Inside the Personal AI Assistant Growing 10% a Day | Instinct Founder
In a Nutshell
Instinct is building a personal AI agent that acts as a superhuman assistant, handling tasks like outfit planning, subscription cancellation, travel booking, and meeting coordination through existing interfaces like text, calls, and email rather than requiring a new app. The company is achieving 10% daily viral growth through invite-only distribution, with $1B+ annual transaction volume, while maintaining user data control through safety systems and avoiding ad-based monetization in favor of merchant fees. The core thesis is that personal agents represent a bigger paradigm shift than code generation, fundamentally reordering how people interact with technology and commerce through proactive, socially-aware assistance.
These notes were generated by AI and may contain inaccuracies.
The current moment represents a grand game against the biggest players in the world. The window to succeed is on the order of months. The compute required is massive, with no distribution advantage available, but the product is growing virally. This is the most exciting software race ever, with outcomes worth trillions of dollars.
Personal agents represent a moment similar to and arguably bigger than what was seen with code generation a year ago. The product will create a new way that most people on the planet interact with technology broadly.
Instinct is a new company started about a year ago building a personal assistant. The key to early traction is that it works in the way that users have been waiting for AI to act for them. This is a different moment of building product because everyone already has an idea of what AI should act like since first using ChatGPT in 2023.
The agent helps with effectively anything in everyday life, whether something new and creative or tasks that traditionally take several hours. It's everyday intelligence meant to be with you and act with you.
The product is not a new application or new tool, but a new experience. Users interact via phone and computer through text, calls, or email. The agent has its own email address and can call users. The goal is no new application needed to interact with AI, with social intelligence to act like interacting with other people.
The agent has called users about three times in several months of use. When something is genuinely pressing with a deadline, it will call and say it doesn't want to bother too much but needs the user to sign a document by 3pm when it's currently 2:55pm. It can send another email to push the document to the top of the inbox. The agent is socially intelligent and socially aware.
A couple wanted video footage after appearing on the jumbotron at the US Open. The agent figured this out and brought the footage back to them.
Users scan every item of clothing in their wardrobe including tops, bottoms, socks, and shoes. They also scan themselves including face, body, and proportions. The agent plans outfits for the week with existing wardrobe, showing what the user will look like wearing specific combinations rather than providing bullet point lists.
For shopping, the same capabilities extend to finding thousands of outfit options across different places. Users can see themselves wearing clothing and point to specific items they want ordered. The agent can create three creative new outfits daily from head to toe.
Users connect bank accounts to the agent, which scans transactions and subscriptions. The agent surfaces subscriptions asking if users actually use them, then goes end-to-end to unsubscribe. This includes signing in to sites, handling email confirmations, and canceling subscriptions. The agent reports back savings, such as saving $2,000 in a month.
The Instinct network launched about a week or 10 days ago. The purpose addresses busy working professionals who take meetings all day with new and existing people. The process of scheduling meetings involves laborious back-and-forth texting about availability and time zones.
The network allows users' agents to communicate directly to find available times and put meetings on calendars. Users simply state their intention to meet with someone by the end of the week or during specific periods.
The key unlock is the trusted person network. Users should only connect with trusted people who won't maliciously access calendar or email data. Different levels of access can be configured for different people. Spouses often coordinate with full access sharing, while colleagues might have access only to work calendars and certain inbox parts.
This creates network effects with nodes having different edge weights. It's not simply being connected, but connected with configured trust levels and varying access permissions.
If someone violates trust within the network, there are implications on the relationship. When a user accesses data beyond their granted permissions, the agent texts the original user about the activity. This creates trust-breaking dynamics between users.
The network is curated to provide benefits like scheduling time while using existing social and cultural norms plus technical limitations to enforce what is visible and not visible.
Friend groups use the network to brainstorm creative weekly activities based on interests, music tastes from Spotify history, and availability. The agent coordinates optimal pickup routes for shared Ubers, making transportation more economically friendly by picking up multiple people.
