Announcing AI Camp: Native Applications

We’re seeking early stage teams who are building at the application layer unlocking new, native user experiences only made possible with AI. Apply here.

We exist in an era of an unusual dissonance. The explosion of compute applied to scaling the capabilities of models that approximate cognition yields new discoveries every day. But it feels fundamentally unsatisfying because so much of those capabilities remain locked away in the weights of models, even as they are proven by research. Our intuition is that the corresponding innovation in the space of applications lags behind the power of the science, and that there are incredible native experiences and companies that can be built and tremendous value for humanity created in filling that gap.

Put differently: Making this is solved…

…but we aren’t yet living in the future that we imagine.

And making this is solved:

…but we aren’t yet living in the future that we imagine.

In our brainstorming around this theme, we ended up with quite a few scribbles to depict the gap between the underlying technology capabilities, the infrastructure, and the applications we actually want to use. Here’s a look at a slightly polished version of that visualization.

The dissatisfaction described above hits us in different ways on a daily basis. We’ve invested in companies that train models. We’ve invested in companies that make the infrastructure for making better models and improving the outputs of AI systems. And yet, when end users like ourselves are faced with a task or project we are certain could be done faster, better, smarter, by harnessing the power of AI, the ready-made tools we have available to us in that moment are surprisingly limited.

At Betaworks, we see AI as a phase change in technology akin to the birth of the internet. And, just like the internet, the services and products built on top of it can form wave-like patterns — replicating existing workflows, augmenting those workflows, and ultimately paving the way for truly native experiences.

Think back to the original apps on the App Store. They were lighters, bells, flashlights. Nothing of distinctive value, but a clear exploration of the native mobile experience that could leverage things like gyroscopes, accelerometers, touch screens, and other elements of the hardware. Services like Stripe, Unity, New Relic/MixPanel and Twilio emerged as important tools/infrastructure within that landscape. The convergence of this type of advanced mobile hardware at scale with the maturation of the infrastructure layer around apps led to brand new, native experiences for consumers. Eventually, we enjoyed high-utility apps like Uber and Instagram or Dots — apps that couldn’t have existed without the multi touch interface on the pocket-sized computers, we carry around in our pockets.

Now put this in the context of AI.

How frequently do you use native AI?

We’ve been talking to people about this for a while now, and it isn’t 100% easy to explain, so maybe it’s best to share a super simple example:

Take restaurant reservations (thanks to Extensible for this example). Before LLMs were a glint in anyone’s eye, you would see a restaurant, go to Opentable, and make a reservation.

But now you have Claude and GPT4! So you teach your LLM how to use the Opentable API, throw up a chat widget and tell your app “make me a reservation at Roberta’s.” You make an incredibly awesome system prompt. Congratulations, you may close Xcode, you just made the 2024 version of iFart.

So what would the AI native version of this app be like? This is what we are trying to find out because to be honest, we’re not completely sure, in part because AI capabilities imply so many different things. But to get the juices (and applications) flowing, here are some ideas of the kinds things that can take you from the GPT API wrapper to something we’d be more interested in working with you on:

• The AI system is resilient to the fact that there are multiple Roberta’s locations, and queries the user.
• Rather than typing in the name of the restaurant, the system accepts a picture that you send it via SMS.
• The system uses tools, like reading the JPEG metadata to determine which location you are trying to make a reservation at.
• The system has ample context, and the ability to know when you like to eat, when you have other reservations, and with whom and kicks off several ancillary workflows in the background, around making calendar entries and inviting friends.
• When the system doesn’t find a reservation at the time it wants to make it, it makes a phone call to the restaurant to see if there are unlisted tables.
• You never even needed to ask it to make the reservation, the system knows tables are hard to get and it noticed you were going at a pretty regular time, so it held the table for you, and sent you an email to confirm if you actually wanted it.
• The system has an understanding of what you like about Roberta’s and makes a different reservation based on a high dimensional mapping of pizza concepts when it can’t get the reservation you want.

