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AI Execution Speed Outpaces Organizational Decision Quality · AI Conversations

• 0:00
  SPEAKER_01: So uh I've been thinking about this idea of like the bottleneck shifting.
• 0:05
  SPEAKER_01: I mean, we've always talked about can we build it, right?
• 0:09
  SPEAKER_01: That's been the big question for so long.
• 0:13
  SPEAKER_01: And now, this piece from Atlassian, it's really making me think that the question isn't can we build it anymore?
• 0:21
  SPEAKER_01: It's like, did we choose the right problem?
• 0:24
  SPEAKER_00: Right.
• 0:25
  SPEAKER_00: The the core issue is that AI is just accelerating production, like incredibly fast.
• 0:30
  SPEAKER_00: But then, as you say, the headline from Atlassian, it points to this fact that product teams are shipping faster, but customers aren't necessarily receiving value faster.
• 0:41
  SPEAKER_00: Does that make sense?
• 0:42
  SPEAKER_01: Yeah, it does.
• 0:44
  SPEAKER_01: It's almost like we've solved the how fast can we make widgets problem, but not the are these the right widgets problem.
• 0:52
  SPEAKER_00: Exactly.
• 0:53
  SPEAKER_00: So the bottleneck, it's shifting from execution to um to judgment.
• 0:58
  SPEAKER_00: That's a very important distinction, I think, for both leadership and you know, AI governance.
• 1:03
  SPEAKER_01: I mean, it really highlights this human-governed AI signal, which is getting stronger.
• 1:09
  SPEAKER_01: We're seeing it pop up in so many places.
• 1:12
  SPEAKER_01: Like with Pi Durable, agents are being designed to operate indefinitely, survive failures, and then crucially allow multiple humans to steer them.
• 1:23
  SPEAKER_00: Yeah, and then when you look at agent transcripts, organizations are being told to preserve what agents were asked, what they did, and how they behaved.
• 1:32
  SPEAKER_00: It's not just about the output anymore, it's about the process.
• 1:36
  SPEAKER_01: Right.
• 1:37
  SPEAKER_01: And then cleft decision models, they're moving beyond just generation.
• 1:42
  SPEAKER_01: They're gathering context, deciding, acting.
• 1:45
  SPEAKER_01: It sounds like AI is moving from just producing outputs to uh participating in decisions and persistent action.
• 1:54
  SPEAKER_00: That's it.
• 1:55
  SPEAKER_00: And once that happens, governance can't just sit outside the workflow as policy documentation.
• 2:01
  SPEAKER_00: It has to be part of the architecture.
• 2:03
  SPEAKER_01: So this really strengthens what you've been calling the authority OS argument then.
• 2:08
  SPEAKER_01: Yesterday we were talking about infrastructure, like containers, state, all that.
• 2:14
  SPEAKER_01: And today, it's one layer higher: steering, decision making, evidence.
• 2:19
  SPEAKER_01: It's a very clean architecture that's emerging.
• 2:22
  SPEAKER_00: Yeah, it is.
• 2:23
  SPEAKER_00: You have the infrastructure layer, which is can the system run?
• 2:27
  SPEAKER_00: Then the agent layer, can the system reason and act, and then above that, the decision layer, what should it do, and then the authority layer, what is it permitted to do?
• 2:39
  SPEAKER_00: And finally, the recovery layer, what happens when it should stop, fails, or crosses a boundary.
• 2:45
  SPEAKER_00: It's a very coherent story for this body of work.
• 2:49
  SPEAKER_01: It really is.
• 2:50
  SPEAKER_01: And there was one line in the newsletter that really stuck with me.
• 2:54
  SPEAKER_01: The recommendation to preserve agent session transcripts.
• 2:57
  SPEAKER_01: I mean, it sounds mundane, but it's not.
• 3:00
  SPEAKER_00: No, it's not.
• 3:02
  SPEAKER_00: Those transcripts can become part of what you've been calling evidence and receipts.
• 3:06
  SPEAKER_00: If an agent influences a hiring decision or a pricing decision, or a financial action, the organization may eventually need to reconstruct.
• 3:16
  SPEAKER_00: What did the agent know?
• 3:17
  SPEAKER_00: What was it instructed to do?
• 3:19
  SPEAKER_00: What decision did it make?
• 3:21
  SPEAKER_00: What action followed?
