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?

I recently authored a paper on a related topic that might be of interest, particularly concerning your challenges with accuracy and long-term simulation stability. Our work, "SALM: A Multi-Agent Framework for Language Model-Driven Social Network Simulation," introduces a novel framework for integrating language models into social network simulations.

I specifically focused on achieving long-term temporal stability in multi-agent scenarios.[2] A couple of our key contributions could be relevant to your work:

I developed a hierarchical prompting architecture that enables stable simulations beyond 4,000 timesteps while significantly reducing token usage.[3]

To address memory growth and maintain agent consistency, I implemented an attention-based memory system that achieves high cache hit rates with sub-linear memory growth.

I also established formal bounds on personality stability, which could be a useful concept for ensuring the accuracy and predictability of your AI personas over time.

Our validation against SNAP ego networks demonstrated the capability of our framework to model long-term social phenomena with empirically validated behavioral fidelity.[3]

It seems like there's a lot of synergy between our research and your product. I'd be very interested to hear your thoughts on our paper and explore if any of our findings could be beneficial for the continued development of Artificial Societies.

Here's the link to my paper: https://arxiv.org/abs/2505.09081

In this case, it's a technology that is hoping to significantly enhance the ability of people with the money to sell whatever they like to targets they have modelled.

Leaving aside the stalky model bit, the thing that should give pause is where is the bulk of that money going to come from?

It's probably (perhaps not with this product but say with the Grok version) inevitable, but is giving more help to oil, junk food, tobacco, dodgy politicians, and the odd millionaire who fancies themselves the next Il Duce what we want?

As a serial wantrepreneur I know what the letters SEO and A/B testing are but little else about this side of business. As a total novice, the ability to run a bunch of experiments quickly and cheaply, even if there isn't a 1:1 mapping to the real world, seems like a huge win.

To be honest I think I feel this way about most things ai/llm right now. I am ok taking the train to the stadium and walking half a mile over taking an uber to the front door and waiting in traffic.

I'm a Director of Market Research, so I did a test:

- One email headline test for a service I'm considering developing.

  -- got a score of 88 - "Exceptional".

  -- got a score of 81 - "Very high"

Feel free to DM me - I really like what you're doing and would be happy to discuss.

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Chain series:

p.diff10 = p.mySeries.subtract(p.ma).mul(10)

Render series in the chart: show(p.mySeries, p.ma) // Show selected series showAll() // Show all series in p