We probably wouldn’t do something in restaurant reservations. These are just some ideas. But really, we want you to tell us about what you’re doing at the edge of what is possible with AI.

In the past year+, millions upon millions of dollars have been invested in the picks and shovels of the AI ecosystem. We’ve invested in some of those picks and shovels ourselves. This camp is for people who are using those picks and shovels, plus their own secret sauce, to make highly valuable, engaging products for end users.

Which is why we’re seeking up to 12 startups building at the application layer of AI to deliver net new experiences for end users and businesses.

To be clear, we don’t necessarily mean mobile apps. We’re focused on the application layer where users can actually interact with, and get value from, the technology. This can take the form of consumer-facing products, software targeted at prosumers, or even business-oriented applications that have a bottoms-up sales motion. The interface for these things can be anything from voice to chat to GUI, on web or mobile or even spatial computing.

Here are some key attributes we’re looking for:

Native Interface/UX  

A unique POV on interface is important. The most obvious interface for AI native applications is natural language/conversation, either via text or voice, but there are likely innovations not yet imagined at the GUI level, not to mention agentic interfaces that may arise within other existing workflows/applications or be altogether disposable/invisible. For example, Dragon (from Opponent) is only accessible via a FaceTime call, so kids interact with the creature just like they would with Grandma.

Personalization

‍Native AI software doesn’t reach its potential without personalization. But this is both a technical challenge and a UX challenge. Systems for persistent memory, context windows, etc. stand in the way of truly personalized software, but so does the move toward easier onboarding and less consumer friction. How do you pull, from the mess of a human brain, preferences, priorities and inherent knowledge and inject that into a model to produce an output that’s as good as, or better than, what a human could do on their own?

Technical Defensibility/Moat

we are not looking for simple GPT wrappers. Whether that means fine-tuning an open source model on a specific data set, running a sophisticated RAG pipeline, or having access to some set of proprietary and/or synthetic data, we require that your application bring something beyond a nicely designed interface atop a closed model. Have you invented the next COT prompting strat that everyone will be using in 6 months? Have you figured out a fine tuning or adapter strategy that’s more efficient than what everyone else has? Have you figured out how to do something that nobody else can do yet? Show us.

Resilience

We all know that a large obstacle to true mainstream adoption is a high failure rate, and a failure rate that increases with complexity. We’ve been working with these models for a few years now. Tell us about how you are going to delight your users with high quality experiences that work when you need them most.

Distribution Edge

‍A lot has changed in the past few years around distribution of consumer software. The old playbook is relatively useless. Most consumer software benefits from scaling its user base — on the obvious end that simply looks like more revenue, but on the more discreet end that may look like a proprietary data set that can be used to make the product more valuable. However, who is thinking about scale as a benefit to the end user? We’re looking for products that incentivize growth via a mechanic within that software.

Teams that bring a unique perspective on these attributes, and demonstrate an ability to bring some technical expertise alongside that perspective, are highly attractive candidates for camp.

If you think that’s you, please apply here.

Here’s how Camp works:

Camp is a thematic investment and in-residence program for startups building in frontier technologies. Each camp consists of 13 weeks “in-residence” at Betaworks to help early stage companies with product development, platform strategy, data science, branding, and fundraising. Entrepreneurs have access to the Betaworks team, its network, and to a carefully curated group of industry leaders to assist with general company-building needs.

For each participating Camp company, Betaworks Ventures will invest $250k on an uncapped SAFE note with a 25% discount, and receive a 5% common stock stake in the business. Our syndicate partners will be adding up to $250k total on uncapped SAFEs with the same 25% discount.

To summarize, participating companies will receive an uncapped SAFE note with a 25% discount from Betaworks + our syndicate partners, and Betaworks will receive 5% of the company’s common stock. More details here.

We also host regular community events, you can sign up here to stay in the know: beta.works/bytes