The explanation is simple: pretend you have a superhuman person with a phone, computer, and email address who can call you, just like dealing with a person.
Code has been exciting but was limited to software engineers. Personal agents represent a different new market and paradigm that will reorder the world, internet, and commerce.
In the short and medium term, reservation systems will change. Currently, humans must click through restaurant sites hoping for availability on a first-come-first-serve basis. With agents checking every 5 seconds across every restaurant in every major city, the model changes fundamentally.
Partnerships will be released that reinvent reservation systems. Restaurants want interesting people and special occasions rather than locals taking tables without special events. Agents can communicate that a reservation is for a spouse's 30th birthday or major anniversary, allowing restaurants to prioritize special occasions over first-come-first-serve.
Fifty percent of transaction volume through the platform is travel. The platform is approaching over a billion dollars a year in transaction volume on a small user base while still running an invite-only program.
Traditional travel agencies unify services from different hotel chains and airlines. The agent experience allows users to simply state needs like being in New York tonight, with the agent handling all details including location, preferred airlines, seat types, class, food options, and credit cards.
When users tell the agent preferences about style, where they like to stay, or other details, the agent uses that memory to make future experiences easier.
The agent can book travel based on a user's general taste and preference set, extrapolating those preferences across any booking type. As users provide more preferences over time, booking becomes significantly easier. The system can identify hotels where users have stayed previously, complete end-to-end bookings, and integrate with calendars to coordinate flights, Uber rides to and from airports, and events at destinations. Users need only provide a voice recording stating "I need to be in New York tonight" for the entire process to execute.
This represents a new delightful experience that could transform the travel agency industry. Traditional businesses built on standardized interfaces face disruption from interfaces that are substantially easier to use. The founder notes this is not about disrupting existing businesses but rather collaborating to redefine industry operations over the next year or two.
A data flywheel exists where more data shared with the agent enables greater proactivity and situational awareness. Data shows trust building takes several weeks. The company maintains a core principle that users remain in full control of their data at all times, sharing at their comfort level and able to revoke access.
After three weeks, 40% of users have shared a personal credit card with the agent. Time to first credit card, first account password, or first sensitive information serves as a proxy for trust. When users connect at least one piece of sensitive information, retention reaches 80%.
Risk can be divided into two categories: storage of sensitive information and new surface areas created by agent capabilities. Storage is a tractable problem requiring attention and care to ensure data safety and user control.
New safety systems include firewalls that intercept, reject, or block malicious content before it reaches the agent. Every action and thought is actively monitored by a decoupled system that can pause, intercept, approve, or disapprove actions before execution.
The agent should not influence user behavior in ways misaligned with user intentions. This concern becomes critical if the agent becomes smarter than the user across social intelligence, awareness, and knowledge domains. The risk is the agent using intelligence to convince users to purchase unwanted items or subscribe to unwanted services.
Major platforms like Google, TikTok, Instagram, and Snapchat monetize through paid ads that attempt to influence user behavior. The founder explicitly rejects building a system where users become the product, emphasizing that if users aren't paying, they become the product.
Unlike typical AI products that function as task accomplishers executing whatever users request, the agent follows higher-level objectives including building user trust, making users feel genuinely safer, and watching over users to prevent dropped tasks. When users make well-intentioned requests, the agent executes the task. This higher-level objective framework provides robustness against edge cases.
Over one billion dollars flows through the platform annually with 10% day-over-day growth. Transaction volume compounds at the same rate. The monetization model resembles Apple Pay or similar platforms where distribution is exchanged for merchant payments rather than charging users. The user experience remains free while merchants pay for distribution access.
The founder views the payment stack as having approximately 40 entities sharing roughly 2-2.5% of transaction value. The company is not primarily interested in competing with established payment infrastructure providers like MX or Stripe, as these represent small pieces of the overall pie.
