00:00Cuando implementa AI,
00:02muchas organizaciones obsesan con la tecnología.
00:05Pero nuestra investigación revela una verdadera sorpresa.
00:08Philosophy es lo que realmente determina el successo de AI.
00:12Hoy, Michael Schrag y yo
00:14vamos a explicar cómo filosofical frameworks
00:16pueden transformar tu iniciativa,
00:18sharpenar tu estrategia de AI,
00:20y superargar tu ejecución
00:22en formas algoritmos solo nunca podrían.
00:25Este es como tu guía
00:27para extracting actual business value from AI,
00:30not just impressive demos.
00:32Because the competitive edge
00:34isn't in having more sophisticated models,
00:36but in thinking more clearly
00:38about what they should actually accomplish.
00:40This isn't theoretical.
00:42It's about ensuring your AI investments
00:44deliver genuine competitive advantage.
00:47Let's dive in.
00:49So we wrote this piece, Philosophy Eats AI.
00:59Why don't we start off by talking a little bit
01:02about what the main thesis is?
01:04Well, the main thesis was based on what's next.
01:07And so the obvious question is,
01:10if software is eating the world and AI is eating software,
01:14what's going to eat AI?
01:16And the logical, dare I say, teleological, ontological,
01:22and epistemological answer to that question was clear.
01:26Philosophy is going to be eating AI.
01:31And the reason why this was not just a good bet,
01:35but a great insight,
01:37was GPT, generative pre-trained transformers.
01:44Training, learning, education.
01:47These are all philosophical constructs.
01:50Yeah, there are all these embedded philosophies.
01:52Exactly.
01:53That's the beautiful irony that our piece doubled down on.
01:57AI should not be seen overwhelmingly as just an ethical
02:01or a technical or a digital innovation and platform.
02:05It's actually a philosophical capability and resource.
02:09Philosophy has had a large, almost outsized role
02:14in the development of computer systems.
02:17But what I think, though, is really, really different, though,
02:20is when you look at generative pre-trained transformers,
02:24we're not looking at legacy notions of logic or reason or rationality.
02:31What we're looking for are what aspects and elements of patterns
02:37are similar or relevant to one another.
02:40So it really does offer, pun intended,
02:43a different philosophical insight into how meaning gets made.
02:48Yeah. I mean, there's this relationship between computation, reason, and patterns.
02:53Yes.
02:54And the value of the dynamic between those
03:00has really shifted in this new world of agentic AI.
03:04And the notion of agents simply being mechanisms that perform tasks,
03:11as opposed to entities that have, yes, agency,
03:17where they can make choices, where they can make evaluations,
03:21where they can make trades.
03:23I think that's profound.
03:25We're moving agents, agentic AI, from the notion of,
03:30how should we ensemble agents as enablers or performers of tasks
03:37into what kind of emergent intelligence?
03:40How would you train an ensemble of agents or a swarm of agents
03:45to solve a problem, to rethink or refine a context?
03:51The ability to play with patterns, to meaningfully play with patterns,
03:58buys you way more than anybody would have expected.
04:01And the ability to simulate reason may be good enough,
04:08or I'll stick my neck out and say better than the real thing,
04:12for a lot of problems, situations, and use cases.
04:15What I find a really compelling angle to what we discuss in the article is
04:21the notion of bounded rationality...
04:23Right.
04:24...is different from bounded patterns.
04:27Patterns.
04:28Actually, that's the kind of thing we should put into ChatGPT.
04:31Here is the Herb Simon Nobel laureate prize-winning notion
04:34of bounded rationality.
04:36But what is the counterpart to bounded patterns or pattern constraints?
04:42Purpose. Purpose.
04:44Right.
04:45Could be an important part of what bounds...
04:49creates some boundaries around...
04:51So we could play with this.
04:52Yeah.
04:53What is the purpose of patterns?
04:54What are the patterns of purpose?
04:56And that kind of reflexivity, I think, is going to be a source of inspiration
05:01for how large language models and small language models get trained.
05:07Well, let's connect that with the Google Gemini example that we discussed.
05:11Yeah. Great example. Yes.
05:12Where Google Gemini, in one of his early instantiations, wound up misrepresenting historical things,
05:21like World War II and America's founding fathers.
05:27There was a lot of diversity that didn't exist back then.
05:30Yes.
05:31And it was almost as if there were competing objectives that were going on in the model.
05:36Well, that was your excellent phrase, teleological confusion.
05:39And I think that's exactly right.
05:41What happens when you have purposes that are at odds?
05:46Or in the first-gen Google Gemini case, genuinely conflicting.
05:52And that you basically, and I picked this word deliberately, you privilege the diversity purpose over the historical accuracy purpose.
06:02I don't think they thought it through.
06:04It's like you have these diversity, equity, and inclusion mandates.
06:11And you have historical accuracy mandates.
06:15They had their ontology for what the world is like in terms of, from an accuracy point of view.
06:24And they also had an ontological point of view of what diversity should look like.
06:30And these were at odds with one another.
06:32The irony is they did this with an ethical imperative.
06:35My own view is not that they didn't think it through.
06:40It's that they didn't care until these things generated bad publicity as well as inaccurate history.
06:49Alright, so you've got indifference and I've got like a lack of commitment.
06:56Which ironically are philosophical perspectives on this.
