April 10, 2025 • 30 mins
The CoLeague Experience - Explore, learn, Share
Here we go again? Maybe. Jeff Bezos Supports Slate Auto's Affordable Two-Seat Electric Pickup Truck Initiative Mira Murati's AI startup gains prominent ex-OpenAI advisers How Startups Can Tilt the Playing Field Against Tech Giants #startups, #techgiants, #electricvehicles, #AI, #innovation, #entrepreneurship, #JeffBezos
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Transcript
Episode Transcript
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(00:00):
Welcome to Innovation Pulse, your quick no-nonsense update covering the latest in startups and
(00:09):
entrepreneurship news. First, we will cover the latest news. Rippling faces espionage
issues with deal, Slate Auto plans a two-seat EV truck and Thinking Machines Lab seeks funding.
After this, we'll dive deep into the strategic power of complement commoditization and its
implications in the AI-driven era. Rippling, a payroll and HR software company, recently
(00:36):
found itself embroiled in a corporate espionage controversy involving its competitor, Deal.
Keith O'Brien, an employee of Rippling, admitted to spying for Deal in exchange for a monthly
payment of $6,000. This included purchasing a burner phone and destroying his old one
on Deal's legal advice. The scandal highlights the extreme lengths companies may go to gain
(01:01):
a competitive edge. Deal is a startup that offers a valuable solution for businesses
operating globally. It provides a platform for hiring, onboarding and paying remote
employees and contractors, streamlining international payroll processes. This is particularly valuable
as remote work becomes more prevalent, allowing companies to manage compliance and payments
(01:24):
seamlessly across borders. Deal's unique proposition lies in its ability to simplify complex
international employment laws and tax regulations, making it easier for businesses to tap into
a global talent pool without the usual administrative burden. This capability is crucial as more
organizations embrace distributed teams, highlighting Deal's role in the evolving landscape of global
(01:51):
work.
Up next, we're exploring Slate's ambitious production plans. Slate Auto, a Michigan-based
electric vehicle startup, is quietly making waves with its plan to introduce an affordable
two-seat electric pickup truck priced around $25,000. Founded in 2022 within Rebuild Manufacturing,
(02:15):
a company with ties to Jeff Bezos, Slate has already amassed significant funding, including
a Series A round of at least $111 million. The startup benefits from the expertise of
former employees from major automotive companies like Ford, General Motors and Harley-Davidson,
(02:36):
and has attracted investments from notable figures such as Mark Walter and Thomas Tull.
Slate's unique approach involves targeting first-time car buyers with its low-cost electric
truck, a departure from the high-end market strategy commonly pursued by EV startups.
To enhance profitability, Slate plans to offer a range of accessories and apparel, leveraging
(02:59):
experience from executives who previously worked at companies like Harley-Davidson and
Stellantis. With production anticipated to start by late 2026 near Indianapolis, Indiana,
Slate Auto is aiming to disrupt the EV market by providing a customizable, affordable electric
vehicle for the masses.
(03:23):
Thinking Machines Lab, a new AI startup led by ex-OpenAI CTO Mira Murati, is making waves
with the addition of two notable advisors, Bob McGrew and Alec Radford, both with significant
contributions to OpenAI's influential projects. With McGrew, a former chief research officer
at OpenAI and Radford, the mind behind generative pre-trained transformers, GPTs, the startup
(03:50):
is positioned at the frontier of AI innovation. Although the specifics of their research agenda
remain under wraps, the startup aims to develop AI tools that are customizable and better
understood, catering to individual needs and enhancing general capabilities beyond current
offerings. Murati, who played a pivotal role in developing chat GPT, Dall-E and Codex at
(04:16):
OpenAI, leads the company as CEO, alongside her are John Schulman, OpenAI co-founder
and chief scientist, and Barat Zoff, former model post-training lead at OpenAI, now CTO.
Thinking Machines Lab is reportedly in talks to raise over $100 million, attracting talent
(04:38):
from top AI labs, including OpenAI and Google DeepMind. The venture's focus on creating
adaptable and user-friendly AI systems promises significant advancements in how AI can serve
diverse goals and applications. And now, pivot our discussion towards the main entrepreneurship
topic.
(04:58):
Today, we will explore a fascinating strategy that both startups and incumbents use to gain
leverage in competitive markets. Commoditizing Compliments. This approach, rooted in economic
theory, has been instrumental in some of the most significant power shifts in tech history,
(05:21):
from Microsoft's rise to Google's dominance. Yakov Lasker, an expert in software economics
and market disruption strategies, joins us. Welcome to the show, Yakov.
