Ten AI companies that investors and founders are keeping a close eye on.
Like many people, I’m fascinated by AI’s capabilities. I’ve read this article from The Generalist twice now. It’s hard to get your head round – and remarkably straightforward at the same time. Although written from an investor POV, I think it’s useful for anyone trying to grapple with what AI means for us going forward.
What are the common characteristics of AI applications that are seeing fast adoption?
- Focusing on what’s new. Apps that leverage the unique advantages of LLMs or other models often see strong uptake. Builders are asking themselves: What can this technology uniquely do that prior tech can not?
- Reducing drudgery. Applications that replace repetitive human labor or core workflows with light machine intelligence are compelling. Reducing or eliminating painful manual work holds obvious attraction.
- Augmenting the human. Full automation may not be possible in many instances. As a result, some applications have taken a “human in the loop” approach, focusing on augmenting user capability rather than taking it over completely. Humans are used to correct hallucinations or provide a qualitative view on accuracy or wording.
AI to watch:
You can read the full article here, or skim through the summary I’ve put together of the ten AI companies below.
Harvey helps lawyers perform tasks in due diligence, litigation, research, and compliance. It is off to a good start: the firm has already landed deals with behemoths like PwC and Allen & Overy. The legal and compliance world is a great example of one that will be remade with AI – for everything from litigation to drafting insurance claims to filing with the courts on behalf of human clients.
Using Kumo, companies can query their future just as they might rely on a database to search their past. Instead of analysing what happened last year, Kumo allows customers to see what may happen next year. The impact of such a product may be profound: businesses are no longer limited to analysing past events; they’re better able to anticipate new opportunities. Users will still want to trace data that shows what went wrong, but they will use Kumo to see what can go right.
ReflexAI brings the best in machine learning and natural language processing to mission-driven, people-centric organisations via innovative tools that transform how they train, develop, and empower their frontline teams.
Co founders Sam Dorison and John Callery are leaders at The Trevor Project, an organisation that does critical work in suicide prevention among LGBTQ youth. They started tinkering with OpenAI’s early models in 2019. They realized the potential to use these models to help train full-time agents and part-time volunteers in crisis conversations and spent a couple of years building software to do that. Their Crisis Contact Simulator was named one of TIME’s best inventions of 2021, training thousands of counselors to better support kids with their mental health, especially in times of need, saving many lives. And then, in 2022, as the world realized the power of GPT-3, Sam and John understood that there was a bigger opportunity: to take their learnings from The Trevor Project and apply them to build AI-powered support tools to train, develop, and empower frontline teams across organizations and companies.
As models and data in AI have opened up, large-scale compute in the field still relies on just a few large cloud providers. Does compute have to be proprietary? Bitcoin, Ethereum, and other crypto networks proved that decentralized pooling of large shared compute resources is possible. What if we could recreate these scaled networks but for higher-value workloads such as LLM training and inference?
Together is aiming to do just that, enabling researchers, developers and companies to leverage and improve artificial intelligence with an intuitive platform combining data, models and computation.
AI is taking off in drug discovery. Companies are racing to produce built-for-purpose, AI-designed drugs with optimized binding or function. These organizations rely on models that search the entire, near-infinite space of possible molecule structures. This process offers exciting possibilities and raises new challenges. For example, as AI gets better at predicting structures with desirable drug properties, increasingly, the question will become: how do we make them?
PostEra’s drug discovery platform, dubbed “Proton,” takes a holistic approach to lead optimization by more explicitly incorporating synthetic pathway prediction into the generative ML. It focuses on removing the bottleneck of chemical synthesis in the design cycle for faster iteration and testing more molecules. Proton leverages the company’s “Manifold” software system, which suggests practical synthetic routes for arbitrary chemical structures.
While “moving fast and breaking things” might work in some industries, it doesn’t in healthcare.
