The Surprising Benefit Of Gen AI Technology

A surprising aspect as we examine generative AI (gen AI) is its impact on the IT community. The belief going into this a year ago was that gen AI would kick off the next boom in data science. It seems to be the opposite. It seems to democratize many of the data science and data management functions.

For example, companies were putting significant efforts into data labeling. But gen AI can label the data very easily. Perhaps the most significant aspect of this democratization is that once the LLM is deployed over data, the average worker or executive is able to interrogate the data and perform analysis that hitherto was the providence of data scientists.

The fact that the LLM can integrate disparate data from many sources and use data that is often incomplete or inconsistent to uncover patterns and insights while putting this power into the hands of the average knowledge worker essentially democratizes the data science role.

It is not that we will no longer have the need for data scientists but rather that we will not need nearly as many.

As this democratization of data science increases, it pushes the data science function out into the business. Even now, at the relative infancy of gen AI, we can already see the need for large data science teams being reduced.

Furthermore, the hottest skill set out of university used to guarantee multiple job offers, but we now see recent graduates struggling to find positions. This portends a significant shift in the need for these hard-to-acquire skills and likely a significant drop in the average compensation for this role. For third-party consultant companies that specialize in this, it also signals slowing demand and the need to diversify quickly.

On the bright side, gen AI creates the whole new job category of prompt engineering. Although this new role is still nascent, it seems to draw on similar skills as data science and likely provides a career path for those with data science training and inclinations.

Like all technologies that came before it, gen AI disrupts existing roles and careers while creating new roles and careers. The prompt engineering role looks to be one such example.

Gen AI seems to have an analogous, but not quite so profound, effect on coding development. There is a raft of specialized gen AI tools, such as GitHub that write and/or greatly assist in the creation of code. As these tools mature and the programing and engineering community adopt them, we can expect a somewhat similar effect as we now observe in data science.

The gen AI tools look like they will greatly enhance the productivity of the programming and engineering teams, ranging from writing code, testing code, and debugging code far quicker. However, just like in data science, while one set of activities is automated and others dramatically made easier, the need for higher-level skills in architecture and design will remain and likely increase.

At this time, there is a significant controversy being debated as to whether companies will need fewer engineers as gen AI matures and is adopted. This argument holds that with the greatly enhanced productivity, fewer engineers will be needed, and we will have a surplus of this skilled labor pool. This, in turn, will have the result of dropping wages, creating fewer jobs, and lowering the need for significant numbers of engineers to find alternative careers. If this scenario unfolds, it will most intensely affect third-party service providers and likely cause significant disruption in countries such as India, where large populations of these engineers exist to satisfy the existing demand.

However, the alternative scenario argues that as the cost of engineering drops, the demand for programming and engineering will more than make up for the improvement in productivity. Under this scenario, we are likely to see an increase in the need for engineering along with rising wages as the workers capture some gains of the productivity for themselves.

I personally favor this alternative scenario and point to the history of information technology as the likely guide. Every time innovation happened in information technology, and the price of technology or the cost to implement and maintain it dropped, the demand for more technology quickly overwhelmed the reduced unit cost, resulting in an expansion of the market and rising wages.

We need look no further back than the move to cloud to observe this phenomenon. Cloud greatly reduced the unit cost of data processing while simplifying many administrative functions. However, this did not result in lower IT expenditure, as demand quickly increased and the cost dropped, resulting in overall more IT expense.

Under the likely scenario in which technology is made more accessible and cheaper to create, deploy, and maintain, we are likely to see companies redouble their investments in technology and further expand the need for programming and engineering talent. It also appears likely that this talent will require more analytical and integrative skills, requiring an upgrading of skills and likely resulting in rising wages.

The impact on the consulting, Systems Integration, and outsourcing firms may be more nuanced. Clearly, this will create disruption to their existing models and pricing structures. The price they can charge for building and maintaining existing estates will by necessity likely drop. Managed services will need to be reestablished, and it is likely the relationships will need to be restructured.

This could well lead to companies consolidating their workloads to fewer service providers. It could also encourage companies with existing large estates that have been outsourced to insource some or all of these functions, as they build and expand their Global in-house Centers, (sometimes known as captives) in low-cost destinations such as India.

However, a likely increase in demand for information technology is likely to more than offset these headwinds. Just like for individuals, it will disrupt the existing status quo, challenging existing business models, but opening up new and potentially more lucrative new services and business models.