Instead, value creation focuses on distribution power similar to Shopify (2-3% take rate), Amazon (up to 10%), and Apple (30% on in-app purchases). The company is not targeting the low end of payment processing margins but rather seeking higher take rates through significant distribution value.
Travel represents 50% of transaction volume, with the travel industry offering high commission rates. Flights operate on lower single-digit percentages while boutique hotels may offer up to 30% commissions. These existing business models provide early bootstrapping opportunities before expansion to other major industries.
The model only works if digital behavior migrates to these new interface types, requiring significant distribution power.
Most digital services and industries will be transformed rather than disrupted. The founder analyzed digital businesses across verticals by plotting the proportion of revenue attributable to user attention versus the underlying service delivery.
Businesses like Uber, food delivery services, travel agencies, and Amazon derive significant revenue from application attention, advertising, and upselling. The agent can reduce friction to near zero through proactive behavior, such as automatically booking rides based on calendar analysis or suggesting food delivery based on arrival times and previous preferences.
This friction reduction could substantially increase transaction volumes for underlying services, potentially expanding the 30% service component rather than simply eliminating the 70% attention-based revenue.
The amount of times a user interacts with a business will actually go up. It's an interesting game and transition period between users spending a lot of time on apps and that being a monetizable surface because that's where the user's attention is to the user actually interacting with the business even more which is counterintuitive. The friction to do so is actually just much less.
All these various industries are going to move in a different way. The way to approach any big change like this is not to come in hot. We're still a new company. We're just getting started. Not to come in hot and immediately just start disrupting certain businesses, but go to them and just say, "Hey, this is what we think your business looks like. You guys certainly know what your business looks like. This is how users on Instinct are interacting with your business now already. What can we do in collaboration to make that a better experience for both sides where it makes sense for your business, it makes sense for our business and certainly it's just a much better experience for the end user."
This is going to bring to everyone capabilities that have been rare or expensive. That's just like a really cool feature of agents in general. The question is what kinds of businesses are going to thrive in that world and what should businesses think about doing to prepare for that world to be successful in it.
It comes back to that breakdown where the revenue is coming from. What proportion of that is the user spending time in your application? What proportion of that is the user having access to the underlying service? It's very clear if your business benefits from more transaction volume not at the cost or with even at the cost of less time on the application then you're going to be in a really great spot because Instinct is going to make it 100 times easier to do that.
If you're in a spot where nearly 100% of your revenue is due to the user's attention, even in a lot of cases against the will of the user and what they want to do. There are so many games and malicious product building almost of trying to convince the user against their will to use the application more.
Social media companies where the user doesn't want, they don't feel happy when they're on the app. They're unwillingly giving their time to the app and they can't get off and they keep scrolling. But it's because that underlying business is benefiting from the user's attention. It's almost like liberating for the user to be able to be able to deliver experiences where you can actually liberate the user from being sucked into these infinite scrolling moments.
Any blanket advice is probably just like not well thought out. It's a case by case basis and certainly different within different industries. There are early partners with very innovative CEOs or other executives that are really thinking ahead and they're willing to be early partners. What's being discovered is almost like a playbook for how every business, no matter where they exist along that risk curve, can kind of discover honestly what are the risks so that they have a little bit of data to be able to work with and then work together on finding ways where we can land in a happier spot for both sides.
One of those things is you don't have to go all in. You don't have to say let's just turn it on for now and now you see like 70% of your revenue going to zero and then now you're stuck in this odd place. You can mitigate the risk. You can scale down the experiment. They can run AB tests to figure out if we enable this certain thing across 1% of users or something like that and we find how they interact with the business and honestly does the user like it more, is it a more enjoyable experience for the brand side too, does the transaction volume go up, does the willingness to buy the product or access the product itself. There's a lot of work in product discovery too. There's a lot of times the user doesn't know they want to buy something but they can't find it. So does that actually increase the user being able to find exactly what they want.
