
**The concept**

Meta has a new feature for their messaging apps that embeds the personality of a celebrity. This is a small step, mind you. These are fake profiles of people who don’t exist. The faces are real but everything else is a kind of archetype-persona wrap.  For example, Kylie Jenner is the ‘big sis’ ‘keeping it real’ but also on the DL (fuck me). I literally don’t know what either of those things means, but I’m sure someone, deep in the neon-dark pits of Meta’s data team, does. Snoop Dog is Dungeon Master (more Dungeons and Dragons than BDSM). This shit is *****weird.***** It’s also undoubtedly backed by petabytes of data.

Even the Queen of Australian sports, Sam Kerr, has a persona. Meet  Sally. And she just, like, totally loves to surf one wave at a time. 

![Sally-aka-Sam-Kerr.png](/img/blog/Sally-aka-Sam-Kerr.png)

*Taking life one wave at a time…..?* 

The revenue stream of celebrity goes hand in hand with the platforms that promote them. To the celebrity, their oxygen is the social platforms that perpetuate their infamy, and therefore their earning power. What's interesting is that these celebs are potentially scorching their own earth. If you squint you can see Meta's longterm play. They're white-labelling social influencers and celebrity. We're getting cheap-knockoff versions of Snoop for now, but you blend that long enough and you'll get 'Taine', a completely falsified individual. We'll either not be able to tell the difference or not care. They're all just 'people' on our screens.  

Undoubtedly, we’re at a tipping-point. The technology isn’t the thing holding us back. We can replicate the look, the voice and, in some sense, now fake the personality. 

Fake people on the internet is nothing new. However, the amalgam of technology packages it differently. These [AI-driven people have certain lives they lead](https://about.fb.com/news/2023/09/social-profiles-for-metas-ai-characters/), pictures they take and places they go. One is a ‘proud-momma of two’, another is a man who gives out dating advice. The images are classic life-style ‘eat,love,laugh’ influencer shtick -  beach-wedding, aesthetically-pleasing food, Thai yoga retreat and.. yep, private plane. 

Meta says their goal is get everyone to create their own AI-bots. I feel that when they say ‘everyone’, they really mean influencers. Oh, and brands. 

All the motherfuckin’ brands. 

**The demand**  

So, what’s this got to do with Meta? Well, and I’m probably wrong, but I think a lot. Meta is in the business of supplying brands with access to people. They are the bridge-troll that takes brands money so they can cross into the chasm of people’s inner-thoughts and life. 

 The second and third order effects of having AI-lives creates other avenues. Threads, their Twitter (I’m not calling it X), competitor is undoubtedly very interested to see how these personalities can play a role in creating content and ‘engaging’ people.

**Where can it go wrong**

 ** Scenario A: The Bots go bad**  

![BotGoBad](/img/blog/OhNoTay.png)

It took just 48 hours for Microsoft’s Tay AI bot to go from Johnny-5 to robo-Nazi. The obvious danger is that the AI just starts posting wild, insane stuff. It’s almost so obvious as to not be an issue because Meta has very smart people who would see this coming a mileaway. Let’s be clear though. The danger isn’t the spreading of misinformation or just fringe ideologies, it’s the disconnect between the brand and the ai-nfluencer (does that work?). You can’t have Ford Motors pay millions to a robot that’s going to say Nazi’s are awesome. 

But it’s not the obvious stuff like racism or anti-semantic remarks I’d be concerned about. The problems are more nuanced than that. If Taylor Swift bot says she’s anti-abortion, is that bad? Is that good? The nuances here are too many. A brand is defined by what it isn’t as much as what it is. Can a bot exhibit a stance on anything? Second question - what does it mean if it does, anyway? Was Microsoft’s AI Tay racist - *****what***** is Tay? 

**Scenario B: The bots go off-brand**

The danger is that in order for the Meta AI’s to be effective, they’d need to adapt to current affairs and contemporary issues. Therein lies the danger. You can’t have Kylie Jenner reflecting on the issues in the Middle East. You can’t have Snoop Dogg aka Dungeon Master be pro-Trump. This is nuclear-levels of danger to the celebrity brands. A celebrity IS their brand. You can’t fuck with that, least you be cancelled. 

The door-swings both ways - if you’re going to leverage the likeness of Kylie Jenner and Mr Beast to create a connection to their AI counter-part, then you’re also going to be on the hook for when they behave inverse to their carefully brand-managed selves. 

