Introducing: the Scale Generative AI Index Scale Venture Partners

Core features and services are still being developed and refined and product differentiation is likely to become more clear as each of these companies builds and solidifies their offering. Whether in the Consumer or Enterprise space, search might become even more powerful and convenient, customizable and relevant, and this is a win for everybody involved. For companies that have been forced to go DIY, building these platforms themselves does not always require forging parts from raw materials. DBS has incorporated open-source tools for coding and application security purposes such as Nexus, Jenkins, Bitbucket, and Confluence to ensure the smooth integration and delivery of ML models, Gupta said. Intuit had MLops systems in place before a lot of vendors sold products for managing machine learning, said Brett Hollman, Intuit’s director of engineering and product development in machine learning. As companies expand their use of AI beyond running just a few machine learning models, ML practitioners say that they have yet to find what they need from prepackaged MLops systems.

We think there is truth in that argument – however, distribution is a place for AI businesses to start but not sustain. A new business strategy might be to build the company first and then wait for the release of more powerful AI models. In fact, many generative AI companies were built prior to the release of their underlying models. Lensa.ai started as a photo editing tool in 2018 but quickly integrated Stable Diffusion when the model was released in April 2022. AI Dungeon rolled out in 2019 and initially used GPT-2 before the more powerful GPT-3 was released. Founders can build in adjacent fields before pivoting to generative AI, and Investors can anticipate where AI technology is heading and be the first mover, much like the Uber investors who predicted the rise of ride-sharing.

Notable Investors

Overall, startup exit values fell by over 90% year over year to $71.4B from $753.2B in 2021. The VC pullback came with a series of market changes that may leave companies orphaned at the time they need the most support. Crossover funds, which had a particularly strong appetite for data/AI startups, have largely exited private markets, focusing on cheaper buying opportunities in public markets. Within VC firms, lots of GPs have or will be moving on, and some solo GPs may not be able (or willing) to raise another fund.

• I recommend IMARC to all that need timely, affordable information and advice.
• What we’re really trying to do is to look at that end-to-end journey of data and to build really compelling, powerful capabilities and services at each stop in that data journey and then…knit all that together with strong concepts like governance.
• By allowing players to create specialized in-game assets, the game became both larger and more personalized.
• We are of significant enough scale that we, of course, have good purchasing economics of things like bandwidth and energy and so forth.
• But AI-native upstarts that create delightful user experiences and create sticky products can catch up on acquiring user data.

Of course, the key to the creative possibility of these objects will be the production-ready granularity of the object’s hierarchy. For example, models will need to be able to generate a car, but also contain logic to be able to regenerate just the side mirrors. With an explosion of potential characters and objects resulting from AI, it will be critical for animation teams to deploy these at scale. Tools like Move.ai, Latent Technology, and others accelerate the most time-consuming and expensive aspects of motion capture, animation and rigging. Modding has also been critical to growth strategies for game communities.

MAD 2023, part I: The landscape

Market research has evolved significantly, transitioning from paper surveys and manual data analysis to where it stands today. With the advent of generative AI, QuestionPro has a mature research platform and the opportunity to take insights management to the next level, revolutionizing market research in the coming decade. Earlier this year, on a “Live with Dan” episode, esteemed market research leaders like Jamin Brazil, Vivek Bhaskaran, and Leonard Murphy discussed the impact of ChatGPT on data quality in research and insights. We at QuestionPro, being early adopters of generative AI in market research, have seen the industry surpass the interest and adoption in this field. From generating research ideas, validating past hypotheses, analyzing large data sets, and evaluating human emotion in qualitative research, AI is pushing the global research industry towards Research 3.0.

In 2021, OpenAI released Codex, a model that translates natural language into code. You can use codex for tasks like “turning comments into code, rewriting code for efficiency, or completing your next line in context.” Codex is based on GPT-3 and was also trained on 54 million GitHub repositories. In turn, GitHub Copilot uses Codex to suggest code right from the editor.

