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15 results for “ai costs”
And more for business owners, we found the strange distortion where they were trying to market products like spend more money, earn more points, but every business owner I ever met actually wanted to spend less and be more profitable. So, we just try
6:35 / 7:52
from transcript6:35 – 7:52
We track the cost to run artificial analysis intelligence index for each bucket of intelligence index scores, and each bucket you just see the line go down really really quickly, and actually go down more quickly to each new level of intelligence tha
1:00:05 / 1:01:07
from transcript1:00:05 – 1:01:07
So these can be from AI research labs themselves, to farms, to nonprofits, mom and pop shops, and this is across both credit card data as well as bill payment data. So it's a pretty good subset of it. And what we've seen is maybe twofold. First, just
7:25 / 8:31
from transcript7:25 – 8:31
Like, my my my view is that, like, we'll have, like, really awesome intelligence before we have like, AGI before we have it permeate throughout the economy. And this is sort of why that reason is. Right? GPT three was trained in, what, 2020, 2021, an
3:21:39 / 3:22:47
from transcript3:21:39 – 3:22:47
models, which are very, very cheap. Yeah. Like, my my conservative, statement is normally like 100%. Yeah. But in fairness, this slide, like I we were actually saying before the podcast, right, is like maybe six months old now, and it's conceptually
59:19 / 1:00:35
from transcript59:19 – 1:00:35
memory capacity differential between NVIDIA Rubin and and, Positron as well. Is okay. At the top of the chat, you said currently, AI workloads are, like, 65% training, 35% inference. Yes. I presume that was ninety ten a few years back. Yeah. A couple
42:46 / 43:46
from transcript42:46 – 43:46
we are pushing the limits of what we can do with chips, the needs of next generation models, how fast TSMC is able to produce. Yeah. I mean, even that is ambitious. Right? If you think you're gonna get five years of depreciation on AI chips, five yea
3:38:13 / 3:39:19
from transcript3:38:13 – 3:39:19
faster and simpler. So help your team get great done with Miro, and check out miro.com to learn more. That's miro.com. Thank you, Miro. Yep. And you get some pretty big numbers right now. It's $1,000,000,000,000. I see that across. Yeah. Developed wo
11:10 / 12:43
from transcript11:10 – 12:43
So this newly manufactured intelligence will spawn a new chapter of unprecedented productivity and development, and that will serve to improve human quality of life. So the IDC estimates that AI will generate $20,000,000,000,000 in economic impact by
33:00 / 34:03
from transcript33:00 – 34:03
People are spending a lot on these models. They're presumably doing this because they're getting value from them. You can maybe argue, like, oh, well, I don't think that value is real. I think people are just playing around. Whatever. But, like, what
0:00 / 1:54
from transcript0:00 – 1:54
insight into the quality, durability of this revenue. There's not a single good example that we can find of sustained positive margin expansion and impact of AI inside of a true corporate enterprise that is not right now a small test. There's not. So
32:14 / 33:15
from transcript32:14 – 33:15
is ending up. Having said that, there's so many valuable bets to be placed at the application layer to just build stuff. But the dilemma is you could do it in a very capital efficient way. So if someone wants to say, I wanna put $10,000,000,000 to wo
30:44 / 31:45
from transcript30:44 – 31:45
Today, that's one of the number one topics in those forums. Maybe the number one topic. Right? Because I think we figured out how with high powered reasoning models and very large context windows, we can have single tasks that suddenly cost dollars.
29:15 / 30:25
from transcript29:15 – 30:25
of revenue flowing from the build out of AI. So, if you assume 20% margins and a 22 times PE, that translates into 35,000,000,000,000 of new market cap. There's been about 24,000,000,000,000 of new market cap that's been pulled forward. Now we could
36:45 / 37:48
from transcript36:45 – 37:48
Are we building towards the biggest economic boom in human history or the fastest collapse? Right now, AI labs are burning billions on compute. Anthropic just built a data center that uses as much power as Indiana state capital and Microsoft's planni
2:15 / 3:25
from transcript2:15 – 3:25
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