**Scenario: C People demand real. Brands go real(er)** 

The other danger is normal, human people don’t want anything to do with AI personalities. And why wouldn’t we? Already these celebrities live lives that aren’t relatable. Private jets, exotic holidays, expensive clothes is what many aspire to replicate. Being known is the very definition of celebrity. Adding an artificial photo to the life of an artificial person with an artificial caption isn’t appealing. It’s poser behaviour. And lame. And.. *****dystopian*****. 

**The benefit** 

Everything Meta does openly is a validation test that effects billions of people. And that’s what we’re in now. This is a social company, testing technology, and hoping to use these AI personas as the vanguard for ai-fluencer brand integrations. 

And this is the big thing. If Meta is able to create artificial brand people, who engage and have discussions, that represent themselves in a one-to-one style relationship but at scale, then.. I don’t know what you have exactly. Falsehood, I suppose. 

Scrolling through some of these profiles, I can’t but feel that we’re better than this. I mean, the tech community as a whole. This can’t be the apex of what we’re able to achieve. It’s not, obviously, but some incredible minds contribute to this shit.



Celebrity-Fakes-As-A-Service

***Credit: MidJourney****
  
# Don't Sell a Sandwich, Solve a Problem

**Scenario A**

Imagine you want to sell sandwiches. You know a lot about ham, and so you create a ham sandwich. Of course, you need the right sticker for the wrapping so people know it's you. So you hire the best sandwich-logo packaging firm in the country to do your Ham logo. Ordering 50 kgs of ham and 100 loaves of bread, you bust your butt to make 500 sandwiches with the awesome logo sticker on the plastic wrapping. 

It’s a lot of money, but that’s okay. You’re going to sell a butt-load of ham sandwiches, right? Everything that’s perfect - the sandwich, the packaging, the website - all of it. Now it’s time to find hungry people who love ham-sandwiches. 

**Scenario B**

Imagine you noticed that there’s numerous hungry people. Intrigued, you ask them why they’re hungry and what they do to satisfy this pesky ‘hunger’ problem. You do this a few times and notice a startaling trend starting to emerge. Most people say they’re too busy to eat a big, complicated meal. Instead, they need something quick they can eat on the run. They’re also going ‘low-carb’ now. 

Thinking on this, you figure you can create a sandwich with carb-free bread, a wrap with lettuce and ham, or a protein smoothie. Unsure of the right option to build your food empire, you decide to get just enough ingredients for each idea and some newspaper to wrap the sandwiches in. The smoothie is in a Styrofoam paper cup. You head to the local market to test this idea and get feedback. 

The mistake in the scenario A is the mindset that you’re in the ham-sandwich business. By definition, the only problems you’re suitable to solve are the ones where people a) are hungry b) like ham-sandwiches c) prefer your ham sandwich over others.

 While this assumption may pay off, it’s investment and time heavy without any certainty that the product (the sandwich) will find it’s fit (customer). More importantly, you can’t adjust if you find ham sandwiches isn’t tickling anyone’s fancy. 

In scenario B, a series of bets is placed against a problem - hunger. So an exploration into how people solve the hunger problem is tested and experimented with against some potential viable options. The process, refining and adjusting, continues until you reach some kind of metric or market ‘fit’. 

*“Don’t find customers for your products, find products for your customers.” - Seth Godin*

![SandwichGodin](/img/blog/seth-godin-eating-sandwich.png)
*Credit: MidJourney*  

This process - the exploration and experimentation - isn’t about achieving perfection or even 100% certainty. It’s about making more informed and therefore better decisions. Whether it’s to continue experimenting, to move forward to scale up or to stop altogether.

Don't Sell a Sandwich, Solve a Problem


Our love affair with AI's large learning models, such as ChatGPT, is already making an impact to our every day lives. Sassy robot butlers seem just over the horizon. With AI, we aim to advance beyond what software currently does best - empowering us, reducing tedious tasks, and making us superhuman. It seems like every day we’re finding new ways to improve the user experience.   



We’re also urging caution. New technology brings new ways of solving old problems. The temptation is to focus on the AI part without seeing how it benefits the user. Worse still, is riding the wave and applying AI to mediocre ideas in order to standout. 