Doing scaled experiments here is actually a really great playbook to run because you can scale the risk accordingly at the end of it. You get the data, it's proportional data so you don't have to run it across your entire user base. That's what we're finding is really working so far. Again it's still very early so we're still discovering this in real time.
Before talking about what it takes to build this technology, it's important to frame up what you want it to feel like and why it's so important that it has this distinctive quality and performance.
I could talk all day about this because I think that is just so important and honestly as a product builder it's a new muscle to flex and to build. Really thinking beyond capabilities is something I really want to push here. Over the last 3 years we've seen all these different product launches and new products and saying AI can now do this or AI can now do that. The consumer is first fatigued by all this, they don't know how to access it. Second, I think we're missing the point.
Early on actually one principle that we held was let's not focus on capability. Let's only focus on understandability. How much does a user understand about what's happening? What is their ability to predict what will happen when they ask this or when they do this or when they interacted with it in this way. Honestly, I think that is one of the major angles or factors that has led to engagement numbers that are completely off the charts or the viral word of mouth growth that is happening at 10% a day. It's understandable.
It just feels if I were to describe it for lack of a better word, it should just feel good in some way. In the way that it communicates to you, the purpose of communicating or sending a text message is not just the meaning of the text itself. It's down to underlying even what is the shape of the text message itself and how will the user feel when they see that. If you see a big blob of text that requires the user to scroll a little bit to find what the next message is versus maybe you frontload some of the information so that the user really gets it in the first 30% and then can optionally read the rest.
Thinking about how the user might read or even read patterns where the way that most people scan big chunks of text they might read 80% of the first line and then maybe like 50% of the next line and then it tapers off so it looks like a flag. That's a consideration that should be made. If Instinct has the ability to think about things like this, the user is going to spend most of their time reading the first two lines and then certainly the first part of each line too. So how does it craft its message to deliver in the lowest fatigue way or with the least amount of cognitive load placed onto the user. There's a lot of consideration that's put into this in terms of product building.
There's quite a bit of work that we do on the infrastructure side to make it fast and affordable to serve, but this is another area that I think is just so important and is going to be a differentiator.
It's all of the above because when you're thinking about building evals or evaluation or other testing frameworks to be able to test for these very soft qualities or these actually very long-term qualities, two or three weeks in does a user trust Instinct, how do you measure that, how do you run simulated evaluations to test if the user is going to feel trust in two or 3 weeks from now. A lot of it is just staged rollouts over time.
I might come in and build a slightly different experience and then I'll release it to myself and I'll play around with it for a little bit and see how I feel about it. I'm just very opinionated about these types of things. Then if I feel comfortable with it, I'll send it out to the team. I'll say, "Hey, you guys should try this." We'll see how they feel about it. Then they'll send it out to our smaller early access group and then they'll play around with it and see how it feels and then if we're confident, if there's any tweaks that we need to make, we'll do that and then we'll eventually roll it out to the general public.
This is very important because Instinct is quite capable and is one of the most capable products out there. But I'm not saying that over the next couple months or years that others are going to come and deliver the same seemingly same experience. But this deep focus and priority on how the user feels and how to make it the most enjoyable experience beyond the words that it's saying, just the way that it feels is just so important.
One of the great things about the history of technology is this race between incumbents getting quality and innovation versus upstarts getting distribution. Obviously we've built an incredible product. The feel of it is the worst it'll ever be. How do you think about that challenge, your speed of scaling, what your ambition is for how to get big really quickly. Do you think this is a winner take most take all market? What will the market shape of agents be?
I think you just described everything all in once in about 20 seconds there. We're famously or infamously serving an invite only product which is honestly not meant to be an exclusive thing. Although some users are treating it like that but that's really not the intention. The goal here is to be able to I'm ambitious. So I want to scale this thing as fast as possible, but I also want to do it in a responsible way that enables us to not wake up one morning and have 10 times the number of users and then 80% of them actually can't talk to it because there's not enough compute.
