Cost Generative AI Technology Vertical 
VC Cafe
JUNE 26, 2023
In December 2022, just a month after the introduction of ChatGPT, Google understood the risk as well and launched a ‘code red’ – a company-wide emergency effort to tackle generative AI and come back with an adequate response to ChatGPT and Microsoft Bing.
VC Cafe
MARCH 1, 2024
In addition, founders thinking about starting a company can be overwhelmed by choice, as there are so many problems to tackle with technology, but it could be comforting to know that investors are interested in those areas in the first place. Robotics – Generalizing automation, thanks to the convergence of AI software and hardware.
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VC Cafe
SEPTEMBER 8, 2023
It’s a bit crazy to think that even a year ago, the term ‘generative AI’ was not exactly on everyone’s lips. To put things in context, let’s start with the basics: 2023 witnessed an impressive influx of $15 billion invested in generative AI startups, significantly surpassing last year’s $4 billion.
VC Cafe
MAY 21, 2024
Although GenAI is a revolutionary technology, incumbents in the infrastructure domain are aggressively investing. Many startups have created so-called “wrappers” around platforms like ChatGPT and other Gen AI technologies. Native GenAi companies should leverage that cost advantage.
VC Cafe
JUNE 2, 2024
Take for example some of Google’s recent AI results spewing ridiculous answers. Questions remain about training data/copyright, privacy, security and costs. Funding for AI startups has cooled in recent months. The case for continued AI momentum: ? Meta is working on its open source LLaMa.
Duct Tape Marketing
MAY 25, 2023
His new book Strategy in the Digital Age: Mastering Digital Transformation, explains how digital technologies enable the creation of innovative services and products. In the digital age, technology has pervasive impacts, and data plays a central role in transforming customer value propositions, operations, and the structure of industries. [02:44]
VC Cafe
JULY 7, 2023
Most of the companies that raised these mega rounds are relatively early stage in terms of product and monetisation (if at all) and about half of the companies that raised large sums, are developing LLMs, which generally requires deep pockets to cover 1) cloud costs, 2) expensive deep learning engineers 3) get the data to train the models.
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