trend: the possibility that the global shortage of data science expertise might not slow the adoption of AI down anymore. “I believe we are entering a ‘post-training era’ in which application developers will drive the bulk of the innovation in applying generative AI to solve business problems,” said Gutmans in the blog post.
He said enterprises will depend less on data science and MLOps. “I believe the availability of LLMs is democratizing access to AI for the broader community of developers, who will not need to become experts in deep learning, but rather expand their skills to integrate LLMs into enterprise application architectures. I draw the parallel to compilers, which were built by few but leveraged for innovation by many,” said Gutmans.
He points out that in the U.S. alone, there are more than 2 million software developers but only around 150,000 data scientists. He calls the shift the rise of generative engineering, or GenEng. He argues these developers are in a better position to best leverage and integrate generative AI technologies into applications.
Gutmans’ role and profile of a generative engineer looks like this: enterprises cannot tolerate the shortcomings of LLM-based chatbots. The value for enterprises is when they can combine generative AI with their proprietary data. GenEng developers will enhance their skills with prompt engineering, embeddings for proximity searches, and leverage frameworks that help them build LLM apps.
Read the full blog post on the Google Cloud website.
Google Cloud Engineering Exec: Welcome to Generative Engineering
Analyst Take: Gutmans’ premise is bold and framed in a way that many have previously alluded to – that generative AI democratizes AI uses. Will GenEng emerge? What are the potential impacts?
GenEng Could Unleash AI Power
There is no question that market adoption of AI applications and use cases has been slowed by limited experienced AI resources. The number of data scientists is small, and most come from academic research backgrounds, which means they do not necessarily have enterprise mindsets (solving business problems) or the business experience to understand the enterprise point of view. Further, there is a lack of data science engineers (read, not PhDs) as well. If GenEng expands AI expertise within enterprises, there is little doubt it will lead to accelerated AI market adoption.
GenEng Could Unleash Some AI Use Cases, but We Aren’t Sure Which Ones
Gutmans’ premise was focused on LLM-powered AI. We are in the very early stages of generative AI and there is not a lot of solid evidence of which, or how many, generative AI use cases will resonate within the enterprise. We will have to wait and see, there are no guarantees at this point.
GenEng Doesn’t Address Non-Generative AI
Contrary to current popular sentiment, LLMs do not key all AI applications. Many of the most proven AI applications and use cases require data science and other expertise (conversational designers, linguists, mathematicians) to design and operate. The next 2-3 years will be a sorting out period when we will understand just how much of AI workloads will require expertise other than GenEng software developers.
GenEng Will Place Even Greater Pressure on Enterprises to Install Solid Data Management and Governance
Gutmans spoke of software developers, but not about data management experts and engineers. He did say enterprises will gain the most benefit from generative AI by leveraging their private/proprietary data, which means the management and governance of an enterprise’s data will be critical to GenEng and generative AI success.
Disclosure: The Futurum Group is a research and advisory firm that engages or has engaged in research, analysis, and advisory services with many technology companies, including those mentioned in this article. The author does not hold any equity positions with any company mentioned in this article.
Analysis and opinions expressed herein are specific to the analyst individually and data and other information that might have been provided for validation, not those of The Futurum Group as a whole.
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Author Information
Mark comes to The Futurum Group from Omdia’s Artificial Intelligence practice, where his focus was on natural language and AI use cases.
Previously, Mark worked as a consultant and analyst providing custom and syndicated qualitative market analysis with an emphasis on mobile technology and identifying trends and opportunities for companies like Syniverse and ABI Research. He has been cited by international media outlets including CNBC, The Wall Street Journal, Bloomberg Businessweek, and CNET. Based in Tampa, Florida, Mark is a veteran market research analyst with 25 years of experience interpreting technology business and holds a Bachelor of Science from the University of Florida.
