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Scaling AI Startups – Hacker Noon

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Not so long ago, AI startups were the new shiny object that everyone was getting excited about. It was a time of seemingly infinite promise: AI was going to not just redefine everything in business, but also offer entrepreneurs opportunities to build category-defining companies. A few years (and billions of dollars of venture capital) later, AI startups have re-entered reality. Time has come to make good on the original promise, and prove that AI-first startups can become formidable companies, with long term differentiation and defensibility. In other words, it is time to go from "starting" mode to "scaling" mode.


29 Of The UK's Most Exciting AI Startups Race To Scale

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The UK is currently third in the world for raising investment in AI and second for the number of AI companies in operation during 2019 (Crunchbase data). Still, 89% of the AI ecosystem in the UK consists of startups with 50 or fewer employees. Starting is not an issue, scaling is. "For the UK to maintain its authority in AI, we need to nurture scalable, globally-competitive, homegrown AI companies that solve real problems. Yet, the pool of AI-focused companies that achieve this beyond Series A remains slim, despite the hype, and the path to scale is uniquely challenging," comments Harry Davies, Applied AI Lead at Tech Nation.


29 Of The U.K.'s Most Exciting AI Startups Race To Scale

#artificialintelligence

The U.K. is currently third in the world for raising investment in AI and second for the number of AI companies in operation during 2019 (Crunchbase data). Still, 89% of the AI ecosystem in the U.K. consists of startups with 50 or fewer employees. Starting is not an issue, scaling is. "For the U.K. to maintain its authority in AI, we need to nurture scalable, globally-competitive, homegrown AI companies that solve real problems. Yet, the pool of AI-focused companies that achieve this beyond Series A remains slim, despite the hype, and the path to scale is uniquely challenging," comments Harry Davies, Applied AI Lead at Tech Nation.


AI Will Change The World...If Investors Catch Up

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It's hard to believe that a mere 50 years ago we still relied on humans to manage telephone switchboards. What once required an army of people to operate at scale was quickly replaced by computers powered by microprocessors. Nowadays, most of us can't imagine a world where humans are required to make a telephone call. Data science is in the midst of a similar revolution. While the mathematical tools to make predictions from data have existed for centuries, and the algorithmic ones for several decades, all have required humans to manage the data inputs and interpret/iterate on the outputs.


AI First, the Overhype and the Last Mile Problem

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AI is hot, I mean really hot. Consumer companies like Google and Facebook also love AI, with notable apps like Newsfeed, Messenger, Google Photos, Gmail and Search leveraging machine learning to improve their relevance. As a founder of an emerging AI company in the enterprise space, I've been following these recent moves by the big titans closely because they put us (as well as many other ventures) in an interesting spot. How do we position ourselves and compete in this environment? In this post, I'll share some of my thoughts and experiences around the whole concept of AI-First, the "last mile" problems of AI that many companies ignore, the overhype issue that's facing our industry today (especially as larger players enter the game), and my predictions for when we'll reach mass AI adoption. A few years ago, I wrote about the key tenants of building Predictive-First applications, something that's synonymous to the idea of AI-First, which Google is pushing.