Most startups, and companies in general, need more engineers, particularly in teams other than product development. This is especially true in the age of vibe coding, where each engineer, including junior engineers, can get exponentially more done.
Despite all the talk about cross functionality, innovation, and efficiency, most startups have a misguided notion that engineers belong in the product team, working almost exclusively on the products they sell to customers. Integral to the theory of value for technology companies is the idea that engineering effort, delivering operational efficiency and revenue-driving innovation, is more valuable than the labour invested. But many startups seem to miss this lesson when operating their own companies: everything inside the company gets solved with process, headcount, and meetings.
Customer support teams expand because nobody automated workflows properly. Revops teams emerge because systems do not integrate cleanly. Finance teams spend huge amounts of time reconciling data across fragmented tools. Sales teams manually research leads. Across all of these functions, LLMs can automate a lot of human labour. But the reality is that there are no great turnkey solutions for these problems. Partly because the applied-AI product space is immature, and partly because there is a lot of diversity between companies, making one-size-fits-all solutions difficult or impossible to build. This is why Palantir, Google, OpenAI, Anthropic, and most other ascendant AI companies employ forward-deployed engineers to rollout and customise their products for new customers. These companies probably won’t give a forward-deployed engineer to your fledgling startup, so you need to hire some of your own.
A skilled engineer can absolutely automate the vast majority of your support tickets, finance and legal tasks, and sales prospecting. Inference, while expensive, is already cheaper than human labour, so this should be excellent for your gross margins, CAC, and G&A spend.
If your team is big enough to have well-defined departments, but you don’t have a few engineers scattered throughout these teams, you’re probably under investing in AI deployment. If you’re building an AI-enabled product, this should be especially embarrassing. You should hire some more engineers.
Is Australia destined to become a training ground for world-class founders and engineers, only to see them move to the US as their careers take off? This is not our destiny, but we need smarter, industry-friendly policy to build an alternative path, where startups start, thrive, and therefore stay in Australia. Let’s be the beneficiary of brain drain, not the victim.
Your software product today is a tool, and it might already be possible for you or a competitor to build an AI-first product that does the work for your customers without any ongoing effort on their part. If not, it’ll probably be possible soon. Either way, a level of disruption that was not possible a few years ago is now possible in almost every software category.
If you are the CEO or product leader currently stuck in a failing all-in-one strategy: pivot. The good thing about having a broad product is that you should have plenty of insights into what areas have the most opportunity for disruption and innovation. Find an area to double down, and a pathway towards deprecating some of the bloat. You’ll create a much more successful product that way.
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