LLMs finally gave someone I know the confidence to up her business rates. Professional services, nothing to do with software dev (yes LLMs are not just for devs). It suggested she revamp her entire pricing structure. She thought her clients would walk, but she did it and nobody flinched. Big revenue boost.
She also uses it daily for all kinds of things. For example recording/transcribing/summarizing meetings, creating plans, writing emails, reviewing employee performance, and a bunch of other stuff. If it went away she would be devastated.
The best clear example I've seen of LLMs making money is a company that now generates custom email text instead of using standard email templates. They increased engagement by some meaningful metric like +15% which translates into hundreds of millions of dollars in revenue.
I know the original email was something like "Alert: you have a new thing: X Thing"
And the new emails are a prompt something like "we know all of this about the user and all of this about the X thing, write an email alerting them to the new thing with these particular goals".
I really don't know much about it so I'm being pretty vague and generic.
I’ve seen LLMs make money most reliably when they’re embedded into an existing
workflow rather than sold as “AI” itself.
One example: a small team built an internal tool for SEO/content teams that
generates structured content briefs and refresh plans from search data.
The value wasn’t faster writing, but fewer failed pages. Clients were willing
to pay because it reduced wasted content spend and made outcomes more
predictable. It ended up as a SaaS with recurring subscriptions rather than
a usage-based novelty.
Another case was customer support tooling for a B2B product. LLMs were used to
summarize long ticket histories, surface likely causes, and draft replies,
but humans stayed in the loop. The business impact showed up as lower support
headcount growth while revenue increased, which leadership cared about more
than raw “productivity.”
Across cases, the pattern seems to be:
- tie the model to a clear economic decision
- charge for risk reduction or revenue lift, not for text generation
- keep humans in the loop where mistakes are costly
Pure “LLM apps” struggled more unless they were tightly scoped or had strong
distribution already.
We've seen some tangible benefits from integrating LLMs into our workflow, particularly in automating customer support and content generation. By leveraging language models, we’ve been able to free up our team’s time and focus on more strategic tasks, which has led to improved efficiency.
We ran into this ourselves when we needed to manage a growing volume of inquiries without scaling our support staff. By using LLMs to generate responses and categorize requests, we not only enhanced our response times but also maintained a level of quality that our users appreciated.
We ended up building Wyshbone to handle sales lead generation and outreach timing, integrating seamlessly with our CRM. This has helped us identify potential leads more effectively and optimize our follow-up strategies.
Yea, it's not not necessarily that the LLM itself is better at customer support than a human.
But i've found that it's just good enough that support and teams can handle addressing the systematic problems while the LLM deals with operational overhead.
A bit the same way Egyptian history experts make money.
By making LLMs for people who want to make money with LLMs.
For me though I see ChatGPT take all the hype now. I'm seeing people get more and more bored with that and in quest of a step up or sideways from that.
That goes pretty slow outside of developers people are still trying to come to grips with OpenAI.
All earlier adopters have been builders interested in the technology for tech sake. The real consumers are veeery slow to ramp up.
LLMs finally gave someone I know the confidence to up her business rates. Professional services, nothing to do with software dev (yes LLMs are not just for devs). It suggested she revamp her entire pricing structure. She thought her clients would walk, but she did it and nobody flinched. Big revenue boost.
She also uses it daily for all kinds of things. For example recording/transcribing/summarizing meetings, creating plans, writing emails, reviewing employee performance, and a bunch of other stuff. If it went away she would be devastated.
The best clear example I've seen of LLMs making money is a company that now generates custom email text instead of using standard email templates. They increased engagement by some meaningful metric like +15% which translates into hundreds of millions of dollars in revenue.
Great example. Do you know what sorts of input they're using to drive this custom messaging?
Not really.
I know the original email was something like "Alert: you have a new thing: X Thing"
And the new emails are a prompt something like "we know all of this about the user and all of this about the X thing, write an email alerting them to the new thing with these particular goals".
I really don't know much about it so I'm being pretty vague and generic.
I wonder how the new ai gmail features will affect email marketing
I’ve seen LLMs make money most reliably when they’re embedded into an existing workflow rather than sold as “AI” itself.
One example: a small team built an internal tool for SEO/content teams that generates structured content briefs and refresh plans from search data. The value wasn’t faster writing, but fewer failed pages. Clients were willing to pay because it reduced wasted content spend and made outcomes more predictable. It ended up as a SaaS with recurring subscriptions rather than a usage-based novelty.
Another case was customer support tooling for a B2B product. LLMs were used to summarize long ticket histories, surface likely causes, and draft replies, but humans stayed in the loop. The business impact showed up as lower support headcount growth while revenue increased, which leadership cared about more than raw “productivity.”
Across cases, the pattern seems to be: - tie the model to a clear economic decision - charge for risk reduction or revenue lift, not for text generation - keep humans in the loop where mistakes are costly
Pure “LLM apps” struggled more unless they were tightly scoped or had strong distribution already.
We've seen some tangible benefits from integrating LLMs into our workflow, particularly in automating customer support and content generation. By leveraging language models, we’ve been able to free up our team’s time and focus on more strategic tasks, which has led to improved efficiency.
We ran into this ourselves when we needed to manage a growing volume of inquiries without scaling our support staff. By using LLMs to generate responses and categorize requests, we not only enhanced our response times but also maintained a level of quality that our users appreciated.
We ended up building Wyshbone to handle sales lead generation and outreach timing, integrating seamlessly with our CRM. This has helped us identify potential leads more effectively and optimize our follow-up strategies.
Yea, it's not not necessarily that the LLM itself is better at customer support than a human.
But i've found that it's just good enough that support and teams can handle addressing the systematic problems while the LLM deals with operational overhead.
So the money from LLMs is in selling them to people who aren't selling enough?
A bit the same way Egyptian history experts make money.
By making LLMs for people who want to make money with LLMs.
For me though I see ChatGPT take all the hype now. I'm seeing people get more and more bored with that and in quest of a step up or sideways from that.
That goes pretty slow outside of developers people are still trying to come to grips with OpenAI.
All earlier adopters have been builders interested in the technology for tech sake. The real consumers are veeery slow to ramp up.
I work in cloud consulting. We make money when companies come to us to do implementations using LLMs…