Media reputation plays an important role in attracting venture capital investment. However, prior research has focused too narrowly on general media exposure, limiting our understanding of how media truly influences funding decisions. As informed decision-makers, venture capitalists respond to more nuanced aspects of media content. We introduce the concept of media memorability - the media's ability to imprint a startup's name in the memory of relevant investors. Using data from 197 UK startups in the micro and nanotechnology sector (funded between 1995 and 2004), we show that media memorability significantly influences investment outcomes. Our findings suggest that venture capitalists rely on detailed cues such as a startup's distinctiveness and connectivity within news semantic networks. This contributes to research on entrepreneurial finance and media legitimation. In practice, startups should go beyond frequent media mentions to strengthen brand memorability through more targeted, meaningful coverage highlighting their uniqueness and relevance within the broader industry conversation.

Large Language Models (LLMs) are a powerful technology that augment human skill to create new opportunities, akin to the development of steam engines and the internet. However, LLMs come with a high cost. They require significant computing resources and energy to train and serve. Inequity in their control and access has led to concentration of ownership and power to a small collection of corporations. In our study, we collect training and inference requirements for various LLMs. We then analyze the economic strengths of nations and organizations in the context of developing and serving these models. Additionally, we also look at whether individuals around the world can access and use this emerging technology. We compare and contrast these groups to show that these technologies are monopolized by a surprisingly few entities. We conclude with a qualitative study on the ethical implications of our findings and discuss future directions towards equity in LLM access.

Of the 70 product categories tracked by PitchBook, only 21 are on pace to grow in VC funding in 2022, driven by leading vertical applications including sales & marketing, information security, and drug discovery. Our Q2 update of AI & ML Emerging Tech Research includes updated market size estimates and spotlights on private companies, including Databricks and DataRobot. The report also tracks major industry trends--covering AI struggles in code completion, developments in streaming data pipelines, and how synthetic data is being used to simulate data for robotics and autonomous vehicle training.

Business technology investment no longer follows a predictable path to growth. The global venture capital (VC) investment in artificial intelligence (AI) was close to its peak in 2021 reaching $22.3 billion, according to the latest worldwide market study by ABI Research. This is just $400 million shy of the historical high of $22.7 billion recorded in 2019. Compared to the $15 billion recorded in 2020, the market made a remarkable recovery, with a 48.5 percent year-on-year growth. Will the future AI marketplace return to stable growth, or will it remain volatile?

Chips to perform AI inference on edge devices such as smartphones is a red-hot market, even years into the field's emergence, attracting more and more startups and more and more venture funding, according to a prominent chip analyst firm covering the field. "There are more new startups continuing to come out, and continuing to try to differentiate," says Mike Demler, Senior Analyst with The Linley Group, which publishes the widely read Microprocessor Report, in an interview with ZDNet via phone. Linley Group produces two conferences each year in Silicon Valley hosting numerous startups, the Spring and Fall Processor Forum, with an emphasis in recent years on those AI startups. At the most recent event, held in October, both virtually and in-person, in Santa Clara, California, the conference was packed with startups such Flex Logix, Hailo Technologies, Roviero, BrainChip, Syntiant, Untether AI, Expedera, and Deep AI giving short talks about their chip designs. Demler and team regularly assemble a research report titled the Guide to Processors for Deep Learning, the latest version of which is expected out this month.

Momentum is building in the drone industry according to latest data, drone industry insight (DII) has revealed. Venture capitalists are putting significant funding into the segment as world leverage on technologies and related infrastructure. According to the its 2019 report, DII stated that the global market will grow from $14 billion in 2018 to over $43 billion in 2024 at annual average growth rate of 20.5%. Specifically, the drone market which has been evolving, has become clearer as VC funding is focused mainly on drone delivery, security, and mining. This was a year on year growth of 67% with venture capitalist funding standing at $930 million.

VC funding and M&A deal activity in the AI space remained inconsistent in both value and volume terms during Q1 2019 – Q4 2019. However, despite the fluctuations, both VC and M&A deal volume showcased an increase in Q4 2019 compared to Q4 2018. In contrast, VC and M&A deal value declined in Q4 2019 compared to Q4 2018. Aurojyoti Bose, Lead Analyst at GlobalData, comments: "AI start-ups have been getting lots of VC investors' attention for the past few years. However, the growth in deal value has slowed recently, which seems to be a short-term phenomenon with long-term prospects likely to remain positive."

As we embark on the next decade of innovations in AI, Daniel Pitchford looks back at the five biggest industry milestones of the 2010s, how they impacted investment in the sector and how they've shaped the advance of technology. The 2010s will be known for the advent of one of the most powerful technologies on the planet – Artificial Intelligence. Over the next decade, as more funding is made available for its development and it becomes more accepted by companies and consumers alike, it is worth reviewing some of the major milestones over the last decade that have made this advancement possible. The game is on, Watson: IBM's Jeopardy triumph The first major milestone of AI hitting the mainstream was when IBM's "super-computer" Watson beat long-standing Jeopardy champions Ken Jennings and Brad Rutter in 2011. Watson won the $1m TV game show with $77,147, leaving Jennings and Ruttner far behind at $24,000 and $21,600 respectively.

At 11:59 p.m. on New Year's Eve 2009, the world held its collective breath. The anticipation and excitement of a new year and new decade were tempered by the fears of the bottom falling out on the financial markets again. The U.S. economy was on the brink of disaster. The financial markets were unstable, and unemployment was over 9%. I remember sitting in my office in Boston at the height of the crisis when one of the executives from Silicon Valley Bank (SVB) called to say, "we need to meet urgently."

Artificial intelligence (AI) startups in China raised nearly $5 billion in venture capital (VC) funding in 2017, overtaking their US counterparts in yet another part of the AI race, according to a Monday report from ABI Research. Overall investments in AI startups increased 150% globally year-over-year in 2017, growing from $4 billion in 2016 to $10.7 billion last year, the report found. AI startups in the US raised $4.4 billion from 155 investments. Meanwhile, Chinese startups raised $4.9 billion from only 19 investments, indicating a focus on mature AI applications with strong commercial viability and successful use cases, the report noted. SEE: IT leader's guide to the future of artificial intelligence (Tech Pro Research) The Chinese government has also made AI a priority, laying out a major policy plan calling for the nation to become the world's primary AI innovation center by 2030, attracting more investment in AI startups, the report noted.