AIBP ASEAN B2B Growth

There are a few genuinely interesting technical points - particularly the behavioral fingerprinting approach to distinguishing crypto mining from industrial loads, and the contrast between underground cable diagnostics (internal signals like partial discharge) versus overhead line challenges (external environmental factors). However, large portions of the episode are padded with generic digital transformation messaging and AI-human collaboration platitudes that any B2B operator has already heard repeatedly.

“bitcoin mining typically shows a very, I would say flat continuous load profile. Uh, it's a 247 operation with minimal uh, variation. Uh, whereas the legitimate loads for industry, it follows, uh, some cycles, some fixed operational cycles.”

“underground cables uh, are buried underground. So so they are hidden. So we really rely on uh diagnostic data such as our insulation resistance, our partial discharge is uh more deep analytics problem to understand the internal degradation.”

The framing of energy behavioral signatures as the detection mechanism for illegal mining is a moderately fresh angle, and the 'eureka moment is adoption not discovery' reframe is a reasonable contrarian nudge. Everything else - AI augments rather than replaces, start with the problem not the data, external vs internal drivers - recycles ideas that circulate everywhere in enterprise AI discourse.

“we are not just detecting how much energy is used or the energy consumption alone. We are also identifying uh, the energy behavior or the behavioral signatures of energy.”

“innovation happens when for example, our solution would flag a risk, uh, or AI solution can uh, detect any uh, anomaly. Uh, and whenever someone on the ground changes their decision or action because of the AI”

Jundi is a genuine practitioner who has scaled analytics capabilities nationwide at a major national utility and has a concrete award-winning project to point to, which distinguishes him from career thought-leaders. However, he is a mid-level technical lead rather than a C-suite or VP-level operator, and the depth of insight in the transcript reflects that - he is credible and relevant but not an unusually senior or uniquely positioned voice.

“over the past few years I've been involved uh, in scaling, uh, up these capabilities, uh, nationwide, uh, in particular, uh, for our critical assets, for example, the underground cable.”

“last year we won the award for uh, underground cable project.”

The guest uses correct technical vocabulary - SAIDI, partial discharge, insulation resistance, harmonics, drone image analytics - and distinguishes asset types with some precision. But there are zero concrete numbers: no cost savings figures, no prediction accuracy percentages, no scale of assets monitored, no timeline for projects, and no named technology vendors or dollar figures, leaving most claims unverifiable and abstract.

“system average uh interruption duration index, this id uh calculation”

“power quality signals like harmonics, customer profile versus the actual usage, and also in terms of location and historical patterns”

The host repeatedly echoes the guest's answers back as affirmations rather than probing deeper, misses every opportunity to request specific numbers or outcomes, and asks formulaic questions (AI replacing humans, Eureka moments) that produce rehearsed responses. The bitcoin mining angle was genuinely interesting but was dropped after a single surface-level follow-up instead of being pushed for operational detail.

“Understand? So it's really looking at like the business value, the impact, the outcomes that your AI insights can drive for the business.”

“So you basically need more people, whether people or technology on the ground for you to look after all these exposed overhead lines.”