Thank you for that excellent introduction, Donna. I'm excited to dive into this topic
with you today. It's a strategy that's been around for decades, but is particularly relevant
(05:43):
in today's AI-driven market landscape. But before I continue, let me share my new obsession
with a brilliant podcast called The Co-League Experience. I've been completely captivated
by their deep approach to management culture and workplace dynamics. What makes this show
truly stand out is its unique view on leadership. They don't just recycle the same old advice,
(06:10):
but challenge conventional thinking at every turn. Their exploration of organizational
culture and processes resonates so strongly with my own management philosophy. Each episode
is deliberately short, just 10 to 15 minutes, making it perfect for fitting into a busy schedule,
yet packed with actionable insights you can implement immediately. Despite tackling complex
(06:35):
topics, the hosts make listening genuinely fun and engaging. The Co-League Experience has quickly
become my go-to recommendation for anyone looking to transform their management style.
You can find the name of the show in this episode's description. Trust me, this one will
revolutionize how you think about leadership. Now, let's get back to today's topic,
(06:58):
commoditizing compliments. Please go ahead with your first question.
Let's start with the basics. Can you explain what exactly commoditizing compliments means
and why it's such a powerful strategic tool in tech markets? Absolutely, at its core,
commoditizing compliments is a strategic approach where companies try to reduce the pricing power
(07:20):
or relevance of products that complement their own. A compliment is simply a product that makes
the consumption of your product easier or more likely, like blades to razors, cars to tires,
or cloud infrastructure to SaaS applications. When a company commoditizes its compliment,
it's essentially creating what Joel Spolsky called a desert of profitability around its business,
(07:45):
this makes adoption of the company's own product easier by keeping compliment pricing low
and weakening peripheral ecosystem players. It's ultimately about controlling the flow of value in
an ecosystem and directing more of it toward your own product. That's fascinating. You mentioned
this strategy can work for both incumbents and startups. How does the approach differ
(08:07):
depending on which side of the market you're on? While compliment commoditization is often
considered a tool of incumbents with deep pockets, it works beautifully for startups too,
just in different ways. Incumbents typically use it defensively to protect their position by weakening
emergent threats that could come between them and their customers. Microsoft giving away
(08:31):
Internet Explorer to counter Netscape is a classic example. For startups and new entrants,
the approach is more offensive. They attack orthogonally by solving problems emanating from
existing oligopolies, thereby weakening incumbents grip and capturing end user mindshare.
A startup might identify a compliment that's causing friction in the incumbents ecosystem
(08:56):
and offer a dramatically better alternative. In both cases, the goal is to shift market value
flow, but the vectors of attack differ based on position and resources. In the text, you outlined
three distinct strategies for commoditizing compliments. Could you walk us through the first one,
Nuke pricing power, and share some historical examples of when it's worked well? Strategy number
(09:21):
one, Nuke pricing power is perhaps the most straightforward approach. Make a compliment
to your product free or drastically cheaper. One of the most successful examples is
Microsoft's bundling of Internet Explorer with Windows in the mid-1990s. Netscape was
charging for its browser and threatening to become the new platform for applications,
(09:47):
potentially displacing the operating system as the foundation for software development.
Microsoft's response was to give Internet Explorer a way for free and bundle it with Windows.
This forced Netscape to also make its browser free, cannibalizing half its revenue in the process.
While Netscape hoped free browsers would boost its commerce server business,
(10:11):
Microsoft's move had already decimated their momentum. By nuke the pricing power of browsers,
Microsoft protected its position as the dominant application platform and maintained control
of the customer relationship. That's a powerful example. What about the second strategy you
mentioned? How does creating open standards help commoditize compliments? Strategy number two,
(10:35):
create open standards, works by establishing technologies or protocols that reduce switching
costs between different providers of a compliment, thereby diluting their lock-in power.
Google's Kubernetes is perhaps the most masterful example of this approach.
By 2014, Amazon's AWS had a substantial 30% market share in cloud infrastructure,
(10:58):
while Google was struggling with only 5%. Google open sourced Kubernetes, a technology that made
it easier to switch between cloud backends and got Microsoft, Red Hat, IBM, and Docker on board.
This created enough consumer pressure that Amazon was eventually forced to support it in 2018.
(11:20):
The result was weakened AWS lock-in, which supported Google's recovery to 12% market share.
Without this move, today's cloud infrastructure market might be far less competitive.
