Everyone Has AI. Who Has the Advantage?

Day 1 · Oct 13 Day 1 digest · More from this day

With AI access now universal, Sid Pednekar argues that advantage comes from judgment plus three pillars: design the right problem, build shared capabilities, and compound the advantage.

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TL;DR

• Only about 37% of surveyed enterprises report a positive EBIT contribution from AI, and only about 6% qualify as high performers (about 5% EBIT contribution). Access is widespread; impact is uneven.
• In an era where intelligence has become cheap and abundant, the scarce resource is judgment: choosing what matters and how to act when there is no single obvious answer.
• Leading firms obsess over the right problem instead of the tool. Company A asks “why are our material signals reaching us too late?”; Company B bolts a summarization copilot onto the existing process — a “faster horse” that loses context.
• Leaders concentrate more than 80% of AI investment in a few high-value, end-to-end workflows, and they redesign work rather than just automate it (75% of high performers redesign workflows versus 25% of the rest).
• Shared capabilities matter: converge technology and business roles, standardize the core and personalize the edges, and modernize infrastructure built for humans, not agents (71% of organizations are modernizing; 23% invest 6 to 10% of annual revenue in it).
• Advantage compounds when individual learnings — with their evidence — are fed back into the system as tools, context, and monitoring flywheels.

Key lessons

1. Quantify the AI gap before planning. Recent studies show only about 37% of surveyed enterprises see a positive EBIT contribution from AI, and only about 6% qualify as high performers (about 5% EBIT contribution). Earlier-year studies show about 66% report productivity gains, but only about 40% and 20% see cost reductions and revenue growth.
2. Treat judgment as the scarce asset. Intelligence (software, analytics, recommendations, reports) is now abundant, so more answers do not create more value. Judgment — the creativity to pick possibilities, the taste to know what good looks like, the weighing of trade-offs — is finite. The core question: where is our best judgment worth spending?
3. Start with the problem, not the tool. Company B asks “where can we add AI to the analyst’s work?” and pipes earnings transcripts through a summarization firehose. Company A asks “why are our material signals reaching us too late?” and “where is our bottleneck?” Studies show leaders spend attention on fewer, higher-quality decisions.
4. Concentrate investment in the highest-value workflows. Company B launches the most AI pilots (a summarization bot here, a copilot there, a data retrieval bot here). Company A aligns engineering, analytics, AI capacity, and judgment toward end-to-end research pipelines. Research shows leaders put more than 80% of investment into these high-value use cases. Separate real value generation from innovation theater and productivity theater.
5. Redesign the work, do not just automate it. Company B speeds up summaries, model updates, and recommendations to the PM. Company A has the analyst define an investment thesis, then monitors continuously for evidence that refutes or supports it, investigates exceptions, and deep-dives select cases. High performers actually redesign workflows at 75% versus 25% for the rest.
6. Standardize the core and personalize the edges. Example: replace one-off data-science workflows with a shared core of data sets, semantic layers, and knowledge of trade-offs, gaps, and what worked. Each new person becomes a plug-and-play module that stands on the shoulders of predecessors. Ask: what should the next person not have to rebuild?
7. Modernize infrastructure for agents. The enterprise was built for one human interacting with one application; agents take context, deliberate, and act across workflows and applications. Studies show 71% of organizations are modernizing core infrastructure for AI, and 23% invest 6 to 10% of annual revenue in core system modernization. Data quality and data reuse are the common challenges.
8. Build feedback loops that compound. Ask of every failure: what can we learn that gives us an edge? Ask of every success: how do we make it part of the system? The test is “the individual learned but did the system?” Feed corrections back into the org as new tools or context, and preserve the evidence along with the learnings.

Watch this session

Sid Pednekar · 3:16:49 PM ET–3:34:48 PM ET · talk · Watch on YouTube from 06:28:57

Figures

The adoption ladder from the MC’s segment and the EBIT numbers from this talk.

