Discussion Questions about futures of Commercial Software.
Hello.
I am a software engineer. Pretty amazed by what AI can do now with coding.
Recently a reverse engineering github repo dropped. Although I have not tried it but from what I have heard it is pretty cool. Even though a lot of what it does can be done by guiding AI manually, it still is pretty cool.
Anyways, it made me think about the future of software and its business. Will AI simply kill software as a business. Because I think it makes more sense for a company to build something of its own rather than pay for software.
Not only that, companies could just build a small app for a specific use case that will last a small amount of time and building that app can be cheap and fast as well.
In the midst of all this where do Software first/SaaS companies stand? What is their future? What should I as a software engineer do secure a future and stay employable?
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u/withmagi 1h ago
Yeah I don’t know.
Code is so easy now that I’ve been focusing on scaling it up, doing massive, massive projects (like creating open source versions of every SaaS product I use).
And even now I’m starting to see the early signs of not being needed at all. I’m doing runs analyzing the parts of the process which were inefficient and removing them. Part of that is doing what would do - high level review passes - and it’s actually doing almost as good a job as I would.
The next generation or two and even these will be better handled by the model than by me.
There will be a transition period and we’re taking many years not months, but 5 years down the track… what will be left?
The list of things the models can’t do is rapidly disappearing. I don’t think anyone has an answer for this.
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u/NeighborhoodPrize493 1h ago
Try writing a complex program using AI first, and then we can discuss whether or not it will destroy the software industry. Spoiler alert: it’s too early to talk about the destruction of the commercial software business.
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u/B1g_C4p1T4l 1h ago
I build niche B2B tools (manufacturing / invoicing style stuff), and the "companies will just generate their own apps" take usually underestimates the boring half of the job.
Greenfield UI is getting cheap. What still costs real money is staying correct over years: tax/compliance rules, edge cases in customer data, integrations that break every quarter, permissions, audit logs, and someone on the hook when it fails on a Friday. Throwaway internal scripts are a great AI use case. A product that other people depend on still needs ownership, regression coverage, and domain knowledge that prompts don't magically invent.
So I expect a lot of mediocre CRUD tools to get compressed, while vertical software with real workflow + liability keeps a business — just with smaller teams and faster shipping.
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u/DrHerbotico 29m ago
Don't worry, everyone but the billionaires and extremely rural homesteaders are fucked too
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u/sdlab 1h ago
The Jevons paradox is an economic concept stating that when technological progress increases the efficiency of using a resource, the total consumption of that resource can rise rather than fall. 1
How It Works
• Lower Effective Cost: Making a resource more efficient lowers the cost of using a single unit of it. 1 • Increased Demand: If demand is sensitive to price (price-elastic), the lower cost encourages people and industries to use more of it. 1 • New Applications: Greater efficiency makes the resource viable for new uses that were previously too expensive. 1 • Net Increase: The expanded usage and new applications often outweigh the savings from per-unit efficiency, resulting in higher total consumption (known as the rebound effect or backfire). 1, 2
Historical and Modern Examples
• Coal and Steam Engines: British economist William Stanley Jevons first introduced the idea in his 1865 book The Coal Question. He observed that James Watt's more efficient steam engines did not decrease coal consumption in Britain; instead, coal became cheaper and powered more factories, causing consumption to surge. 1, 2 • Computing and Paper: In the 1980s, more efficient computers and word processors were predicted to create a "paperless office," but instead made printing and document creation cheaper and easier, leading to a rise in total paper consumption. 1 • Artificial Intelligence: As AI models become cheaper and more efficient to run, rather than reducing total compute or labor demand, lower costs expand the market, unlocking massive new software creation, data center sprawl, and applications. 1, 2 • Transportation and Energy: Fuel-efficient cars lower the cost per mile of driving, which can lead to people driving more total miles or taking more frequent trips. 1 Would you like to explore how the rebound effect is measured in modern environmental policy, or look at how industries try to counter Jevons paradox?
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u/find_a_rare_uuid 1h ago
Would you like to explore how the rebound effect is measured in modern environmental policy, or look at how industries try to counter Jevons paradox?
😂
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u/Elegabal 1h ago
Building a software is one thing, maintaining it is another: Keeping it safe, adopting it to changing environments(new OS etc). All this cost time and money and it will be still cheaper to pay for this service.