The credits can be used by early-stage start-ups for computing, storage and access to database technologies, as well as AI chips made by Amazon.
Amazon Web Services (AWS) has announced supports for early-stage start-ups worth $230m as it looks to fund the second cohort of its generative AI accelerator.
In an announcement today (13 June), the cloud division of Amazon said that the generous investment includes AWS credits, mentorship and education for start-ups working in the AI and machine learning sectors.
The top 80 that make it to its generative AI accelerator, the first cohort of which was announced last year, will get up to $1m in AWS credits as well as hands-on expertise from experts in the field. Applications for the programme opened today and will close on 19 July.
“For more than 18 years, AWS has helped more startups to build, launch, and scale their business than any other cloud provider,” said Matt Wood, vice-president of AI products at AWS, adding that most AI and machine learning unicorns “run on AWS”.
“With this new effort, we will help startups launch and scale world-class businesses, providing the building blocks they need to unleash new AI applications that will impact all facets of how the world learns, connects, and does business.”
These AWS credits can be used for computing, storage and access to database technologies, as well as Trainium and Inferentia2, AI chips made by the company. The credits can also be used on SageMaker, a fully managed service that helps companies build and train their own foundation models.
Amazon has been investing heavily in AI to stay ahead of the curve. In March, it pumped $2.75bn into AI start-up Anthropic to complete a $4bn investment commitment it made last year. It first invested $1.25bn in Anthropic last September, when it announced that AWS will be the primary cloud provider for the company.
And just last month, the e-commerce giant posted strong financial results in its latest quarterly earnings report with net sales up 13pc and operating income from AWS nearly doubled.
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