• 3:22
  SPEAKER_00: Who had authority, who intervened, what happened afterward.
• 3:26
  SPEAKER_01: Yeah, a simple transcript isn't a full governance receipt yet, but it's moving organizations towards this idea that agent activity must be reconstructible.
• 3:37
  SPEAKER_01: And that's very close to your lineage plus evidence refs plus prior receipt architecture.
• 3:43
  SPEAKER_00: Precisely.
• 3:44
  SPEAKER_00: And this also ties into the workforce intelligence signal, the death of coding by hand discussion.
• 3:50
  SPEAKER_00: I wouldn't interpret it as software engineers disappearing.
• 3:54
  SPEAKER_01: Right.
• 3:54
  SPEAKER_01: It's not that.
• 3:56
  SPEAKER_01: It's more about the role shifting.
• 3:58
  SPEAKER_01: We, what used to be write every line, is now more specify, direct, inspect, validate, integrate, govern.
• 4:07
  SPEAKER_00: Exactly.
• 4:08
  SPEAKER_00: AI doesn't just automate tasks, it changes where expertise sits in the workflow.
• 4:14
  SPEAKER_00: And the market signal is clear.
• 4:16
  SPEAKER_00: Companies may still pay a premium for engineers who can build AI native products, even as manual coding declines.
• 4:23
  SPEAKER_01: So the valuable worker becomes less the person who can perform the most production, and more the person who knows what production should occur, how to direct it, and how to determine whether the result is acceptable.
• 4:38
  SPEAKER_01: That has huge implications for career insurance, too.
• 4:42
  SPEAKER_00: It absolutely does.
• 4:44
  SPEAKER_00: This is another reason traditional skill inventories are becoming inadequate.
• 4:48
  SPEAKER_00: Someone can say, I'm a software developer, but career insurance should increasingly ask: can you supervise AI-generated work?
• 4:56
  SPEAKER_00: Can you validate outputs?
• 4:58
  SPEAKER_00: Can you translate business intent into agent instructions?
• 5:02
  SPEAKER_00: Can you recognize failures?
• 5:04
  SPEAKER_00: Can you document decisions?
• 5:06
  SPEAKER_00: Can you operate across human machine workflows?
• 5:09
  SPEAKER_01: Yeah, those capabilities increase career portability because they sit above individual tools.
• 5:17
  SPEAKER_01: The person whose value is entirely tied to production mechanics faces greater exposure than the person who can direct, validate, and govern production.
• 5:27
  SPEAKER_00: It could become a measurable component of what you've called the career insurance risk index, um AI supervisory capacity.
• 5:35
  SPEAKER_00: Not simply can you use AI, but can you remain economically valuable when AI performs an increasing share of your current tasks?
• 5:44
  SPEAKER_00: That's a much stronger question.
• 5:46
  SPEAKER_01: It's a really strong question.
• 5:48
  SPEAKER_01: And it feels like there's a whole thought leadership piece sitting inside this issue.
• 5:52
  SPEAKER_01: I mean, building it around at Lassian's point.
• 5:55
  SPEAKER_01: AI made work faster.
• 5:57
  SPEAKER_01: Why didn't value arrive faster?
• 5:59
  SPEAKER_00: Yeah, the thesis being that the first stage of enterprise AI focused on accelerating production.
• 6:05
  SPEAKER_00: The next stage will expose a harder constraint.
• 6:08
  SPEAKER_00: Organizations cannot automate their way past poor judgment.
• 6:12
  SPEAKER_01: Right.
• 6:13
  SPEAKER_01: So you have execution speed, where AI allows organizations to produce more rapidly, then decision quality, more output doesn't guarantee better choices, and then authority quality, even a good decision requires clarity about who or what is authorized to act.
• 6:32
  SPEAKER_00: Exactly.
• 6:33
  SPEAKER_00: And the closing line, I think, should be the future of AI advantage may belong less to organizations that move fastest and more to those that know what deserves speed, what requires judgment, and what must remain under human authority.
• 6:48
  SPEAKER_01: That's a powerful insight.
• 6:49
  SPEAKER_01: So it's not just about building faster, but about building wisely.
• 6:54
  SPEAKER_00: That's it.
• 6:54
  SPEAKER_00: It's about recognizing that gap where governance becomes truly operational.