 

[We’ve](https://www.pcmag.com/how-to/how-to-disable-bixby-on-your-samsung-phone) [been](https://www.shavershop.com.au/oral-b/genius-ai-electric-toothbrush-with-3-replacement-heads--and-smart-travel-case-black-010597.html) [here](https://en.wikipedia.org/wiki/Long_Blockchain_Corp) [before](https://en.wikipedia.org/wiki/Pets.com). 

From the user's perspective, we believe there are many opportunities to create ideal user experiences with AI. However, the fundamentals of good software experiences have not changed: empower the user with the tasks they want to do and minimise the ones they either don't want or can't do by themselves.

## Good AI in action

While AI isn’t necessarily new, the most effective use is in how it lives under the hood. The AI is there as a considerate friend, a guide, a servant at your beck and call. It’s either helping you or moving things out of your way. 

- Composing an email in Gmail allows for it to do a sort of ‘super’ autocomplete, to finish emails off
- Notion AI tool allows for several options to help with writing or generating ideas.
- Adobe is making Photoshop be able to do more with their generative background fill
- Commonwealth Bank, as one example, use AI for pattern discovery and Fraud Detection
- Git Co-Pilot assists developers with code suggestions that’s contextually relevant.

*Notion’s AI toolkit makes suggestions for writing. However, I don’t know if it necessarily makes the user into a better writer. The jury is still out whether AI helps writing OR reduces the need to write clearly, but that’s a topic for another day.* 

## A few predictions : 

- One big thing we’re already seeing is the ability for unstructured data, like an email or conversation, to be converted to a structured format suitable for an API or some other piece of software. Scenarios where documents need to be read, interpreted and actioned will be ripe for further reducing friction.
- How we describe and label AI-fused actions may not necessarily need to invoke ‘AI’ in the label. I.e. “Remove background” is clearer than “AI Background remover”.
- Navigational tools, like search, can be better used to parse search queries. Instead of ‘Men’s Nikes’ a user could search using sentiment ‘Something sporty and fun’.
- Prompts for generating images, such as Mid-Journey or Dall-E , are still a pain. Further investment into a UI will be needed to move it from early adopters to the general public (though other platforms will most likely adopt it and apply their familiar UI to need the needs of the user. See: [Canva](https://www.canva.com/magic-write/))
- JasperAI, a writing tool for marketers, will be vulnerable to users going direct to ChatGPT. They will either pivot or die by the AI-sword.
- AI is very expensive, computationally. Pricing plans that target a per-use basis will become  more common as more products embed AI.
- Very long term - Our big bet is AI will be able to navigate software including using authentication to access bank accounts (scenario: ‘hey Siri, transfer $50 to my nephew for my birthday’). I can see Apple introducing Siri 2.0 that is far more capable and, importantly, more trusted to do riskier operations. Apple’s next Trillion will come from mining trust, not data.
- In the realms of AI-made art, taste and curation will become key. Will this be AI-lead? Potentially, but AI isn’t ‘smart’ - it operates on probability. And humans are notoriously hard to peg down when it comes to taste.
- Relationships & trust will still be hard-earned. You cannot outsource trust and connection to AI. That’s what alcohol and coffee is for.


Don’t go chasing (AI) Waterfalls


# Defining the problem: common issues


Conventional wisdom in the product space (be it digital or physical) is that you’re in the business of solving a problem for people. And that, in exchange for solving that problem people will pay you money.

  

The **other** conventional wisdom says that **not being sure of the problem you’re solving** is how most start-ups fail.

  

Products fail when they don’t meet product market fit. At the risk of sounding obvious - People aren’t going to buy things they neither need nor want.





Knowing this, why is it that so many products fail? There’s thousands of reasons, but let’s start at the very beginning: **defining the problem.**


  

### Common Issues

  

1. Not understanding the problem sufficiently

2. Not checking if it’s big or painful enough

 3. Fear of finding flaws with the opportunity



## 1. Not understanding the problem sufficiently

  

It’s not enough to see *a* problem. You have to become a master in what we call ‘the why’ of the problem. You’ve got to dig deeper.

  

But what if you’re trying to solve a problem in a domain you’ve got years of experience in? This is a common catalyst for a lot of products, where scratching your own itch has lead to a revelation that, hey, maybe others need this product too?

  

This is a great way to start. However, we advise caution in diving in before setting a direction on what problem you’re really solving. You have to step outside the comfort of what you believe to be true and validate it, but even more when you’re an expert due to your bias.