Notable quotes

“Judgment is choosing what matters and how to act when there’s no single obvious answer. It’s the creativity that lets you explore what possibilities are worth exploring. It’s the taste that tells you what good looks like in your given context.” — [06:33:19 | 3:21:10 PM]

“So the question that they’re focused on is, given judgment is a finite asset, a limited capacity, the main question becomes, where is our best judgment worth spending?” — [06:34:00 | 3:21:51 PM]

“And such a workflow of being a faster horse introduces one key nuance. That is, you are losing context as you have information, I guess, reduction as part of your summarization.” — [06:35:39 | 3:23:30 PM]

“And the way they think about this is, you know, the individual learned but did the system.” — [06:45:52 | 3:33:44 PM]

Session narrative

Dr. Sid Pednekar opened by framing the talk around one question: organizations all have access to the same AI, so who actually gets the advantage? He introduced himself as an AI technology and data executive in asset management, with tenure in hedge funds, investment, and academic research, where he led strategy and built specialized teams across three verticals: optimizing efficiency, driving P&L, and reducing costs. He then set up the talk’s running device: two hypothetical asset-management companies, Company A and Company B, whose research-analyst workflows the audience follows for the rest of the talk. The session ran in the livestream’s afternoon block (3:16:49 PM to 3:34:48 PM ET), earlier than the 3:45 PM slot the published agenda listed.

The lay of the land, per recent studies he cited, is uneven impact: only about 37% of surveyed enterprises see positive EBIT contribution from AI, only about 6% are high performers (about 5% EBIT contribution), and earlier studies show 66% claiming productivity gains but only 40% and 20% seeing cost reductions and revenue growth. From there he introduced the cross-cutting idea and the three pillars. The cross-cutting idea: intelligence has become cheap, so in an abundant world the scarce resource is judgment — ubiquitous across the C-suite, software engineers, AI leaders, portfolio managers, and data scientists — and since judgment is finite, the question is where to spend it.

Pillar one, design the right problem, carried the bulk of the talk. Company B starts with the tool: it finds a summarization workflow, pipes earnings transcripts through a firehose, and calls faster summaries a win. Pednekar called this a “faster horse” that loses context through information reduction. Company A instead redesigns the workflow: it asks why material signals arrive late, where the bottleneck is, and where to focus attention. He cited research that leaders concentrate attention on fewer, higher-quality decisions, put more than 80% of investment into high-value use cases, and redesign rather than just automate (75% versus 25% for high performers versus the rest), warning against innovation theater and productivity theater. His worked example: Company A has the analyst define an investment thesis and then continuously monitors evidence that refutes or supports it, investigates exceptions, and deep-dives select cases.

Pillar two, build shared capabilities, covered the convergence of technology and business — forward-deployed engineers, forward-deployed product managers, and the inverse, forward-deployed portfolio managers — plus breaking down silos. He advocated standardizing the core and personalizing the edges, with a data-science shared core (data sets, semantic layers, trade-offs, gaps) that makes each new person plug-and-play. Because the enterprise was built for humans on single applications while agents cross workflows, he cited 71% of organizations modernizing core infrastructure and 23% investing 6 to 10% of annual revenue in it, with data quality and reuse as the common challenge. He sketched team agents as the reusable-knowledge layer: expert agents as subject-matter experts, workflow agents like Slack bots that open and close tickets, and chief-of-staff agents that track organizational state. Pillar three, compound the advantage, closed the argument: leaders mine failures for an edge, make successes part of the system, and ask whether the system (not just the individual) learned, preserving evidence with learnings and running an AI-first capacity flywheel around monitored metrics. He ended where he began: everyone has AI — who has the advantage?

Speaker details

Sid Pednekar - Head of Data and AI, Asset Management, Arcsight Capital (Millennium). Sid Pednekar, PhD, is a data, technology and AI executive who develops strategy and leads its execution, building capabilities that help organizations make better decisions and put new technology to practical use. His experience is rooted in investment management — quantitative research, alternative data, portfolio and risk analytics, and applied AI — and he holds a PhD in Computational Physics and Process Engineering. A recurring focus of his work is turning bespoke solutions into reusable capabilities with the ownership, workflows, and adoption needed to make them effective across teams.