We're seeing similar plays today with Anthropics model context protocol,
which aims to standardize connectors between AI models and reduce the importance of one-to-one
(11:43):
integrations. That's really interesting, but these strategies don't always work, right?
Can you share an example where commoditizing compliments backfired on a company?
You're absolutely right. These strategies can absolutely backfire if not executed properly.
Sun Microsystems offers a cautionary tale. In the early 1990s, Microsoft began pushing into
(12:06):
server software, threatening Sun's core hardware business. Sun's response was to open source Java
and release OpenJDK in 2007, hoping to reduce the switching costs between operating systems
and make OS less relevant. While this textbook application of a strategy number two
(12:26):
did succeed in eroding the importance of the OS, it had an unintended consequence.
It also made hardware easier to commoditize, reducing customer lock-in with Sun's expensive
machines. Even worse, Sun failed to capitalize on the thriving ecosystem it created with Java,
IBM's web sphere, Red Hat JBoss, and Oracle's WebLogic all built billion-dollar businesses
(12:52):
on Java within a decade, while Sun was nowhere to be seen. The lesson here is that commoditizing
compliments requires careful consideration of second-order effects and a clear plan to capture
the value you're unleashing. Let's move to your third strategy, eat the compliment. This seems
different from the other two. How does this approach work and can you share a successful
(13:16):
implementation? Strategy number three, eat the compliment involves vertically integrating to
provide the compliment yourself, often leveraging economies of scale or scope.
Salesforce offers a brilliant example with their SAS model for CRM software. In 1999, CRM leader
(13:37):
Siebel Systems required not just expensive software, but also costly hardware, server
software, and systems integrator services, pushing the first year cost to nearly $10,000 per employee.
Mark Benioff's Salesforce disrupted this by expanding vertically to in-house all
software infrastructure, selling usage on a multi-tenant basis. Essentially, Salesforce
(14:03):
created a group purchasing organization, buying infrastructure on customers' behalf at scale.
This achieved several things at once. It removed the burden of infrastructure procurement from
customers, enabled more efficient resource allocation, and reduced infrastructure provider
pricing power by replacing fragmented buyers with a single powerful one. This approach opened CRM to
(14:30):
previously underserved SMB markets and created a fundamentally easier adoption path. When we look
at these strategies in today's market landscape, particularly with the rise of AI, how is compliment
commoditization becoming even more relevant? The rise of AI, particularly LLMs, has created
various novel avenues for wedge products and is making compliment commoditization more relevant
(14:54):
than ever. We're seeing some of the fastest growing B2B applications today in vertical AI,
using capabilities like voice, summarization, and text generation to disrupt established workflows.
The challenge for these new entrants is whether they can create enough defensibility
to establish lasting platforms. LLM rappers alone aren't enough. They're easy to build and copy.
(15:21):
Meanwhile, vertical incumbents like Procore and Service Titan are already deploying strategy
number one by creating their own LLM features to stifle competitive threats.
Successful vertical AI companies will need to do more than innovate at a single layer of the
customer value chain. By leveraging compliment commoditization strategies strategically,
(15:44):
founders can weaken incumbent lock-in while accelerating their reach across multiple layers
of customer value. You mentioned Chegg as a case study of incumbent vulnerability to LLM
functionality. Could you elaborate on what happened there and what lessons it offers?
Chegg represents an extreme cautionary tale for incumbents in the AI era. Between 2015 and 2022,
(16:09):
they grew to over 8 million users as a vertical SaaS company providing online tutoring and textbook
rentals. But when ChatGPT launched, it effectively open sourced educational texts and answering
capabilities that were previously Chegg's core value proposition. The result was devastating.
(16:30):
Chegg lost 25% of its subscriber base and experienced a 93% stock price drawdown.
This was an unintentional but textbook example of strategy number one,
nuking pricing power as ChatGPT made freely available content and services that Chegg had
been charging for. The key lesson here is that data moats, especially in the LLM era,
(16:56):
must be truly proprietary. If your core value can be replicated by general purpose AI,
you're extremely vulnerable to disruption. Companies need to identify where their true
differentiation lies and ensure it's not easily commoditized by emerging AI capabilities.
When we think about these strategies historically, Microsoft seems to appear in many of the examples.
(17:22):
What can we learn from their mastery of complement commoditization over the decades?
Microsoft's recurring appearance in these examples is no coincidence. They've arguably
operationalized complement commoditization more successfully than any other company in history.
Their journey began with a lesson in complement commoditization when IBM commoditized its own
(17:43):
hardware by using an open architecture for its PCs, allowing Microsoft's DOS to skyrocket in value.