  

## 2. Not checking if it’s big or painful enough.

  

Some problems just aren’t that painful to fix or too narrow that it impacts too few. Not always a bad thing, but assuming everyone has the same caliber of problem can lead to poor results.

  

## 3. Fear of finding flaws with the opportunity



When you don’t challenge your idea, or at the assumption of the problem the idea fixes, you run an enormous risk of mishandling your product-market fit. You should challenge your idea and your understanding of it. Ask others if they are afflicted by the same challenges as you are. Ask what they do about it. Go looking for how people are solving the problem. 

  

## **How to de-risk defining your problem**

  

- Talk to people impacted in the ‘problem-space’ directly.

- Review any research or secondary research in the space you’re in.

- Talk to a subject matter expert (most experts' rates somewhere between $150 to $300 an hour) the ROI on a few hours with an SME on their area is well worth it.

- Checkout specific community or sub-groups (Reddit.com is a **gold mine** for what people are experiencing or thinking)

- Keep asking **why** something happens (if your answers become philosophical you may have gone too far)


> By putting an MVP out there and testing how customers respond, founders are supposed to avoid squandering time and money building and
> marketing a product that no one wants.*
> 
> Yet by neglecting to research customer needs  -before-  commencing their engineering efforts, entrepreneurs end up wasting valuable time
> and capital on MVPs that are likely to miss their mark..
>
>   [Harvard Business Review - Why Businesses Fail by *Tom Eisenmann*](https://hbr.org/2021/05/why-start-ups-fail)


Defining the problem: common issues

## Here's to the small ones, the private, the humble, the profitable

If you think of online companies you think Airbnb, Uber, Google and Facebook (or something like that). Maybe Netflix.

They're either public or VC'd up the wazoo. And very, very large.

But there's something you don't really hear or see much of - the smaller, private SaaS or product companies. These are often bootstrapped or started with a modest amount of capital. They're not massive, maybe 5 - 100k users and they're often niche and technically boring. We work with the big and small, but the small don't really get seen as much as maybe they should. 

If you're: 

- Lean with a team size of 20 or less
- The founder / co-founder sharing BD and sales duties vs with someone else handling the technical side
- Profitable
- Not afraid of growth, but not obsessed either
- Budget-conscious, but not afraid of risk
- Humble
- MRR in the thousands, not millions

Not all great businesses in this game need to be rocketships. The ones we love aren't. So here's to them. 


Here's to the small ones, the lucid, the humble, the profitable.


Email is boring (but it works)

Long after Facebook, WhatsApp, Uber, and AirBnb are gone, email will still be there. It's the OG of the internet. It's a cornerstone of how we connect. Email has attempted to be [fixed](https://en.wikipedia.org/wiki/Apache_Wave), [changed](https://hey.com/) or [replaced](https://www.slack.com). But, it's still there. 

But despite it's cockroach-like nature, email is easy to forget about. But you shouldn't.   Email is a relatively low-effort, automated and accessible way to help your users find their way back to your product. It's vital you can help users form habits in which your product makes up their day to day. Here's a few examples of how Monday.com, Xero, and Uber use email. 

## Example - The Welcome to..  - (on registration)

This is the initial email that gets sent after a user registers or signs on. Often there's a confirmation email that gets fired at the same time, or potentially, as a result of confirming the account. 

You want to make it friendly, make it clear what they can do and what the next steps for the user is. Chances are the person signing up is already in the platform, so it's more of 'read later' or 'here's what you can do now' sort of vibe. 

### Monday.com 
![img](/img/insights/monday.com-welcome.png)

### Xero
![img](/img/insights/xero-welcome.png)

### Uber
![img](/img/insights/uber-welcome.png)

---
## Example - The Follow-up
Following up, generally a day or two after the user has registered, is a great way to get the user to revisit your platform. Uber, in particular, is all about getting users to try it out. They realise for a (relatively) new service, getting a user to complete a trip increases the likelihood of using the service again. They rely on this so much that they give the first trip away for free. 