Links: LinkedIn

Full timestamped transcript

[06:28:57 | 3:16:49 PM] Good afternoon, everyone present here.
[06:29:05 | 3:16:57 PM] It's been a great day and an absolute pleasure to be here.
[06:29:11 | 3:17:03 PM] The topic of my talk today is: everyone has AI, who has the advantage?
[06:29:21 | 3:17:13 PM] I'll be talking about what the orgs that are doing, that are leading in AI with their implementation and adoption, what are they doing differently?
[06:29:33 | 3:17:25 PM] And so before that, who am I and what's my POV?
[06:29:39 | 3:17:31 PM] So I'm Dr.
[06:29:41 | 3:17:32 PM] Sid Pednekar.
[06:29:42 | 3:17:33 PM] I am an AI technology and data exec in asset management.
[06:29:47 | 3:17:39 PM] I've spent most of my tenure in hedge funds and investment and academic research.
[06:29:54 | 3:17:46 PM] What I've done and focused on is lead strategy and build specialized teams to
[06:30:01 | 3:17:52 PM] Teams towards three main verticals: optimizing efficiency, driving P&L, and reducing costs.
[06:30:11 | 3:18:02 PM] So I'll be following the discussion along with two hypothetical companies, Company A and Company B, right in this slider below.
[06:30:22 | 3:18:14 PM] These are hypothetical, you know, with made-up workflows, but we'll be following their journey along the rest of this presentation.
[06:30:34 | 3:18:25 PM] So everyone has AI.
[06:30:36 | 3:18:27 PM] Who has the advantage?
[06:30:40 | 3:18:31 PM] Before we get started there, first let's see what the lay of the land is.
[06:30:44 | 3:18:35 PM] And recent studies have shown that only about 37% of enterprises that were surveyed are showing a positive contribution to their EBIT, and only about 6% of those would be qualified as high performers.
[06:31:03 | 3:18:55 PM] So that's about 5% contribution to their EBIT.
[06:31:07 | 3:18:58 PM] Other studies earlier in the year have shown that about 66% are saying they're having a productivity increase, but only about 40 and 20% are actually seeing cost reductions and revenue growth.
[06:31:22 | 3:19:13 PM] So as we know, access of AI is widespread, yet the impact remains uneven.
[06:31:31 | 3:19:23 PM] So the question I would ask, or how I would think about this, is really in terms of three verticals.
[06:31:43 | 3:19:35 PM] And the way I think about these three verticals should Apply to organizations and individuals alike, and it should apply to functions regardless of where you are in that organization.
[06:31:59 | 3:19:51 PM] So the three main vertical pillars that I think of are, number one, design the right problem.
[06:32:07 | 3:19:58 PM] Number two, build shared capabilities.
[06:32:10 | 3:20:02 PM] And number three, compound the advantage.
[06:32:14 | 3:20:06 PM] Now, before we dive into each of these pillars, there is an idea that I think goes through all these verticals, and I think the best leading firms are taking advantage of that and capitalizing on it.
[06:32:30 | 3:20:21 PM] As we know, throughout time, the only constant in this world has been change.
[06:32:36 | 3:20:27 PM] What's different in the AI and superintelligence era is that intelligence has become cheap.
[06:32:44 | 3:20:35 PM] Regardless of what your unit of intelligence is, whether it be software, analytics, recommendations, reports, AI is creating an abundance in all of it.
[06:32:55 | 3:20:47 PM] And as we know, more answers don't necessarily create more value.
[06:33:02 | 3:20:53 PM] So what is scarce?
[06:33:04 | 3:20:55 PM] What is the scarce resource in an abundant world?
[06:33:09 | 3:21:01 PM] What are companies taking advantage of, the leaders at least?
[06:33:14 | 3:21:05 PM] It's judgment.
[06:33:16 | 3:21:08 PM] What is judgment?
[06:33:19 | 3:21:10 PM] Judgment is choosing what matters and how to act when there's no single obvious answer.
[06:33:25 | 3:21:16 PM] It's the creativity that lets you explore what possibilities are worth exploring.
[06:33:29 | 3:21:21 PM] It's the taste that tells you what good looks like in your given context.
[06:33:34 | 3:21:26 PM] It's the decisions where you can weigh what trade-offs are worth making.
[06:33:41 | 3:21:33 PM] Judgment is ubiquitous.
[06:33:43 | 3:21:35 PM] It exists throughout your organization on a daily basis.
[06:33:48 | 3:21:40 PM] Your C-suite, the C-suite is asking questions, taking advantage of judgment all the time.
[06:33:53 | 3:21:45 PM] So are the software engineers, the AI leaders, the portfolio managers, and the data scientists.
[06:34:00 | 3:21:51 PM] So the question that they're focused on is, given judgment is a finite asset, a limited capacity, the main question becomes, where is our best judgment worth spending?
