The hottest investment area in AI right now, the race to make AI more mainstream and other tech news this week
LinkedIn News
Bringing you the business news and insights you need to stay informed.
Welcome to LinkedIn News Tech Stack, which brings you news, insights and trends involving the founders, investors and companies on the cutting edge of technology. It’s created by Tech Editor Tanya Dua and you can check out our previous editions here.?
First: Catch up on the latest edition of our weekly Q&A series, VC Wednesdays, featuring solo investor Rex Salisbury of Cambrian. And check out the bonus video, where he shares when’s the right time to quit something, here.
A deep dive into one big theme or news story every week.
Last week, 英伟达 and Capital One backed cloud data analytics startup Databricks in a new funding round in which the company raised more than $500 million at a $43 billion valuation. It wasn’t Nvidia’s first rodeo, but a notable addition to the chip maker's growing roster of AI infrastructure startups.
Nvidia isn’t alone. As AI adoption matures and enterprises go all-in on the technology, AI infrastructure startups – the tools and platforms that actually power AI models – have become hot areas of investment, amassing 70% ($11.3 billion) of the overall funding in the generative space between the third quarter of 2022 and the second quarter of 2023, per CB Insights .
“The easiest way to think about it is all the picks and shovels that are helping the AI ecosystem develop such that we can take these applications into the enterprise,” said Elliott Robinson , partner at VC firm Bessemer Venture Partners . “I’m seeing more and more competing term sheets for these kinds of startups.”
A major driver is that as companies of all stripes scramble to figure out their AI strategies, they are facing infrastructural challenges like what data to use and how to organize it, how to build and deploy AI models safely, and how to scale efficiently, said George Mathew , managing director at Insight Partners .
“There’s a tremendous amount of pressure in the market for companies to figure out how to enable themselves from an AI standpoint – and that's coming totally from the CEO and the board level,” Mathew said. “To get AI applications and foundation models into production, you have to have a number of key capabilities figured out.”
Investors who flocked to AI startups after the overnight success of ChatGPT are also learning that converting buzzy startups into successful businesses is not easy, said Rudina Seseri ,?founder and managing partner of Glasswing Ventures .
For one, it’s hard to compete with the “oligopoly” of foundational or large language model (LLM) providers like OpenAI and Cohere , which are backed by deep-pocketed incumbents. And second, investors are moving away from AI applications like chatbots because of the realization that they are neither differentiated nor have a defensible moat. If all you’re doing is building a generative AI application on top of an LLM, bigger players could easily mimic it, said Seseri.
“The middle layer is where the opportunity is,” she said.
That’s not all. AI infrastructure is an umbrella term that’s broad in scope. AI models are trained on reams of data that enable them to get increasingly efficient in making predictions, or what’s called inference. Everything that aids this process – from hardware like chips and GPUs, software platforms that store data and secure data, tools that help fine-tune models and even startups that evaluate the best LLMs to use – fits into the bucket of AI infrastructure, investors said.
“It’s ever-evolving, but it’s essentially a compilation of tooling systems and platforms that enable engineers and data scientists to build and deploy AI,” said Madison Faulkner (Hawkinson) , an investor at Costanoa Ventures .
To be sure, so-called MLOps within AI infrastructure, which focuses on streamlining the process of taking machine learning models to production, has existed for a while, said investors including Kanu Gulati , partner at Khosla Ventures . But LLMs are a different beast given their size, and they bring a new set of challenges when it comes to training and evaluating them, she said.
One area that Hawkinson said she’s paying attention to is startups that help companies convert their messy, unstructured data into structured data that can be used for AI models, like unstructured.io . Another set is those startups that enable enterprises to easily collect, transform and analyze their data in real time – without which they wouldn’t be able to build any AI applications. Feldera , which Costanoa just invested $6 million into last week, is an example.
领英推荐
Like was the case with the cloud revolution, another buzzy subset of startups include those that remove adoption barriers by focusing on security, compliance and data privacy, said Glasswing’s Seseri. An example is Allure Security , which aims to tackle bad actors who use generative AI to spoof genuine websites and carry out scams.
Perhaps the most visible group of startups are those on the hardware side that are trying to tackle the ongoing GPU shortage, including CoreWeave , Lambda Labs , Foundry Technologies and Together AI , said Bessemer’s Robinson.
“Right now there's a huge imbalance between the supply and demand of AI in terms of compute and chips – and a huge opportunity for startups,” he said. “Because if you are Microsoft Azure or Amazon AWS, how likely are you to totally restructure your data centers for AI-specific workflows when you have a P&L to manage?”
What areas of AI infrastructure are you paying attention to? Let us know in the comments.
Here’s where we bring you up-to-speed with the latest advancements from the world of AI.
Catch up on the tech headlines you may have missed this week and what our members are saying about them on LinkedIn.
Here’s keeping tabs on key executives on the move and other big pivots in the tech industry. Please send me personnel moves within emerging tech.
Thanks for reading. Pitch me the interesting investors, founders, ideas and companies powering emerging technologies like AI. Share your feedback and follow me on LinkedIn for other tech updates.
Business Development Manger
10 个月Top 7 Microsoft 365 Recovery Considerations Join Our Webinar: 28 November 2023 at 11 AM (GMT) https://bit.ly/47vWB2b, #microsoft365 #microsoft #365 #datasecurity #data #safety #datarecovery #databackup #microsoftoffice365
Financial Modeling Practitioner |Aspiring Trader & Analyst at eClerx | Financial Markets | Consumer Lifecycle Management & Compliance (CLMC)
10 个月Top 15 useful Al websites in 2023 1. Kickresume - Al resume builder 2. Stockimg Create images with Al - 3. LOVO - Al voice generator 4. Rytr - Write 10x faster 5. tldv Meeting notes with Al - 6. Elai - Create videos from text 7. Durable - Al website builder 8. Decktopus - Al presentations 9. Opus Clip Content repurposing 10. Saga - Al-powered workspace 11. Rows - Build better spreadsheets 12. Perplexity - ChatGPT on steroids 13. Hoppycopy - Write emails 10x faster 14. Scispace Al research assistant - 15. Cohesive - Create magical content
Communication Strategist at Career Development Centre, MREI | Content Writer & Marketer - AI, B2B SaaS, eCommerce, Personal Tech | Founder, VyasSpeaks - Comforting, Reassuring, Uplifting Content
11 个月This is hands down my favorite and the most useful newsletter on LinkedIn. For an AI and tech enthusiast like me, this quickly brings me up to speed.
Founder 360 Wellness Solutions, LLC
11 个月Not a good move to invest in cheating.