07:01I really believe that these corporate culture, technical value, what's our real philosophy, what do we really stand for issues,
07:12have to become more important in a generative AI, predictive AI context and circumstance.
07:20I think these values issue and what is it that we're really seeking to optimize becomes more important.
07:28And that's why philosophy eats AI.
07:31That's why this theme, this narrative, this argument, this assertion has to be taken more seriously.
07:39We promise in the article that there's some actionable thing that you can do with this talk about philosophy eating AI.
07:46What is, to your mind, one of the biggest things that executives can do with this discussion?
07:55To me, I think the most important actionable and inexpensive insight is organizations need to map what they believe.
08:07Right now with responsible and ethical AI, they're mapping the ethical and responsible elements and aspects.
08:15It's like a responsibility mapping.
08:18Exactly.
08:19It's responsibility mapping and you and I both agree that there's been perhaps over-indexing and over-investment in the ethical components of AI, the philosophy of AI.
08:32Our article argues that what is the teleology?
08:37And we had a good negative example.
08:39Well, what are the good positive examples in that regard?
08:42Well, we also talk about Starbucks and Amazon.
08:45Exactly.
08:46The whole notion of what does loyalty really mean?
08:51Is it the superficial metric of repeat business or is it that actually your customers become advocates?
09:00They become champions.
09:01They become defenders.
09:02And you can track those elements and aspects of evangelism and defense and sharing communication that they celebrate.
09:14And that's, you know, to me, one of the really intriguing things that an organization that is bothered to go through the agony and investment of digital transformation and then putting an intelligent stack or capability on top of that digital transformation, they really should be thinking and mapping, gee, what's the teleology?
09:34What's the purpose?
09:35What's the ontology?
09:36How do we label and categorize these things?
09:39Epistemology, what is the nature of knowledge that informs categorization and purpose?
09:45Do we have them in conflict?
09:47Is there a virtuous cycle?
09:49What do we want our software to learn?
09:53What do we want our AI to learn?
09:56What do we want our agents to learn?
09:58This is where you have a marriage of the technical capability with the philosophical need and the business purpose.
10:06One of the big practical insights is that if you are going to lead with metrics, you need to have a deep understanding of what you can actually measure in the enterprise.
10:18Yes.
10:19And that you have new techniques for measurement.
10:21Yes.
10:22With all due respect to Peter Drucker, you manage what you measure.
10:26He was not, this was not like a complimentary thing.
10:30So you can only measure your measure a small amount of your business activity.
10:35So now there's so much more at business activity that you can measure that can drive performance that you can like tailor metrics.
10:50I completely agree.
10:52My only pushback is, is it broadens the notion of what business means.
10:57You know, it's not just customer satisfaction or NPS.
11:02It's what is customer experience?
11:04What is the purpose of customer experience?
11:07What is the, what are the epistemological underpinnings or ontological aspects and categories of customer experience?
11:15Same thing with partnerships with, with suppliers.
11:18The whole notion of what the vocabulary of value creation can and should be is fundamentally disrupted and fundamentally changed.
11:28The essential narrative, the essential virtuous cycle there is, you learn to prompt and you prompt to learn.
11:36And if you do it right, the way you learn to prompt informs how you prompt to learn.
11:42It's not just your learning, it's the machines learning.
11:46If you're not learning as much as your AI models are, something is wrong with your human capital balance and human capital portfolio.
11:55And it's the same sort of thing, if your humans are learning faster than your models, gee, I think your ML ops aren't as efficient or as effective as it should be.
12:05What kind of virtuous cycle do we want to enable and empower between machine learning and human learning?
12:12Some, some of my recent work has focused on like reconsidering how decisions are made.
12:17Yeah.
12:18But we're talking about right now, reconsidering how thinking occurs in the enterprise, which is a significant transformation in how leaders conceive of their own role in the enterprise.
12:31If you can think out loud with an LLM or with an agentic AI.
12:36I mean, you can be augmented in all sorts of ways that you weren't able to before.
12:41Right.
12:42As you come to have a greater understanding in every meaning of the phrase of what it is you seek to accomplish, what it means, and what the knowledge supporting that means and is, the whole notion of what you want to automate, make mindless, should become clearer.
13:01And what you want to augment, i.e., put the human in the loop, add value to the leadership's and the leader's decision making capabilities also becomes more acute.
13:11I think the whole notion of how we enable people to think more rationally, intelligently, and intentionally about the trade off between automating and augmenting agents that think and agents that just perform tasks.
13:27I think that's a big aspect as well. So I think you're spot on.
13:31Are you in any way optimistic that those people who are inclined to think less rigorously now have a capability for thinking more rigorously out as they think with AI?
13:48That's sort of like the cognitive counterpart to Ozempic versus Weight Watchers. Would I rather just take the drug or am I prepared to follow a diet?
14:01There really is a difference between leaders who take rigorous, comprehensive, philosophical thinking seriously and those who are looking to optimize share price, market share, etc.
14:17Generative AI is the battleground and the battle space for competing and conflicting philosophies for value creation and experience.
14:29The bottom line? Philosophy isn't just academic. It's a practical approach to AI that delivers meaningful business value.
14:38What philosophical questions are you asking before implementing AI in your organizations? Drop your thoughts in the comments below.
14:45And for a more detailed analysis, check out our article Philosophy Eats AI on the MIT Sloan Management Review website.
14:54And for more insights from our authors, check out this curated playlist.
14:58Thanks for watching.
