We’re thrilled to share that Caylent has been named Company of the Year – Artificial Intelligence at the 2025 Globee® Awards for Technology! 🏆
Huge thanks to our team, customers, and partners for making this possible! 🚀
Learn more 👉 caylent.com/blog/caylent...
We’re headed to the Big Apple! 🍎
Caylent is a Bronze Sponsor at the AWS Summit New York on July 16, and we can’t wait to connect, collaborate, and chat about all things cloud!
But that’s not all, we’re also hosting an exclusive Networking Reception with AWS, Monday, nOps, Relutech, and Glean!
Our AI Fireside Chat in Seattle was an evening to remember! 🔥
Caylent's CTO, Randall Hunt, joined industry leaders from AWS, CrowdStrike, and Anthropic for an insightful discussion on AI innovation and shared actionable strategies on how to drive organizational success. Thanks to everyone who came!
We’re proud to share that Caylent has achieved the AWS Machine Learning Competency! 🎉✨
This designation recognizes our deep expertise in delivering ML solutions on AWS.
Learn more about how we've helped our customers with ML: caylent.com/case-study/u...
In our new blog, Caylent’s Jeremy Yelle shares how to use Amazon Web Services (AWS) Step Functions and ASL to solve the "24 Game" mathematical puzzle. Through this demonstration, learn how ASL enables seamless orchestration of Lambda functions, making it easier to chain operations: hubs.li/Q036jqgZ0
Automatiq partnered with Caylent to migrate from GCP BigQuery to an AWS Data Lakehouse.
Learn how implementing this scalable solution enabled real-time decision-making & improved analytics capabilities, while saving the company over 30% in operational costs: caylent.com/case-study/a...
Is DeepSeek’s R1 worth the hype?
In our new blog, Caylent's CTO @randall.dev examines R1, from its computational cost savings and open-source flexibility to its privacy concerns and technical constraints: caylent.com/blog/evaluat...
If not friend, why friend shape?
Also no.
I guess Delta is stuck with me until 2031.
Have a great holiday
Why should we *not* build new data centers?
The idea that these data centers are metabolizing the planet is hyperbolic. Agree or disagree?
Are you intentionally misconstruing my point to create a strawman?
> "What is happening all around us is the creation of a technical infrastructure that is directly competing with humans for basic resources like water, energy, and land-pushing the limits of a planet already under strain."
I disagree with this quote re: energy and land. Water maybe.
The original supposition is that AI workloads are metabolizing the planet. I disagree that AI workloads are a significant driving factor of energy consumption globally. No one is claiming GPU hungry workloads are less energy intensive. The discussion is what portion of consumption does it represent?
When faced with evidence and hypothesis, if your general activity is to go ad hominem and attack the integrity of the person you disagree with, then you kill any form of discussion.
I'm open to being wrong, are you also open to being wrong? Do you hold your opinions or do they hold you?
I will be direct.
When the evidence doesn't support your priors, maybe you should reexamine your assumptions?
Across hyperscalers and smaller operators there is no one number that is correct. The error bars are massive. Anyone claiming to have a perfect idea of the numbers is likely incorrect.
Yes. I worked:
1) at AWS
2) on the Pleiades supercomputer
3) on infra for PyTorch and model training at Meta across on prem and AWS
4) on prem DCs for SpaceX
5) currently for several customers who run large ML workloads across AWS and on prem GPU farms
I am an expert in this field and I am explaining to you that it is difficult to quantify due to a lack of public data.
One can hypothesize though and the fact is that power hungry GPU based workloads are not as widely deployed as overall data center capacity. At least not yet.
You're right I shouldn't have used an overloaded (pun intended) term there.
Workload here refers to load in the electrical sense
The term workload here is not workload in a devops sense but workload in an electrical sense
I'm not saying it's zero or meaningless to think about these things... but it's not metabolizing the world. That's a statement that sounds good but has little to no backing in actual numbers. Would 2% of global power usage be the first target for efficiency gains?
Here's my hypothesis:
Total large GPU deployments in data centers
-----
Total # of data centers
My bet is the denominator is *much* bigger than the numerator. There are fixed power costs for each of the DCs in the denominator that don't go down.
I don't? What evidence would you use to support that conclusion?
1. Building data centers takes time.
2. Not all data centers have massive GPU deployments, most around the world basically function as CDNs.
Huh?
It's difficult to calculate AI's energy impact.
By some accounts, AI workloads account for ~12% of all data center workloads.
Data center workloads currently account for ~2% of global electricity usage.
We should pursue renewable or nuclear power instead of voting against it in NAMER and EMEA.
Anthropic and OpenAI supposedly respect the robots.txt file for crawl prevention as well.
The thing is search engines and data crawlers aren't asking for anything. They're just doing it. The ninth circuit thinks that's not a violation of any existing law. It might be morally wrong but legal.
It's been debated in the US court system numerous times. Look at the web scraping cases over the last 15 years. Web scraping is fully legal in the 9th circuit now 🤷♂️?
CFAA, DMCA, CCPA, and other acts have all lost cases in courts.
It's going to be a wild few years in the courts.
Typically yes, not every implementation requires this though.
