StealthGPT vs Walter Writes

Generative AI Will Reshape Knowledge Work, Not Replace It

Some say generative AI will replace analysts, programmers, and writers. Others say generative AI is overhyped autocomplete. Both miss an important point. Generative AI will have a transformative effect on knowledge work, but it’s more useful to think of it as a powerful tool that requires judgment and expertise to get the most out of it.

Generative AI will make it possible to automate tasks that have been considered knowledge work for years. Workers will increasingly use generative AI to summarize documents, draft emails, and write boilerplate code and texts.

Generative AI has the potential to make workers more effective. MIT researchers found that those who were allowed to use ChatGPT completed writing tasks 40% faster than those that weren’t allowed to use it, and received higher quality ratings. GitHub reported that developers using its product, GitHub Copilot, complete coding tasks much faster than developers that don’t use Copilot.

As a result of generative AI, first drafts will be cheaper to create. The value of a document or code won’t be in writing it down, it’ll be in ensuring it’s accurate and applicable. Humans will be responsible for figuring out what needs to be written down, checking facts, and flagging any errors.

That’s because generative AI makes mistakes, misses context, and echoes user biases. Recently, a lawyer in the case of Mata v. Avianca included an AI-generated, but non-existent, case in her legal submission. Generative AI revealed her lack of judgment, she wasn’t replaced, but exposed. As generative AI floods the world with content, judgment and accountability will be more important.

Some say generative AI is so advanced that it can replace entry-level knowledge workers, threatening the pipelines that promote talented workers into leadership positions. For example, junior developers fix bugs, junior analysts draft memos, and learn the craft. Generative AI may be able to fix bugs and draft memos, but can’t promote talented workers. If companies use generative AI as a way to cut costs, they’ll struggle to find qualified leaders down the line.

Spreadsheets didn’t replace accountants. Yes, they removed the drudgery of calculating by hand, which meant accountants could focus on higher-level analysis. Generative AI will be a similar leap forward, expanding what’s possible for those that wield it.

Generative AI will transform knowledge work. Those that can frame problems and critically assess AI-generated information will be rewarded for their judgment and domain expertise. Organizations should revisit how they develop talent and structure roles to maximize generative AI’s capabilities. Generative AI is a powerful tool. But like any tool, its effect depends on who wields it.

Generative AI is going to completely alter (restructure) knowledge work. While it will make many aspects of our jobs easier (and potentially less time consuming), it will also create the opportunity for us to be judged for our decision making and judgment.

These are just some examples of things that AI does well:

generating boilerplate code drafting emails summarizing documents creating initial drafts of most types of written content

All of these tasks used to take up a significant amount of a worker's time. Research done at MIT showed that employees who use ChatGPT to assist with writing tasks finish their assignments about 40% faster and have higher quality ratings. Similar results have been seen from research done at GitHub regarding developers and the task completion times with and without the assistance of Copilot. The closer we get to a point where generating a first draft costs nothing, the less valuable the first draft becomes. What takes on greater importance is all the other steps involved in creating a final product. Who determines if there is enough justification to pursue a particular issue? Does the user recognize when a system is providing a false confidence in its ability to provide an answer?

One of the problems with generative AI is that it often generates information based upon the assumption that the user wants "right" answers. This means it sometimes provides false citations, misreads contextual clues provided in the question, and/or makes flattering assumptions based upon the input provided. In addition, a lawyer who provides a court citation produced by AI in an effort to support his argument, such as was demonstrated in the recent *Mata v. Avianca* court case, has not lost their job due to the introduction of AI into their profession. Rather, the lawyer failed to utilize their analytical skills when reviewing their potential argument before presenting it to a judge. Verifying accuracy and being accountable for one's actions remains the responsibility of humans. As the volume of generated material increases, so too does this responsibility.

There is a very real concern related to this trend, particularly concerning entry level professionals. Traditionally, entry-level positions within a field have acted as an educational experience for young professionals. Junior analysts learn by drafting memos and junior developers learn by identifying and correcting errors in programs. With AI absorbing much of this type of work, companies will need to identify new methods to train professionals or else they will lose the individuals who know how to supervise these systems. Companies that view AI as solely a method to cut costs may end up eliminating their own talent pipeline.

Another criticism is that improvements in capability are happening rapidly enough that even areas that require high levels of judgment will be able to be automated. This is certainly possible. However, historical trends suggest that while automation replaces many of the rote, repetitive tasks associated with a position, it rarely eliminates an entire occupation. Spreadsheets did not replace accountants; instead they reduced the number of hours spent performing arithmetic and increased what accountants needed to analyze.

As always, the best way to respond to innovation is not with either panic or complacency. Professionals should focus on developing their abilities in problem identification and analysis; critical evaluation; and area-specific expertise. Organizations should design new roles and training opportunities focused on human oversight of AI.