My first reaction to the live catch wasn’t “That rocket is impressive”
At 11 p.m. on Wednesday, I was still in Hangzhou revising a quote for a freelance client when I came across SpaceX’s Starship launch stream. On screen, the booster returned to the launch tower, ready to be caught. My first thought was: “How does this relate to the AI services we’re building?” I’ve been stuck with the same disconnect: aerospace news always feels far away from a small company. Then I realized it eventually reaches our books through compute prices, model services, and tooling costs.
What this maneuver is actually trying to solve
Simply put, SpaceX wants the launch tower to “catch” a returning booster during flight instead of bringing it back for recovery at sea. The goal is to shorten the recovery process, reduce reuse costs, and make future launch schedules more flexible. SpaceX isn’t alone in exploring reusable launch systems: Blue Origin has worked on reusable rockets for years, while Rocket Lab keeps investing in small rockets and rapid launches.
I need to draw the boundaries clearly: this was Flight 15, and the public headline used the word “catch.” Whether it achieved a complete capture and what happened to the hardware afterward shouldn’t be concluded from a headline or one clip. I made that mistake too: after seeing a short video saying “it caught it,” I forwarded it to a client and said costs would drop immediately. The more realistic connection for us isn’t paying less for a tool tomorrow. As major infrastructure continues getting cheaper, AI compute and cloud services may become more affordable, lowering the barrier for indie founders building intelligent products, automated customer support, and content-generation tools.
What I can replicate today isn’t catching a rocket, but testing a new tool with low risk
If I wanted to run a useful experiment inspired by this, the replication cost would be: $0; 30 minutes of my time; a technical barrier of being able to open my laptop, create an account, and test one workflow. The first step: open the AI tool I already use, create a blank project, paste in the client reply I’ve repeated most often over the past week, and let it sort and classify the replies before I check each one.
This won’t prove how large language models will evolve or replace professional judgment. It can help me estimate how much time I could save each week if service prices continue falling. SpaceX’s experiment runs on a timescale of years; our small test can produce a result today. There’s no urgency to try it right now either. Saving the idea and waiting for a price drop that actually hits our budget before acting is perfectly reasonable.
Here’s how I’d choose based on my current stage
If I’m just starting out: I’d first use AI for one repetitive task, such as organizing requirements or client questions, without rushing to build a complex system. I’d cap both the money and time at 30 minutes; saving effort on even one task would make it worthwhile.
If I already have one or two clients: I’d test one low-risk part of the workflow. I’d keep the original delivery process, have AI generate a second version, and compare the time, cost, and quality. I wouldn’t paste client data directly into a public tool; I’d remove names, contact details, and commercial information first.
If I’m scaling: I’d watch the three factors that will really change our books: the unit price of model calls, cloud-service pricing, and whether automation is stable. Then I’d decide whether to buy more seats or invest in custom services. I wouldn’t chase a new tool because of a spaceflight broadcast; I’d expand my investment only once the cost curve fits my budget.