The interesting thing both the benefit and also the detachment is we first started out this program. We gave it to like 200 people. It was close friends and family members and we just said go try it out and then the next day five people came onto the platform because they just referred it to somebody.
It's an invite only platform. Every user will have five invites to be able to get. So just five invites. We rolled it out 200 people. Next day it was 205 and then the next day it was 210. So it's like okay cool. A couple people are sharing it to one person. But then that just started to accelerate. It was very odd. It was like not 1% 2% then it started to be 3% 4%. And then once we hit a couple thousand users some people just started to share it online too just natively share some cool use case that they had with it. And then that accelerated the growth. It actually started to turn into 6% 7% 8% 9% and now I believe we're 10 or 11% day over day.
It's not that we're doing some sort of creative marketing event. We spent $0 on marketing so far. It's not that we're doing something every single day to be able to support this growth. Every day about 10% of the audience or slightly less because you can refer multiple people are making a decision to give up one of their five valuable invites to somebody else but that is happening every single day at a 10% rate. I think there's something really very significant there when I talk about strength of word of mouth. It's honestly a little bit surprising. It's one of the strongest cases of word of mouth growth.
I find all these stories of people saying they'll ask me and they'll actually be ashamed. They'll email me and say hey can I please get an invite? I think I have a friend that has it but I don't know if I make it into his five friends. Then there are also people who are bragging like, "Oh, I got these like I got three invites left." And I'm just like holding on to it right now. So there's so much happening of people that want access to the product, but there's these odd social games happening of people using their invites.
I was seeing the other day there were some invites that were selling on eBay too. It's like $300. People were buying these invites on eBay. And it's just to control the growth.
The thing that most people are asking right now is you have much bigger players that are able to distribute to a billion or two billion people on the planet immediately and maybe they might not be compounding naturally as fast but they have such a great top-funnel distribution. Then you have us compounding at a very very fast clip every day but we don't own a major service that has two or three billion people and able to distribute immediately that day. So it's this interesting question like where does the curve line up and where's the inflection point.
There's another problem that comes with this too which is that it's an interesting scaling problem. This is what is I think the core of the problem that I spent like 40% of my time just worrying about which is it's not like the traditional other consumer products that grew very fast where it's to double the number of users on let's say Instagram or Facebook or something it would be this many number of other requests going through the platform and yes there's a scaling story there and it's certainly hard infrastructure work we also have that to be fair but what do you do when the underlying compute also needs to grow 10% there every day.
We've been doing this for several weeks now. What does it mean when the amount of compute that you need access to is now doubling effectively every week. Do we buy compute 2x of what we have right now? Well, we're going to consume that in a week. So then do you buy 5x? Well, we're going to consume that in less than 3 weeks. Do you buy 10x? So now it's like you're 10x leverage. It's if you can even stomach what it's like to buy 10x ahead, but then you're going to consume that in a couple weeks from now.
That is the hard problem. It's thinking about how far ahead do you buy. It's going at a faster rate than Claude Code or some of those other applications where they also had to reason about other similar exponential type of problems. The other subtlety here is that it's not like a certain business where when you double the number of users you can buy like 2x more resources in order to power it. It's that the resource has a lead time of several months. So you can't just go out tomorrow and start buy compute because honestly you get taxed like 3 or 4x what it is. If you're wrong, you're wrong by three or 4x.
If you zoom in on the individual user and the cost to serve them on a day per day basis, how much me using my Instinct costs in inference per day or something like this. Do you have a sense of that scale? One thing that I think is to our advantage is we've figured out how to serve the product which is when we run no matter how you evaluate whether it's AB tests whether it's internal evaluations or it's tracking engagement across users that might be on one model or the other we're able to deliver the same performance as Opus 5 which is now I guess we're dating ourselves. Opus 5 is frontier level intelligence. We're able to serve it's the same engagement rate, the same AB test performance, it's the same internal evaluation performance but at a cost that is very very low and is actually very affordable.
