Market Map: Gen AI Companies with Foundational Models

In such a scenario, key players must adopt effective strategies to stay ahead of the competition. This segment captures a significant market share in 2023 due to the high demand for text generation and text completion. In addition to cataloging tools by content type and business function, the map labels each according to the following five technical categories. Sonya Huang, a partner at Sequoia, recently tweeted an evolving “map” of the generative AI landscape.

But based on the early data we have for generative AI, combined with our experience with earlier AI/ML companies, our intuition is the following. Just to make a 3D model, you need to go from concepts to modeling to optimizing to texturing to UV-unwrapping to rigging to animating to composing to lighting… Along the way you may return to early stages to make various improvements. And then you need to get this content out to participants in an ever-changing world. All this requires high amounts of expertise and a wide range of supporting technologies at each step.

Financial wellness in the AgeTech industry provides opportunities for insurers

So, in general, there’s significant cost savings by running on AWS, and that’s what our customers are focused on. But every customer is welcome to purely “pay by the drink” and to use our services completely on demand. But of course, many of our larger customers want to make longer-term commitments, want to have a deeper relationship with us, want the economics that come with that commitment. Another huge benefit of the cloud is the flexibility that it provides — the elasticity, the ability to dramatically raise or dramatically shrink the amount of resources that are consumed. In the first six months of the pandemic, Zoom’s demand went up about 300%, and they were able to seamlessly and gracefully fulfill that demand because they’re using AWS. You can only imagine if a company was in their own data centers, how hard that would have been to grow that quickly.

We expect to see constant yet healthy tension between Infra providers’ general models and application startups’ finer-tuned vertical ones. Given text AI is the area that has been researched and invested in the most, the dynamics seem to be the most fluid and fast-changing. The biggest change has been the rise of generative AI, and particularly the use of transformers (a type of neural network) for everything from text and image generation to protein folding and computational chemistry.

Call for Startups

In the Enterprise sector, Anthropic, Cohere and AI21Labs are developing embeddable text processing and generation tools to enable productivity gains in corporate functions such as Customer Service, Marketing, Coaching, Search, Back-Office and Sales. “A lot of these places that are attempting to do this are just not tech-native or tech-first companies,” BCG’s Gupta said. For one thing, Yakov Livshits smaller companies are competing for talent against big tech firms that offer higher salaries and better resources. “There is a lack of technical talent to a significant degree that hinders the implementation of scalable MLops systems because that knowledge is locked up in those tech-first firms,” he said. Jamie Condliffe (
@jme_c) is the executive editor at Protocol, based in London.

Today, Generative AI outputs are being used as prototypes or first drafts. As the models get smarter, partially off the back of user data, we should expect these drafts to get better and better and better, until they are good enough to use as the final product. We have seen this distribution strategy pay off in other market categories, like consumer/social. Just as mobile unleashed new types of applications through new capabilities like GPS, cameras and on-the-go connectivity, we expect these large models to motivate a new wave of generative AI applications.

If you are a founder and would like to meet or would like us to add your startup to our market map, please reach out to us at Jenny AT leoniscap.com or Jay AT leoniscap.com. Despite the impressive performance of AI, entrepreneurs should resist the urge on both ends of the spectrum – over-promising before delivery and over-building before knowing there is a real market. Another factor that makes the generative AI landscape ultra-competitive is that the technology has become a consensus almost as soon as it took off. Typically, major tech revolutions evolve slowly because most people are skeptical at first, great examples include the PC and smartphone revolutions.

Yakov Livshits
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.

This article is a guide to the companies building the generative artificial intelligence technology that will lead to these virtual worlds (games, simulations, metaverse applications). Today, foundation models (e.g., GPT-3 and Stable Diffusion) are frequently adapted to build generative applications, as that’s their most “wow! This first wave of Generative AI applications resembles the mobile application landscape when the iPhone first came out—somewhat gimmicky and thin, with unclear competitive differentiation and business models. However, some of these applications provide an interesting glimpse into what the future may hold. Once you see a machine produce complex functioning code or brilliant images, it’s hard to imagine a future where machines don’t play a fundamental role in how we work and create.

These applications are already fundamentally changing the way we create, the way we synthesize information, and the way we work. Several frameworks have emerged, each building its own interchangeable and complementary ecosystem of tools. LangChain has become the developer community’s open-source focal point for building with foundation models.

Generative AI: A Creative New World

Developers can improve performance across all three vectors by iterating on prompts, fine-tuning the model, or switching between model providers. However, measuring performance is more complex due to the probabilistic nature of LLMs and the non-determinism of tasks. Builders who deeply understand their users’ workflows and they can be augmented by AI will have a leg up. It’s no surprise that some teacher co-pilots are built by former teachers themselves. A school principal built MagicSchool, a lesson planner and assessment generator; another pair of principals are working on EnlightenAI, an AI-assisted grading tool for essays.

And just as the inflection point of mobile created a market opening for a handful of killer apps a decade ago, we expect killer apps to emerge for Generative AI. Making Powerpoint decks is as close as many people get to being creative at work, but new generative AI apps like Tome make it easy to design beautiful presentations that bring your ideas to life with only text prompts. Another take on work productivity comes from Adept, which has built an action model, ACT-1, that’s trained on how people interact with their computers. Its goal is to eventually automate some of the searching, clicking and scrolling you have to do now to get tasks done.

Generative Applications

Real-time and agile insights became essential in this dynamic environment, prompting researchers to adapt and stay ahead of shifting trends and demands. The reason these are “pseudo new markets” is that AI applications/companies are solving copying writing, sales, content creating, and coding – the same problems, with a much more efficient approach. Those are not completely new markets but those are the markets that can be expanded with the new type of tooling that’s provided by AIGC. At Leonis Capital, we were bullish on AI in 2020 when it was not as hot. We see both AI and decentralized protocols as “supercycle technologies” from a long-term point of view. We will elaborate more on this thesis in the upcoming essay and explain how these underlying technologies are creating new ways for human society to generate, manipulate; store and verify data, therefore impacting our society in the coming decades.

• Code completions happen in your IDE; image generations happen in Figma or Photoshop; even Discord bots are the vessel to inject generative AI into digital/social communities.
• And cynics are right to seriously question both the attention span and herd-like behavior of the VC industry in general!
• The data mesh is a distributed, decentralized (not in the crypto sense) approach to managing data tools and teams.
• The built-in AI text editor could make it much more convenient to use than standalone web apps.
• We believe the modular model and further standardization are here to stay as the benefits of modularization extend beyond increasing development speed and agility.

Spots are still available for this hybrid event, and you can RSVP here to save your seat. Rather, before taking the judge position Faruqui was one of a group of prosecutors in the U.S. Attorney’s office in Washington, D.C., that called themselves the “Bitcoin Strikeforce,” and worked with agencies like the IRS and FBI in federal investigations. There, Faruqui prosecuted cases that involved terrorism, child pornography, and weapons proliferation.

It has made AI more accessible to people who do not have a background in computer science or machine learning. OpenAI’s GPT models are a flavor of transformers that it trained on the Internet, starting in 2018. GPT-3, their third-generation LLM, is one of the most powerful models currently available. It can be fine-tuned for a wide range of tasks – language translation, text summarization, and more.