### Monday.com ( < 24 hours)
![Monday.com followup](/img/insights/monday.com-followup.png)

### Xero.com's Follow up ( < 24 hours)
![Xero followup](/img/insights/xero-followup.png)

### Uber's Follow up ( < 24 hours>)
![Uber followup](/img/insights/uber-followup.png)
---

Finally, the reminder or promotional email. This is something you want to do delicately. Don't spam. Always provide value in some way - even if it's not of monetary value, like a discount. For example, Xero, a tool for small businesses, don't offer cheaper subscriptions or deals - instead, they offer education and knowledge. Monday.com leverage your content and use it to tap you on the shoulder and say 'Hey, remember this?'. Uber offer regular discounts and excuses to open up the app. It's all about front of mind. 

## Example - The Reminder
![monday.com reminder](/img/insights/monday.com-reminder.png)

### Xero.com's The Reminder ( < 24 hours)
![Xero reminder](/img/insights/xero-promo.png)

### Uber's The Reminder ( < 24 hours>)
![Uber reminder](/img/insights/uber-promo.png)

Next time your reviewing your retention, consider how email can play a part in getting users to come back. It's cheap, relatively easy to implement with third-party platforms, and very low-hanging fruit to reaching users outside your platform. 

Email is how your users come back


## First, see through the lens of what the user needs
A lot of failures around shipping a new feature or even launching an entirely new product is the loss of focus in knowing what motivates the user. 
Knowing what motivates your users is vital in knowing how your product, what it does & doesn't do. 

## Start with asking 
This sounds obvious, but it's often easy to assume a lot about the people who you are building the product for, especially across big teams or for new members that join. User interviews are the best place to start in defining the motivators across your user types.

## What people say and what people do are two different things.
Ask someone to describe their dream house, they'll talk about Italian marble, the beautiful oak cabinets, the feature-lights and incredible mountain view. What they won't mention is that they have a door and plumbing with hot and cold.  These are the obvious 'must-have' things. They're vital. Without them, you have no users.
Additionally, some users will reject and loathe an update - Facebook, for example, made an enormous change to their UI in 2010. People vowed to leave, that it was the end of the social giant. The reality is, they kept using the product. What people say, and what they do, are often two very different things. 

## You don't need to satisfy all motivations.
Not every product needs to scratch every itch the user has. But, you should aim to meet the must-haves first.

Satisfy the motivation.





Why product-market is less product, more market.

Back in 2012, I co-founded a startup that focused on disrupting the film rental market. Think 'Uber' for high-end film gear.

I was co-founder and CTO. I worked like a dog for 18 months. 

It didn't work. 

It didn't work for a few reasons, and in retrospect, I could've saved about 20k and 18 month of my own time and resources, not to mention our investors money. If I invested more time in understanding the problem and the people that it impacted, I think I would've either concluded that it was a bad idea, or, I would've changed practically everything - the product, the user-acquisition strategy, the time invested, the stupid t-shirts (100% cotton though, so maybe not everything).

## Myth: "Once our users see it, THEN they'll get it." 

This is a common misconception and, in my experience, predictated on two things; a) laziness b) fear. 
Laziness, because, frankly, designing and building a product is way more fun then talking to people. And Fear, because if I ask if people really would want my product, they might say no. And, damn it, I've come too far to turn back now! 

Former me was more of an idiot that present me. I wasn't a 'dreamer', I was a fool. I pulled on every lever to overcome that nagging self-doubt about the product - "Steve Jobs said stay hungry, Stay Foolish!!", "I'm a visionary! I see what the world doesn't see!". Ugh, get a grip. 

What I lacked in market research, I made up for in delusion. 

## My time again 

Ok, so here's how I'd approach things today, me being wiser and all. 

First, I'd start with defining the problem / opportunity space.

For example,  *A camera guy owns a 100k camera. He rents himself and the camera out for 3 months out of the year as a contractor. For the remainder of the year, his gear is left dormant, depreciating in value.* 

Is it just one person that works this way or is it more common? Why does it work like this? Does he lend his gear to colleagues or friends? 

I want ask at least as many people as I can these questions. I can then start grouping them and finding patterns where I can apply potential solutions.

I think if I spent three months researching instead of delving straight into build, I'd either realise that my "idea" wasn't going to work -at all- OR that the solution would be executed differently. I'd have a better idea of the problem, how our potential users thought about it, and what we could do to ensure our approach would work. 

If you've got an idea or an inkling of something, take a breath. Do your research, or get us to help you. You'll save time, money, and be far more likely to have product-market fit. 













Hatch Head

Product