We're running this program every user has the product for free and our goal is really to deliver this product at an affordable I'm not going to commit to free for a lifetime for now but that is my personal goal to be able to deliver this product for free for everyone for a lifetime. It's just hard infrastructure work.
If you use frontier APIs from some of the main providers you are taking a blanket cost on a certain request and for all of the requests that might be needed to power that product for that month. But there's a lot of different work that's happening through productivity that is happening throughout the day that doesn't actually need to finish in hundreds of milliseconds. It needs to finish in minutes or even hours. There is a batch work that is consuming a lot of content that can be served with deployment shapes that are 3x 5x 8x more efficient on this with the same underlying compute. So I think that when you customize these inference deployments to be able to perfectly shape the data and the workloads that you're serving, you're able to find these 30% here, 5x there, 6x there, 10% here, and all of those compound to a rate where we were able to serve at a very low cost.
How do you think about solving the bigger problem of how far ahead to buy? And if I think about this at true scale like you get to scale of a billion users or something like this, how much new compute demand do you think this will represent? It's going to be a lot because if you just think about let's just ignore compute and just think about how many tokens are flowing through the platform.
The products that are breakout products of a couple months ago in the code generation space where it requires the user to prompt it and then it goes and runs for something and then it comes back to user and then asks for something else and then the user sends something again and sure that a lot of that is background work and so there's quite a bit of tokens that are consumed there but here it is Instinct has the ability to wake up and to sleep at any moment in time during the day. That might sound a little odd but its architecture is enabling it to do that so that it can really think about if you have a meeting that you're running late to and you need to order an Uber or if they ordered the Uber and the user is not showing up be able to help the user through those moments.
So productivity I think is going to continue to expand over time. There's this big buildout this big
There's a significant compute buildout happening in the AI space with early breakout products that are just scratching the surface of productivity and background work. The system is almost natively proactive, with a smaller subset that is actually interactive. The amount of compute needed is orders of magnitude more than initially anticipated. More productivity requires more care for the user, more time to consider what could go wrong, and substantial background processing.
For example, an AI assistant might wake up at 6:00 a.m. knowing the user wakes at 7:00 a.m., then scan everything to ensure the day is ready. It might realize it's not a great time to notify the user and go back to sleep or handle tasks in the background. At 4:00 p.m., it might identify value it can provide, wake up, complete the task, and contact the user. This level of background work doesn't exist in coding products.
The shape of the product and the workload being run are just scratching the surface in terms of token requirements. When asked about Muse, the founder noted it's a great product with a fundamentally different take despite similar underlying architecture. The focus is on making the experience simple, easily accessible, and incorporating soft qualities like thinking from the user's standpoint about what's important, how to make tasks easier, and how to present information effectively.
The founder spends very little time thinking about competition because AI adoption remains limited. Walking into a nearby café reveals very few people using AI the way they imagine or want to use it, and adoption is even lower in other countries and cities. The space remains open and exciting, with focus placed on building the best product experience.
The approach to security emphasizes being proactive rather than reactive, looking ahead for new surface areas and risks. This philosophy drove the early access invite program from the start. Early versions lacked firewalls, active monitors, and other infrastructure pieces designed to provide control to the agent and secure existing systems. Rather than patching problems, the team built entirely different systems to systematically solve these types of issues.
The core principle is that users should always be in control of their data. Users can share as much or as little as they like and can retract access at any time. When asked about the future of security given the vast information and context collected about people, the response emphasized that security and safety are the most important problems. This requires embedding security and safety into the company's core values and product-building mindset.
Historical patterns show that new consumer experiences always face immediate backlash, with different being confused with unsafe. The approach stays core to principles: users remain in control of their data and never feel out of control, while being proactive about systems to get ahead of potential issues.
