I love the idea of going from "AI generated customer avatar" to "simulated real people". It would help add depth to the customer avatars, and lead to better product design.

I tried creating a society around products that I sell, but it looks like the "real-world data" is pulled from LinkedIn? I'm not necessarily targeting business people.

Hadn't seen that paper, thanks for sharing it. This is the one I see cited most often that's got some similar vibes: https://arxiv.org/abs/2411.10109

I’ve seen a a couple of start ups pitching similar ideas lately - platforms that use AI personas or agents to simulate focus groups, either for testing products or collecting user insights. I can see the appeal in scaling audience feedback, reducing costs, reaching demographics that are traditionally hard to access.

That said, this is one of the areas of AI that gives me the most concern. I work at a company building AI tools for creative professionals, so I'm regularly exposed to the ethical and sustainability concerns in this space. But with AI personas specifically, there is something a little more troubling to me.

One recent pitch really stuck with me, in this case, the startup was proposing to use AI personas for focus groups on products and casually mentioned local government consultation. That's where I think this starts to veer into troubling territory. The idea of a local council using synthetic personas instead of talking directly to residents about policy decisions is alarming. It may be faster, cheaper, or even easier to implement but it fundamentally misunderstands what real feedback looks like.

LLMs don't live in communities. They don't vote, experience public services, or have lived context. No matter how well calibrated or "representative" the personas are claimed to be, they are ultimately a reflection of training data and assumptions - not the messy, multimodal, contradictory, emotional reality of human beings. And yet, decisions based on these synthetic signals could end up shaping products, experiences, or even policies that affect real people.

We're entering an era where human behaviour is being abstracted and compressed into models, and then treated as if it's a reliable proxy for actual human insight. That's a level of abstraction I'm deeply uncomfortable with and it's not a signal I think I would ever trust, regardless of how well it's marketed.

Would be curious to know what your approach is to convince others that may also be skeptical or not want to see this kind of tech being abused for the reasons listed above?

If it isn't based on ACTUAL buyers who have ACTUAL input, what is it really doing? Great job at creating something, but at the same time, it feels kind of unnecessary.

You're essentially telling people what you THINK they'll want.

This seems like it could be useful for product discovery (“what are these people complaining about?”), content marketing (“how will my twitter followers react to this blog post?”), and other… reactionary… activities. But what about GTM and lead-gen? Can you ask it “who has job title of CISO, within two degrees of connection to me, working at a company with at least 500 employees that is SOC2 certified?”

I think you need to focus on a target buyer and make sure you nail their use case, or you risk wasting time on a really cool product that kinda/sorta does everything.

What’s your differentiator? Are you in the business of data gathering and curation? Or do you enhance some existing targeting data with “talk to my audience?” These are two distinct product development paths… either you invest in sophisticated data scraping, or you focus on “bring your own audience.” Most companies already have this sort of data on their customers and prospects – how can you meet them where they are?

The other problem is garbage in, garbage out. This product is only as useful as the data you can gather (or that your customer brings to you). A list of emails and names isn’t much on its own. You need the data generated by those people. Maybe you need to partner with data brokers to enrich audience data with social media profiles. Or maybe you leave it up to your customer – let them upload all their support tickets (ZenDesk) and sales calls (Gong) to your software so that they can “talk to their customers.” (Hint: maybe you should partner with Gong, and similar companies who already have this data, to provide this feature to their customers. White-labeling this product might be your fastest path to market.)

But more existentially… is “opinions” the most important aspect of your customer that you want to simulate? And if so… why do you need a _network_ of customers for this? It seems like two disconnected ideas. An “audience” might be a group of people that you only know to be associated because of their shared subscription to your product. Or they all follow someone on Twitter. Or they’ve all written an HN comment with “trivial” in its text. Does the _network_ aspect actually matter, at all?

And if the network aspect is important… why? Is it because you want to discover a new audience? In that case, are you focusing on the right value by simulating the current audience? Or should you be focused more on features for expanding/enriching/discovering “similar” profiles?

I think you’ve got the basis of something really cool here, but you need to figure out your identity and core competency, or you risk doing a bunch of marginally useful stuff kinda well.

Did you see the recent HN launch of Sumble? I see some overlap and similarities between your products, and I’d suggest reaching out to them in case you can work together…

edit: Just saw you went to Cambridge… I live here, if you’re ever around and want to grab coffee. I’m “Miles Richardson” on LinkedIn (and uh… in life). Feel free to message me… I spent five years on a startup that never hit product/market fit, so I’m always happy to point out the hazards…

I think you guys might be onto something but I'm still skeptic as to whether you are the most accurate (on whatever metric). It's not surprising that you beat a survey of experts, or straight out of the box commercial LLMs.

I'm more interested in seeing how your model performs against purpose specific models that are currently industry standard. Unless you're making the claim that you're the first service to predict content engagement?