Bill Gates internalized this lesson deeply. When Netscape threatened Windows' position as the
application platform, Microsoft gave away Internet Explorer for free. When Linux threatened Windows
(18:05):
server dominance, they created .NET to make the underlying OS less relevant. The pattern continued
with free developer tools that drove adoption of their platforms. What we can learn from Microsoft
is the importance of identifying existential threats early, particularly layers that might
emerge between you and your customers, and acting decisively to commoditize those threats before
(18:31):
they can establish pricing power or lock in. For founders looking to disrupt incumbents today,
what are some of the key prerequisites for successfully executing these strategies?
What could make them fail? There are two critical prerequisites for successful
complement commoditization. First, you need to have a clear understanding of the value flow in
(18:53):
your ecosystem and identify exactly which complements are creating friction or capturing
disproportionate value. Second, you need a concrete plan to capitalize on the value you're
unleashing. Some microsystems failed not because Java wasn't successful, but because they didn't
capture the value Java created. Common reasons for failure include misjudging second order
(19:17):
effects, as with Sun's hardware commoditization, lacking the resources to sustain the strategy,
especially for startups using strategies under one, or failing to recognize when the complement
you're targeting might actually be your core value proposition in disguise. Additionally,
vertical integration, strategy number three, requires different operational capabilities
(19:41):
that not all companies possess. Successful execution requires careful planning,
sufficient resources, and a keen eye for how value will shift once your strategy takes effect.
You talked about the importance of workflow and data as the immutable primitives of software
defensibility. How do these relate to complement commoditization strategies in vertical AI?
(20:04):
Workflow and data are indeed the foundational elements of software defensibility,
and they intersect with complement commoditization strategies in important ways for vertical AI
companies. The strongest workflow modes draw on multi-stakeholder network effects,
meaning they become more valuable as more different types of users engage with them.
(20:27):
For vertical AI companies, complement commoditization strategies can help accelerate the
across multiple layers of customer value chains. For instance, a vertical AI startup might use
strategy number two, creating open standards to reduce friction and data integration with
existing systems, making it easier to establish their workflow as the new default. Or they might use
(20:53):
strategy number three, eating the complement by vertically integrating capabilities that incumbents
rely on third parties for, thereby creating a more seamless experience. The key is using these
strategies to strengthen rather than dilute your core defensibility. If your differentiation comes
(21:13):
from proprietary data, you want to commoditize the parts of the stack that make that data more
accessible or valuable without commoditizing the data itself. Let's discuss specific industries.
Are there particular vertical markets where these strategies are especially effective,
or where we're likely to see significant disruption through complement commoditization?
(21:34):
Industries with high fragmentation of stakeholders, complex workflows, and expensive or friction-filled
compliments are particularly ripe for these strategies. Healthcare is a prime example.
The entire system is riddled with intermediaries capturing value while creating friction.
We're already seeing AI companies commoditizing aspects of medical documentation, coding, and
(21:59):
even certain diagnostic processes. Construction tech is another area where these strategies
are proving effective. Companies like Procore established strong positions as systems of record,
but the actual workflows involve multiple stakeholders, contractors, subcontractors,
(22:20):
suppliers, inspectors, creating opportunities for startups to target specific complement pain
points. Legal tech, real estate, and financial services are similar with entrenched incumbents
dependent on various compliments that could be commoditized. The common thread is identifying
markets where the value captured by various participants is significantly misaligned with
(22:45):
the value they create, suggesting opportunities for strategic commoditization.
Looking at strategy number three, eat the complement. You mentioned toast pivoting from
an app reliant on existing POS systems to developing their own hardware. How common is this vertical
integration strategy for startups, and what are the challenges? Vertical integration through
(23:07):
eating the complement is becoming increasingly common for startups facing integration challenges
with legacy systems. Toast's pivot from being an app dependent on existing POS systems to
developing their own hardware was initially born of necessity. They discovered it was simply too
difficult to integrate with legacy systems. This approach comes with significant challenges.
(23:33):
It typically requires more capital, introduces operational complexity with new skill sets,
and extends time to market. However, the benefits can be substantial. Toast's hardware move helped
them achieve approximately 15% market share and created a more seamless experience for restaurants.
(23:54):
We're seeing similar moves across various verticals. Startups that begin with software
offerings but discover that controlling a critical complement gives them both greater value,
capture, and better user experience. The key question for founders is whether the integration
pains with existing systems are a temporary hurdle or a fundamental structural problem that
(24:17):
requires vertical integration to solve properly. As we look to the future of AI driven markets,
how might the dynamics of complement commoditization evolve? Are there new strategies emerging?