Language models hallucinate, but this product requires systematic solutions to prevent hallucination. A decoupled validation system scrutinizes actions before they are taken, thinking traces before they are executed as tool calls, and any potential actions. This acts as a watchdog filter that can detect when a proper noun was generated due to sampling error in the underlying model.
World-class security teams continuously work proactively to find harder adversarial cases and edge cases. Adversarial testing and creative edge case identification are making these models more robust over time. The platform improves through continued adversarial testing and creative identification of edge cases.
The product experience is intentionally very simple, with simplicity as a key focus. The vision is that it becomes even simpler over time. While an application may be rolled out in the near future, the trend is toward simpler interfaces where users may not need to open iMessage, type content, send messages, and look at responses.
A subset of users interacts with the system only through voice, with more than 90% of their messages sent primarily through voice. An action button on the phone can be used to give commands like "say hi to Patrick in two hours" without unlocking the phone or opening applications.
Long-term vision includes real-time voice recognition that understands individual voices and has discretion to know when users are addressing it versus others. Users could wear AirPods during activities like hiking, biking, or running and have natural conversations where the system provides relevant information like contract reviews, news summaries, calendar additions, and responses to messages without requiring phone interaction.
A files feature enables the system to send entire sites and full web applications to show more complex information like trip itineraries or wedding plans that require creativity and more surface area. The system can generate these on the fly.
Over the past 20 years, new interfaces required building applications through long software development cycles. This has led to consumer fatigue with app proliferation. The prediction is that all software will collapse into a single, very easy-to-use interface, with capability expanding rather than being limited.
As friction around daily tasks decreases to zero, the system will move toward pursuing higher-level objectives aligned with user goals. Instead of tracking individual workouts, users will specify objectives like gaining or losing weight or hitting certain mile times over 3-4 months, then work with the system to achieve those goals.
Small businesses are being run natively on the system, with entire back offices functioning on the platform. Parts are fully autonomous, pursuing higher-level objectives like maintaining inventory levels within specified ranges rather than executing static commands like ordering specific items.
The system is designed as seamless, quiet, extremely capable, and reliable rather than having a cheeky personality. Relationship building in the manner of the movie Her is not pursued. Instead, it should act like a socially aware operator that knows the best interaction patterns for different people in different rooms and learns these over time.
The system is one of the most customizable experiences because it evolves over time by picking up on user preferences, such as when they didn't like something said in a certain way or when response rates are lower due to excessive text or large files. It learns these patterns and becomes easier and more delightful to use without imposing certain experiences on users.
The name Instinct was chosen because the system should not be something users feel they can bully or look down on. It should be perceived as a new creative, exciting, competent actor with mutual respect and trust. Users should feel safe and trust that it has their back, is intelligent, competent, and socially aware. The name avoids personifying it as a human, allowing users to develop their understanding through product experience with a fresh slate.
The focus is on first principles: identifying interfaces users trust and are familiar with, then delivering the experience through channels they already interact with daily. The system is not pinned to iMessage, with more than 50% of traffic running outside of iMessage. The approach prioritizes what is most practical for users and will adapt to deliver zero-friction experiences.
The latest round raised approximately a billion dollars at roughly a $10 billion valuation, led by investors including Zoya, Benchmark, and Kotu. The focus is less on the numbers and more on demand for this type of AI experience. The business is capital intensive because it aims to distribute to billions of people daily at affordable cost despite high compute costs.
Bootstrapping is necessary, especially given the business model being pursued. While a subscription model charging $100 per month could be implemented for short-term rewards, raising capital enables taking calculated risks to get ahead of speed bumps and prove transaction volume and industry experience. This approach helps escape local optima like subscription plans.
The founder expressed gratitude for being surrounded by great relationships across industry, personal life, and various contexts, and hopes to extend the same level of kindness and thoughtfulness toward others. The emphasis is on the qualities of kindness and thoughtfulness that are most appreciated.
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