The healthcare industry is among the slowest adopters of new technologies, and possibly rightfully so. An AI model with 70% accuracy in predicting email text is annoying. But when making decisions that impact patient outcomes, it’s unacceptable.
Pathway is an AI-first clinical decision support tool that has spent years building a vast and structured medical knowledge graph, vetted by experts to ensure reliability. By leveraging advanced language models with this best-in-class data, Pathway aims to generate trustworthy output, free of hallucinations, anchored in well-referenced and verified information.
The result is something like a smart assistant for doctors. Using Pathway, medical professionals can seamlessly read relevant medical guidelines, receive patient-specific advice, and explore differential diagnoses. Pathway refers to itself as doctors’ “instant second opinion.”
Neural Radiance Fields, better known as “NeRFs,” is a technology that, in simple terms, allows you to transform photos taken from any device into fully-fledged 3D models. Unlike previous 3D scanning technologies, it requires no specialized hardware (such as LIDAR sensors). The output is considerably higher quality than anything we’ve seen before, with far higher visual fidelity and photorealism. Light, shadow, and reflection are all possible with NeRFs.
Luma is at the vanguard of deploying this technology. The startup’s app lets customers capture photorealistic 3D from their smartphones. These images can then be used as game assets, e-commerce product shots, or artistic creations.
If you can photograph it, it can become 3D. Luma’s tagline says it best: “3D, finally for everyone!”
Despite its ubiquity, visual content is the most challenging form of information to analyze as it is typically unstructured. Missing out on the insights in visual formats is a loss for data-driven organizations. It’s also part of a broader issue: according to MIT, 80% of enterprise data is unstructured, trapped in audio, video, and web server logs.
With the product, customers upload raw images or videos directly into Coactive’s platform through an API or secure data lake connection. The visual data is then embedded and indexed by Coactive’s platform with minimal manual supervision or labeling. It’s then made available through Coactive’s fully hosted image search API and SQL interface for users to gather insights and run queries and searches. Coactive carefully developed its UI/UX to make it easy for both the citizen and data scientist to leverage and derive value from it.
For example, a fashion brand can upload large sets of visual images and videos and define concepts and categories within seconds rather than days. This allows the brand to better understand how customers interact with their products in near real-time.
In all the excitement, it’s important to remember that, as with any other great technology, AI is only as good as its UX when it makes contact with reality. Right now, we’re seeing a lot of cool point-based solutions that demonstrate magical features such as transcription, summarization, creative writing, image/video generation, and coding.
an AI-powered learning platform for enterprise. The product is both a traditional learning management system where you can create courses and run live sessions, and a knowledge management platform that creates a “company brain” that can be queried directly by integrating into platforms like Google Workspaces, Notion, GitHub, and so on.
The platform is beautiful, has real-time collaboration, and is way ahead of the market in terms of core SaaS functionality. But it’s also clearly designed to be AI-first in a subtle but effective manner. You can use AI to make an entire course from scratch (text, images, quizzes) or as a co-pilot that helps you complete your work faster by automating information retrieval and content creation.
Enveda is doing fascinating work. It teaches computers Chemistry, the language of chemistry. Combining next-generation mass spectrometry (a syntactic representation of chemical space) with LLMs, Enveda goes from generally indiscernible syntactic gobbledygook to a well-defined grammar and finally to the semantics of chemical structure and properties.
Why does this matter?
- We can suddenly read the functional lego pieces resulting from billions of years of evolution. (What are the planet’s naturally occurring chemistries, and why have biological processes evolved under intense selective pressure to produce them and not others?)
- We can better understand human disease by analyzing metabolites inside our cells, the building blocks for DNA, RNA, and proteins.
- We can speak back, using this new tool – language – to design novel chemistries (for those in the industry “small molecules”) as therapies.
As Zavain Dar, founder and Managing Partner at Dimension Capital says, “We’re amidst a singular moment in the history of humanity. We’ve invented an alien intelligence and are (sheepishly) grappling with the near-term tech and venture implications.”