AI: State of Industry and Investment | Aravind Kandiah, Jun Wakabayashi, John Homer Alvero - E717
episode
BRAVE Southeast Asia Tech: Singapore, Indonesia, Vietnam, Philippines, Thailand & Malaysia Startups, Founders & Venture Capital VC (English)
29 min
8 chapters
transcribed 19 days ago
Transcript
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Transcript generated automatically by AI and may contain errors.
What is the current reality of enterprise AI adoption?
This big vision, everybody's gonna be out of a job. What's the reality today from your perspective on enterprise EI adoption?
Now implementing what they call measured intelligence to track token costs cause yes, they are skyrocketing and you're kingdom horror stories of people accidentally spending, I don't know, like five million dollars a month or something like that just on token output.
They're spinning up the most expensive machine to like cook rice. Like using nuclear power to cook rice. That's kind of like what we're doing here now.
People plays a big part of the adoption of AI. They should be able to trust the tools for them to scale the use of AI in an organization. Hello everyone. So thank you so much. We're here to have uh exciting conversation about enterprise adoption of AI. And one thing we've done is we as a group have discussed the questions and everything and we promised to be a little bit contentious, a little spicy, uh and do our best. So on that note, maybe we'll what we'll do is we'll do a round of introductions. I prepared some questions. And at the end of the panel, towards the end, we'll have some time for you to ask questions as well. So if any questions, feel free to stay ahead and uh raise your hand. Uh so for me, myself, I'm Jeremy Howe.
Um I'm actually uh CEO for medical aesthetics group. Uh and my hobby that I do is that uh I run Southeast Asia. number one tech podcast. Uh shout out to A Joe for being an early supporter and co host of the podcast. Woo uh and uh happy to talk about this panel, so please go ahead. Uh
Awesome. Hey guys, I'm Ardit, CTO and co-founder over at Bifrost. What we do at Bifrost is we recreate the world, the physical world, in a simulation. And then we use that simulation to test robotic systems to figure out how they don't fail and where they will fail. So imagine testing ten thousand possible different futures and then you can figure out, oh, it's gonna fail when it meets a very specific scenario. Right. This way robots don't fail in the real world, they fail in our virtual worlds. Right. We serve everyone from folks like NASA to ban spacecraft on Mars all the way to folks like US Air Force to help prevent certain conflicts to folks building humanoid robots, houses and factories and science labs, etc.
Great. Uh hey guys, my name is June. Welcome by Ashi. Uh my headline title is uh venture principal at Appworks. We're early stage funds and startup accelerator, one of the largest and most active in the regions. Um actually my day job is actually being partnerships at this uh uh company called NECA that AI. We essentially do ecocentric data capture to help train physical AI models.
Hello, I'm John. I'm with Converged ICT Solutions. I head the AI engineering team of the company. So uh we're cons we are the one of the largest uh piper broadband pipers here in the organs. And we are transitioning from a tech co to our uh uh from a telco to a taco company. Fantastic. So, you know, I think what's in front of us is really about you know know, the age of AI and enterprise adoption and there's this hype, you know, it's like billion dollar companies with one employee or no employees, um, you know, this big vision, everybody's gonna be out of a job, etc. What's the reality today from your perspective on enterprise AI adoption? Yeah, so Uh at least from where I am now, uh the reality in the enterprise is that organizations are careful about uh adopting AI.
Uh specifically for mature organizations where uh governance and uh six cybersecurity is a strong practice, right? Those uh teams will ask about the safety and the security uh of uh the use of uh AI. Right.
How do pilots vs. forced‑function AI projects differ for enterprises?
So we wanna move fast and we wanna adopt AI, but we're also concerned about our data. We wanna protect our customers' data. We want to make sure that the data of our customers is not used for training uh the next generation of uh of models, right? So We're pilotings, right? We want to move fast and we're also careful at the same time.
I
would
add to it. Enterprise is a very fake word. And actually there's like a whole spectrum of enterprises.
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Chapters
8 chapters
1
What is the current reality of enterprise AI adoption?
0:00–3:55
2
How do pilots vs. forced‑function AI projects differ for enterprises?
3:55–8:06
3
Why are engineers now the biggest consumers of AI token spend?
8:06–11:53
4
What are the main organizational blockers to scaling AI?
11:53–15:10
5
How should companies measure AI ROI and control token costs?
15:10–18:54
6
When is it better to build a custom AI solution versus buying one?
18:54–23:19
7
What role does distribution play in creating a defensible AI moat?
23:19–27:11
8
What advice would the panel give their younger selves about AI strategy?
27:11–29:53
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