[06:34:16 | 3:22:07 PM] I'd like for you to hold on to that thought.
[06:34:19 | 3:22:11 PM] With that, we'll dive into our first pillar: design the right problem.
[06:34:23 | 3:22:15 PM] So questions, a question that the leading organizations are asking all the time is they're obsessed over what is the right problem to solve, right?
[06:34:37 | 3:22:28 PM] It's tempting to start with the tool.
[06:34:38 | 3:22:30 PM] Today we're surrounded with better models, harnesses.
[06:34:42 | 3:22:33 PM] We're surrounded with all these AI tools.
[06:34:45 | 3:22:36 PM] We're surrounded with copilots.
[06:34:47 | 3:22:38 PM] But that isn't the way about, is it?
[06:34:50 | 3:22:42 PM] At least that's not the way the leaders are approaching it.
[06:34:53 | 3:22:45 PM] Now let's go back to our hypothetical two companies.
[06:34:57 | 3:22:48 PM] Let's say they're from asset management.
[06:34:59 | 3:22:50 PM] Let's say we're tracking the workflow of two research analysts in these hypothetical organizations.
[06:35:06 | 3:22:58 PM] Well, let's say organization B starts with the tool, the copilot.
[06:35:13 | 3:23:04 PM] They're asking, where can we add AI to the analyst's work?
[06:35:17 | 3:23:08 PM] They're looking at the current existing process to say, earning transcript comes in, I discovered a summarization workflow.
[06:35:25 | 3:23:16 PM] Let me pass all of this in through my firehose.
[06:35:29 | 3:23:20 PM] Analysts get earnings summaries faster.
[06:35:32 | 3:23:24 PM] Win-win, right?
[06:35:34 | 3:23:26 PM] Well, not so much, at least not compared to the guys who are leading.
[06:35:39 | 3:23:30 PM] And such a workflow of being a faster horse introduces one key nuance.
[06:35:45 | 3:23:37 PM] That is, you are losing context as you have information, I guess, reduction as part of your summarization.
[06:35:53 | 3:23:44 PM] So what is a hypothetical organization A doing in this?
[06:35:57 | 3:23:49 PM] Well, they're obsessed in the right problem to ask.
[06:36:00 | 3:23:52 PM] They could be asking, why are our material signals reaching us too late?
[06:36:06 | 3:23:57 PM] Where do we focus the attention, the AI, the bandwidth, the capability on?
[06:36:11 | 3:24:02 PM] Where is our bottleneck?
[06:36:12 | 3:24:04 PM] Right?
[06:36:12 | 3:24:04 PM] And so it becomes this redesigning of the workflow, or at least the reevaluation on what to focus on.
[06:36:20 | 3:24:11 PM] That's what they're doing different.
[06:36:21 | 3:24:12 PM] Now, this consideration isn't particular to finance.
[06:36:24 | 3:24:16 PM] It's across the realm and function.
[06:36:27 | 3:24:18 PM] You know, you're asking the same question on what to focus on, whether it be research capacity, decision speed, quality of your data, latency, reliability.
[06:36:39 | 3:24:31 PM] It traverses all of that.
[06:36:42 | 3:24:33 PM] And studies show this.
[06:36:43 | 3:24:35 PM] They show leading studies, on average, spend their attention on fewer, higher quality decisions than the other cohort.
[06:36:54 | 3:24:46 PM] So leaders are being more selective in where they focus their attention and what they focus their attention.
[06:37:00 | 3:24:52 PM] So it's great.
[06:37:01 | 3:24:53 PM] The leading companies have an idea of what the right problems are.
[06:37:05 | 3:24:57 PM] Where do they go from there?
[06:37:09 | 3:25:00 PM] Well, they also concentrate their investments in the highest value workflows.
[06:37:16 | 3:25:07 PM] I get it, and I've been culpable of something similar where, you know, AI is almost creating too many opportunities.
[06:37:25 | 3:25:17 PM] And experimentation can be a good thing, but one needs to separate experimentation and the resulting innovation theater, or worse, the productivity theater, which actual value generation.
[06:37:39 | 3:25:31 PM] Going back to our two hypothetical companies, what does this mean?
[06:37:43 | 3:25:35 PM] Company B is looking at how do we launch the most number of AI pilots, and what that ends up looking like is a document summarization bot here, a copilot there, a data retrieval bot here.
[06:37:57 | 3:25:49 PM] It's scattered all over the place.
[06:38:00 | 3:25:51 PM] Whereas what Company A is focused on in their investments is potentially use cases like end-to-end workflows on the research pipeline.
[06:38:12 | 3:26:03 PM] They're focused on aligning engineering, analytics, AI capacity, and judgment towards a singular focus.