Gaming x AI Market Map: The Infinite Power of Play

The low cost and ease-of-use of these models is causing the evolution of AI-apps to accelerate as more engineers jump into building with artificial intelligence. We are closely following the momentum of these overlapping but distinct trends. And to track our own work and share some of the knowledge we’ve accumulated, we’re introducing the Scale Generative AI Index, a list of nearly 200 companies in the space and details about what they’re building.

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.

That being said, many customers are in a hybrid state, where they run IT in different environments. In some cases, that’s by choice; in other cases, it’s due to acquisitions, like buying companies and inherited technology. We understand and embrace the fact that it’s a messy world in IT, and that many of our customers for years are going to have some of their resources on premises, some on AWS. We want to make that entire hybrid environment as easy and as powerful for customers as possible, so we’ve actually invested and continue to invest very heavily in these hybrid capabilities. In general, when we look across our worldwide customer base, we see time after time that the most innovation and the most efficient cost structure happens when customers choose one provider, when they’re running predominantly on AWS.

AI products that focus too much on AI and not enough on customers and market size are essentially hammers looking for nails. They might be cool at first but soon lose traction as similar products emerge or when consumers get used to the AI models. Text models are the most mature type of generative AI model, and they are also the earliest models to be developed. There are also many more text models available than any other type of generative AI model and there are also more available APIs and open-source models. Aside from well-known labs like OpenAI and DeepMind, newer entrants are also contributing to the AI language model infrastructure layer, including the Israeli AI lab AI21 and the Canadian startup Cohere. This is probably because generative AI is creating (pseudo2) new market categories by providing aggressive ROI value propositions.

The best (or luckiest, or best funded) of those companies will find a way to grow, expand from a single feature to a platform (say, from data quality to a full data observability platform), and deepen their customer relationships. As there are comparatively few “assets” available on the market relative to investor interest, valuation is often no object when it comes to winning the deal. The market is showing signs of rapidly adjusting supply to demand, however, as countless generative AI startups are created all of a sudden.

We created this visualization of all large-scale language models (LLMs) released since 2018 (there are many of them!) The quick conclusion is that these models are becoming bigger and more compute- and data-intensive at an exponential rate. Foundation model “scaling laws” predict that model capability will increase with model size. Interestingly, however, these companies might also benefit from the generative AI hype because investors tend to lump them into the “generative AI” bucket. However, they are unlikely to capture the true value created by the foundation model revolution unless they innovate their underlying technology.

Non-Generative Applications Built with Foundation Models

In the past two weeks, several key developments have occurred, shaping the market and pushing its boundaries. Another consideration is how new AI tools integrate with existing software in schools and workplaces. These tools are often outdated and deserving of disruption, but enterprise tech stacks are often entrenched and not quickly replaced. Nascent stage studies show that roles like research ideation, data entry, data analysis, report building, Yakov Livshits storytelling, drawing conclusions and trendlines from past research, and more will see a proliferation of the use of conversational AI. Overall, generative AI’s influence on the market research industry has been transformative, propelling it into a new era of innovation and adaptability. At Leonis Capital, we are long-term bullish on the next generation of “supercycle companies” that are powered by AI and decentralized protocols.

Databricks seems to be on a mission to release a product in just about every box of the MAD landscape. This product expansion has been done almost entirely organically, with a very small number of tuck-in acquisitions along the way – Datajoy and Cortex Labs in 2022. It announced three acquisitions in the first couple of months of 2023 already. Private equity firms may play an outsized role in this new environment, whether on the buy or sell side.

Furthermore, the increasing investments in Artificial Intelligence research and development across the globe will also cause an increase in the generative AI market growth. The versatility and potential of generative AI across different sectors have driven its adoption and contributed to market growth. Generative AI has found extensive use in the entertainment and media industries. It is used to create realistic computer-generated graphics and special effects in movies and video games.