I co-mentor with a large online school for an AI accelerator course. We get about 600 participants each month, paying about $500-600 for a 14 day course. I only co-mentor for 2 days - the days we teach fundamentals of software development and then show how to code with Replit, Bolt, Lovable, Emergent, etc.
One of the most common questions is "can I build on xyz and shift to abc because I do not want to pay?" And another is "can I host the code myself?"
Customers know they do not need to stay with any of these code builders. The platforms know it too. They spend tons of $ to get customers, who use the credits and then leave.
Each of the players are just eating each others customers and showing growth. Perplexity has acquired who knows how many customers in India through their 12 month free Pro offer via Airtel (a telecom provider): https://www.perplexity.ai/help-center/en/articles/11842322-p...
>paying about $500-600 for a 14 day course. I only co-mentor for 2 days - the days we teach fundamentals of software development and then show how to code with Replit, Bolt, Lovable, Emergent, etc.
Wow.
Do these tools require training to use? Are they not really designed such that a 5 year old can use it? "build me a thing that does Y" style.
The course is broader and it is for people who are not from a technical background at all. Think about a course to introduce the idea of working with LLMs for day to day work. What are agents, then focusing on coding agents, and so on.
What’s “fraudulent” about lying in a marketing blog post? Private companies can, in most situations, say whatever they want about their business. They are almost certainly lying (as are most of these companies) by categorising any revenue as “recurring” but it wouldn’t be fraudulent to do that. Their investors are surely aware of the real financials and have no problem with this public posturing because it serves only to increase the value of their investment.
ARR drives startup valuations. For example people might argue if you have $100M ARR the startup is worth 10x, so $1B.
Now that’s a pretty good incentive to drive up ARR, no matter how.
Welcome to the world of creative accounting!
One-time projects? Count as ARR! 3 months contract with no extension? Count as ARR! You gave a discount of 50% for the first year? Make sure to count the full price as ARR!
And now Perplexity has mailed all of those "free" users to add a "card" (wont charge now) to continue with its free pro offer. Apart from airtel perplexity ran a lot of college based programs where students were basically referring each other for money.
They also have 12 months free when subscribing through Paypal. There's almost zero chance I remain a customer after those 12 months are over, since I find ChatGPT way more valuable.
Setting aside the veracity of the $100m claim: Manus is a spin out of a spin out, building on ~10 years of previous work. The “fastest company to 100m arr” has become a meme so it doesn’t deserve much scrutiny but it seems a somewhat tenuous claim in this case.
The thing about ARR numbers is that in most cases they didn't really recur, especially for an 8-month-old company. Also, I don't really care about these numbers anymore unless they share how much they spent on marketing.
IMHO at least some spending on AI are FOMO driven. Companies think how they can adopt AI faster than competitors in a fear being left behind and loose the race. They don’t yet know if ROI of AI adoption is above zero. Even if adopting AI is the only right decision some of AI users may go out of business having overspend on AI and either way AI customer base will decrease.
Looking at that list, the top three companies are essentially about building apps without writing code. The next one is about helping developers write code. Perplexity is the only real outlier, and even that not by much. I am by no means an AI pessimist, but I can't help think where are all the awesome companies in other sectors that this technology is supposed to unlock.
I understand that many industries will take years to adopt. Fine. But about sub-sectors in tech—gaming, design, data? What is happening beyond "make software development easier"? Is it because ChatGPT like apps are enough for most people?
There's a loop between adoption of a technology and adaptation of a technology. For some domains that loop is fast, the adaptations prove to be easy and the feedback from adopters is easy to get. For other domains it's slow, especially towards the end of the process of going from something interesting to something useful. A good example of a slow loop is self driving, it's hard to get feedback about self driving in safety critical real world situations... another example is medicine.
The other issue is that the value is more or less all in the LLMs (at the minute). For example, I built a data engineering toolkit using LLMs, it created synthetic data from examples, it created ingestion pipelines given different source filed and a target, it created data test rules. I liked my little toolkit and some people were impressed, but the value was all in the models that underpinned it. The crust of clever bits that added value was thin, very thin. Ok, we used the llms to generate some python that then created the synthetic data and testing rules to reduce costs, we had three or four "agents" that worked together to create the pipelines. We decorated target code with open provenance code to create provanance... But just by saying these things or letting you use the toolkit and you seeing what it made - that's enough for any half competent person to relicate it (with AI assistance) in an afternoon, or maybe a couple of afternoons. Maybe.
So, to create a viable company is going to take significant effort (if you can think of a value add) because the value add still has to be real.
If these customers have set an automated system to pay you $200 every single minute, that’s correct. If they haven’t and it was just a one off sale, you are missing the “recurring” part in ARR.
Readit News
One of the most common questions is "can I build on xyz and shift to abc because I do not want to pay?" And another is "can I host the code myself?"
Customers know they do not need to stay with any of these code builders. The platforms know it too. They spend tons of $ to get customers, who use the credits and then leave.
Manus is running $5000 credit for 2000 people. A simple search shows so many offers: https://x.com/search?q=ManusAI%20credits&src=typed_query
Each of the players are just eating each others customers and showing growth. Perplexity has acquired who knows how many customers in India through their 12 month free Pro offer via Airtel (a telecom provider): https://www.perplexity.ai/help-center/en/articles/11842322-p...
Wow.
Do these tools require training to use? Are they not really designed such that a 5 year old can use it? "build me a thing that does Y" style.
How would this work though? They give out free "credits" and then claim usage of those as ARR? That would be outright fraudulent, no?
Who is paying for those "Manus Credits"?
Now that’s a pretty good incentive to drive up ARR, no matter how.
Welcome to the world of creative accounting!
One-time projects? Count as ARR! 3 months contract with no extension? Count as ARR! You gave a discount of 50% for the first year? Make sure to count the full price as ARR!
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https://en.wikipedia.org/wiki/Manus_(AI_agent)
I understand that many industries will take years to adopt. Fine. But about sub-sectors in tech—gaming, design, data? What is happening beyond "make software development easier"? Is it because ChatGPT like apps are enough for most people?
The other issue is that the value is more or less all in the LLMs (at the minute). For example, I built a data engineering toolkit using LLMs, it created synthetic data from examples, it created ingestion pipelines given different source filed and a target, it created data test rules. I liked my little toolkit and some people were impressed, but the value was all in the models that underpinned it. The crust of clever bits that added value was thin, very thin. Ok, we used the llms to generate some python that then created the synthetic data and testing rules to reduce costs, we had three or four "agents" that worked together to create the pipelines. We decorated target code with open provenance code to create provanance... But just by saying these things or letting you use the toolkit and you seeing what it made - that's enough for any half competent person to relicate it (with AI assistance) in an afternoon, or maybe a couple of afternoons. Maybe.
So, to create a viable company is going to take significant effort (if you can think of a value add) because the value add still has to be real.
Just a faster horse.
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