[06:38:21 | 3:26:12 PM] And research has shown that the leaders spend more than 80% of their investments on these high-value use cases, transforming key business functions and creating new offerings.
[06:38:37 | 3:26:29 PM] Perfect.
[06:38:39 | 3:26:30 PM] We know they're focusing on finding the right problem, and they're also spending deep investment in select workflows.
[06:38:47 | 3:26:38 PM] Well, they're also doing something different.
[06:38:51 | 3:26:42 PM] They're reimagining how work is done.
[06:38:54 | 3:26:46 PM] I think this is key, and it goes back to what we discussed previously, which is judgment.
[06:39:01 | 3:26:53 PM] They're redefining and reorganizing functions, roles, capacities around this key element of judgment.
[06:39:10 | 3:27:02 PM] What does that mean for our hypothetical company?
[06:39:13 | 3:27:04 PM] Well, Company B might be focused on, as we discussed, I'm getting my earnings transcript, I'm getting my research reports.
[06:39:20 | 3:27:11 PM] How do I summarize it?
[06:39:22 | 3:27:13 PM] Maybe I make that faster.
[06:39:23 | 3:27:15 PM] Maybe I update my financial models faster.
[06:39:26 | 3:27:17 PM] Maybe I send my recommendation to my PM faster.
[06:39:29 | 3:27:21 PM] Company A is focused on an entirely different ballgame.
[06:39:34 | 3:27:25 PM] Hypothetically, they could be asking the analyst to define the investment thesis, and they're watching and gathering evidence that could refute that thesis or support it.
[06:39:45 | 3:27:36 PM] They'd be monitoring continuously.
[06:39:47 | 3:27:38 PM] They'd be investigating exceptions, and they could be deep diving on, you know, select examples that could lead to actual generation of value.
[06:40:01 | 3:27:52 PM] So the winners just don't just automate.
[06:40:03 | 3:27:55 PM] Just automate their redesign.
[06:40:05 | 3:27:56 PM] And studies show that the cohort of high performers are 75% more likely, or in practice, they actually do redesign their workflow versus 25% of the cohort that doesn't.
[06:40:18 | 3:28:10 PM] So where does this leave us with our two hypothetical companies and designing the right problem?
[06:40:24 | 3:28:16 PM] Company B adds AI to existing workflows.
[06:40:27 | 3:28:18 PM] They're completing individual projects and they're moving to the next initiative, whereas Company A is rethinking how work is done.
[06:40:35 | 3:28:26 PM] They're redesigning processes around AI and they're obsessing over the right problem.
[06:40:40 | 3:28:32 PM] That's the difference in how organizations put AI to work.
[06:40:44 | 3:28:36 PM] Great.
[06:40:45 | 3:28:36 PM] So they've picked the right problem.
[06:40:48 | 3:28:39 PM] What happens next?
[06:40:50 | 3:28:41 PM] Well, we all know in order to actually enable that capacity, you need to build shared capabilities across the org.
[06:40:59 | 3:28:50 PM] And what's a trend that's seemingly emerging for some time now is that the lines between technology and business are converging.
[06:41:08 | 3:29:00 PM] And we're seeing this everywhere.
[06:41:10 | 3:29:01 PM] We're seeing engineers that are moving closer to business outcomes.
[06:41:13 | 3:29:04 PM] We're seeing business professionals that are building tools and automations.
[06:41:18 | 3:29:10 PM] And as you can see and imagine, you can have forward deployed engineers, you can have forward deployed product managers, but you could also have the inverse, which is forward deployed portfolio managers.
[06:41:30 | 3:29:21 PM] And it's really this combination of getting the right people in the room by breaking down organization silos that the leaders are really leaning on.
[06:41:43 | 3:29:34 PM] Studies have shown that more and more of the large enterprises are viewing their technology arm as a business growth function.
[06:41:52 | 3:29:43 PM] And that's creating an edge for these companies.
[06:41:56 | 3:29:48 PM] So the other sort of vertical inside building shared capabilities that is probably a personal favorite of mine, it's standardizing the core and personalizing the edges.
[06:42:10 | 3:30:01 PM] And this is something that isn't new to the technology world.
[06:42:13 | 3:30:05 PM] We've built infrastructure, we've built, you know, shared modules and system design.
[06:42:17 | 3:30:08 PM] But what's interesting here today is that it's taking a very different shape with the capabilities out there and with the org and the breaking down of silos.
[06:42:26 | 3:30:18 PM] And this could look very different depending on where you sit.
[06:42:31 | 3:30:23 PM] You could be building shared cores for a team that looks very different with analysts and clients.
