The AI Tools That Actually Survived 2024
The AI Tools That Actually Survived 2024
Last year I wrote about AI startups that failed in 2024. The natural follow-up: what actually made it, and why.
The answer is not what most people expected. It had nothing to do with model quality or feature count.
The survivors
Cursor
Cursor became the clearest success story in developer tooling. It raised at a $2.5B valuation in 2024 and grew almost entirely through word of mouth.
The reason is simple: it did not build a chatbot next to your editor. It built AI into your editor. Tab completions, multi-file edits, codebase-aware context — it went deep into the workflow instead of sitting beside it. Once you use it for two weeks, switching back feels like going from autocomplete to typing everything by hand.
That switching cost is the whole business.
Perplexity
Perplexity found the one thing Google was slow to defend: the direct answer. Not ten blue links. Not ads. Just an answer with citations.
Whether their citations are always accurate is a separate debate. The product found a real wedge and users stuck with it. By the end of 2024 they had over 10 million daily active users and were raising at a $9B valuation.
ElevenLabs
Voice quality is the moat here. ElevenLabs' voice cloning is genuinely hard to replicate. It is not just "text to speech" — the emotional range and naturalness is in a different tier. They signed deals with major media companies and expanded into dubbing and sound effects.
The lesson: if you can build something that is noticeably better at a specific, hard thing, that gap protects you.
Midjourney
Midjourney never built an app. Still no website you can log into. Everything happens through Discord.
That decision, which sounds like a limitation, is actually why their community is so strong. People are not passive users — they are participants in shared channels, they see each other's work, they learn from each other. The retention Midjourney gets from that is something most SaaS products would pay heavily for.
GitHub Copilot
Copilot had the unfair advantage of already living where developers work. GitHub had 100 million users before a single line of Copilot code was written.
They did not need to convince developers to try a new tool. They just needed developers to press Tab.
The pattern
Every product that survived had at least one of three things:
Distribution — they already had users before adding AI (Copilot, Notion AI).
Technical depth — the AI output was genuinely hard to replicate at that quality level (ElevenLabs, Midjourney).
Workflow lock-in — removing the product from your day would break your process, not just remove a convenience (Cursor).
The products that failed had none of these. They called an API, wrapped it in a UI, and called it a product. When OpenAI or Anthropic shipped the same capability natively, there was nothing left.
What to watch in 2025
The same pattern is playing out in vertical AI. Products that embed into specific professional workflows — legal, medical, finance — have a shot. The ones that are just ChatGPT with a custom system prompt do not.
The interesting question is not "what AI tool should I build?" It is "what existing workflow is painful enough that people will change their habits if you fix it?"
That is still the right question to ask.