Will AI Replace Me?

A fellow developer recently asked me:

I build Google Workspace Solutions, and I fear that my skills are about to be replaced by AI.

AI can do a lot of things. Some seem scary, and a few may ultimately reduce the workforce. But let’s examine what developers do. And by “developers”, I’m referring to people who use their brain power to craft solutions such as spreadsheets, workflows, and automations. These power users often use Google Apps Script, and many are domain experts in their specific areas of expertise. It is often the case that solution-builders come from backgrounds and education pathways other than computer science. This makes them valuable to an organization - they know the business and are technically competent.

Externalities

About 80% of the stuff we do to engineer a solution is unrelated to code or formulas. I refer to these tasks as externalities of the solution. Thinking, pondering, designing, speaking with stakeholders, and considering all facets of the business process - these are the things that are very difficult to codify into an AI prompt. To approach the depth and breadth required to provide a large language model with the specifics of a given solution requires a mega-prompt. Prompt windows are limited in size, and LLMs require very discrete explanations, even for the simplest of tasks.

The Meg

And if you try to encourage Bard to craft a solution without considering all these externalities, the results will be very poor. Your boss will not be impressed, and neither will your users.

Where AGI Shines

AI might make 20% of the work 50% more efficient in solution-building. These percentages may vary depending on each situation and your role. But overall, when it comes to code generation, AI is useful to a point. Where it stops being useful is applying its vision to practical implementation strategies. Humans have the edge in this regard.

AI may also play a key role in the other 80%. For example, I used Bard below to explain an apps script function. It was quick and mostly accurate. This makes me more efficient and affords me more time to focus on things AI can’t do well.

If all you care to know is the risk of AI replacing you, this is my guidance.

We should not fear being displaced by AI. We should fear being replaced by someone effective using AI to make 20% of the work 50% more efficient.

Practical Example

In my work, I have used Google Apps Script to control how an FAQ bot responds to questions framed in natural language by customers. One of the challenges is preventing jailbreaking of the AI model. It requires a deep understanding of several solution principles that are difficult for an LLM to understand without costly fine-tuning.

Jailbreaking is a process that uses a prompt injection to bypass safety and moderation features placed on LLMs by their creators. Jailbreaking usually refers to Chatbots which have successfully been prompt-injected and now are in a state where the user can ask any question they would like. This is a serious risk in AI solution development, and most projects get to production without jailbreak testing.

I use embedding vectors to thwart this risk to ensure the questions injected into my AI solution are relevant to the system's objectives. If a question doesn’t meet a relevance threshold defined by its vector score, it is politely ignored. Here’s an example of a recent jailbreak attempt on one of my systems.

FAQ System Jailbreak Attempt

Embeddings are extremely powerful because they can test query relevance to a given domain of information. But the design of that similarity testing process is complicated and requires a lot of abstract thought and planning - ergo, design.

LLMs know a lot of patterns, and they can generate new designs. However, they tend to fail because they don’t know all the nuances of the business domain or the users. Embodying this logic in a prompt is exceedingly difficult. Here’s a narrative of my solution - a single Google Apps Script function that processes every FAQ query.