[06:42:39 | 3:30:30 PM] You could be doing that with a bunch of analysts, or you can be doing that for software engineers or data scientists.
[06:42:47 | 3:30:38 PM] With the data scientist example in particular, that could look very interesting, where instead of having one-off workflows that is really common in the industry, you could build a shared core of the data sets, their semantic layers, the trade-offs of what worked, what didn't, what some data sets do good of, where the gaps, and so on and so forth, such that each new personnel becomes almost a plug-and-play module in this system where they can then stand on the shoulder of their predecessors.
[06:43:23 | 3:31:15 PM] So what should the next person not have to rebuild?
[06:43:28 | 3:31:20 PM] Now, what's also interesting is, you know, the enterprise wasn't really built for agents.
[06:43:34 | 3:31:26 PM] Agents, they work across workflows and applications.
[06:43:39 | 3:31:30 PM] They take context, they deliberate, they take actions.
[06:43:42 | 3:31:34 PM] Whereas really the enterprises are focused previously on single human or a collection of them interacting with a single application.
[06:43:53 | 3:31:45 PM] So the leaders are cognizant that something needs to change and that they need to modernize their infrastructure.
[06:44:00 | 3:31:52 PM] And they're very self-aware of where they are in their journey.
[06:44:06 | 3:31:57 PM] And so studies show that, you know, 71% of the orgs are modernizing their core infrastructure to support AI.
[06:44:13 | 3:32:04 PM] And, you know, 6 to 10%, or 23%, of them are investing 6 to 10% of their annual revenue in core system modernization to take advantage of this.
[06:44:26 | 3:32:17 PM] And a common complaint here, or a common challenge here, is, you know, data quality and the ability to reuse data.
[06:44:35 | 3:32:27 PM] So great, we, you know, we have the right people in the room.
[06:44:40 | 3:32:31 PM] We have, you know, we have the right knowledge there.
[06:44:43 | 3:32:35 PM] What do we do next?
[06:44:45 | 3:32:37 PM] And then comes the concept of, hey, how do we make this knowledge reusable and accessible?
[06:44:51 | 3:32:42 PM] And that's where we have, you know, depending on the adoption and where they are, you can have different types of team agents, where you can have expert agents being subject matter experts.
[06:45:01 | 3:32:53 PM] You can have workflow agents that work across streams like Slack bots that are creating and opening tickets, they're closing them and what have you.
[06:45:09 | 3:33:01 PM] And then you can have really chief of staff kind of agents that keep a tab on what the state of the organization is and what needs to be done.
[06:45:18 | 3:33:10 PM] So, you know, going back to our workflows, A, again, is looking at this very differently in terms of shared capabilities.
[06:45:25 | 3:33:16 PM] Now, the last pillar here that I want to draw your attention to is, well, when does this system really compound?
[06:45:34 | 3:33:26 PM] What happens when it's deployed?
[06:45:37 | 3:33:29 PM] Well, the leading companies, what they're doing is they're actually thinking about it slightly differently.
[06:45:44 | 3:33:35 PM] What are failures that can give us an edge that we can learn from?
[06:45:48 | 3:33:40 PM] If there's a success, how do we make it part of the system?
[06:45:52 | 3:33:44 PM] And the way they think about this is, you know, the individual learned but did the system.
[06:45:57 | 3:33:49 PM] Is there a way, perhaps, when they've encountered something where they need to make a correction, that that knowledge is fed back into the org, either through a new tool or context or what have you?
[06:46:09 | 3:34:00 PM] And what's key here is preserving the evidence along with those learnings.
[06:46:16 | 3:34:07 PM] So really the last vertical here is AI-first capacity.
[06:46:22 | 3:34:14 PM] Where does it go?
[06:46:24 | 3:34:15 PM] Being cognizant that you need to redesign your system and really focus it on key metrics that you're evaluating, you're monitoring, and you're creating a flywheel off.
[06:46:37 | 3:34:29 PM] So where does this leave us for companies A and B?
[06:46:41 | 3:34:32 PM] You know, company A has their entire work redesigned.
[06:46:45 | 3:34:37 PM] They're building capabilities at compound, and they have shared capabilities built.
[06:46:51 | 3:34:43 PM] So I want to leave you off with one final question.
[06:46:54 | 3:34:46 PM] Everyone has AI.
[06:46:55 | 3:34:47 PM] Who has the advantage?
[06:46:56 | 3:34:48 PM] Thank you.