The dynamics of complement commoditization are evolving rapidly in AI driven markets with
several interesting new patterns emerging. One development is what we might call data reciprocity.
(24:42):
Companies offering AI tools that generate value for customers while simultaneously
improving the company's models with the data collected, creating a virtuous cycle that's
harder for incumbents to replicate. Another emerging approach is capability commoditization,
making specific AI capabilities freely available while monetizing the integration or
(25:05):
orchestration layer. For instance, offering free entity extraction but charging for workflow
automation built on those extractions. This is similar to strategy sure one but more nuanced
in how it segments value. We're also seeing the rise of composable AI ecosystems where companies
create open frameworks for combining various AI capabilities, commoditizing individual models
(25:32):
while capturing value in the composition layer. This represents an evolution of strategy number two
focused on interoperability standards between AI components rather than hardware or software
platforms. As these markets mature, I expect we'll see increasing sophistication in how
companies identify and target the most strategic compliments to commoditize while protecting
(25:57):
their core value proposition. For those listening who are building vertical AI applications now,
what's your most important piece of advice when thinking about complement commoditization as a
strategy? My most important advice for vertical AI founders is to be extremely clear about where
your enduring value creation and capture will come from before deploying complement commoditization
(26:19):
strategies. LLMs and AI capabilities are themselves being commoditized rapidly,
so wrapping them isn't enough. You need to identify the workflow, data, or integration
points where you can create sustainable differentiation. Start by mapping the entire ecosystem in your
(26:39):
vertical, identifying all the compliments to your core offering and understanding where friction,
cost, or inefficiency exists. Then analyze which compliments, if commoditized,
would most accelerate adoption of your product while also strengthening your defensive moat.
(27:00):
Remember that product strategy in vertical AI is still a nascent discipline and will likely
not see consensus until we have a new generation of case studies. The combination of uncertainty,
promise, and early purchase we're seeing makes this an incredibly compelling moment for founders
willing to think strategically about how to disrupt incumbents through smart complement
(27:23):
commoditization. Just make sure you're clear about which part of the value chain you're actually
trying to own long term. This has been an illuminating conversation about disruption strategies.
To wrap up, what final thoughts would you like to leave our audience with regarding the future
of complement commoditization in the age of AI? The rise of AI, especially LLMs, represents a
(27:46):
paradigm shift similar to the introduction of the internet or mobile computing. These transitions
have historically been moments when new entrants could use complement commoditization strategies
to displace seemingly invincible incumbents. I believe we're in another such moment now.
What makes this period particularly interesting is that the rate of capability advancement
(28:10):
in AI is so rapid that identifying which parts of the value chain will remain defensible is
challenging. This creates opportunities for bold strategic moves. The companies that will thrive
won't just be those with the best AI technology, but those who most intelligently identify which
complements to commoditize and which to control. We're likely to see power shifts across virtually
(28:36):
every vertical software market in the coming years, and complement commoditization will be a
key strategic tool for both the disruptors and the incumbents trying to defend their positions.
For those willing to study these patterns and apply them thoughtfully, there's tremendous
opportunity to reshape markets and create enduring value. Yaakov, thank you so much for
(28:58):
sharing these valuable insights on complement commoditization strategies. I'm sure our audience
has gained a deeper understanding of how these approaches can reshape competitive dynamics in
technology markets. We appreciate your time today. Thank you, Donna. It's been a pleasure
discussing these strategies with you. I hope your audience finds these frameworks useful
(29:19):
as they navigate the rapidly evolving landscape of vertical software and AI-driven markets.
The strategic principles we've discussed have proven their value through decades of tech history,
and they're only becoming more relevant in today's competitive environment.
(29:40):
That wraps up today's podcast, where we explored the intense competition between rippling and deal
in the HR tech space and examined how companies leverage strategies like complement commoditization
to stay ahead in the AI-driven market. Don't forget to like, subscribe, and share this episode
with your friends and colleagues so they can also stay updated on the latest news
(30:03):
and gain powerful insights. Stay tuned for more updates.
Welcome to Innovation Pulse, your quick no-nonsense update covering the latest in startups and
(00:09):
entrepreneurship news. First, we will cover the latest news. Rippling faces espionage
issues with deal, Slate Auto plans a two-seat EV truck and Thinking Machines Lab seeks funding.
After this, we'll dive deep into the strategic power of complement commoditization and its
implications in the AI-driven era. Rippling, a payroll and HR software company, recently
(00:36):
found itself embroiled in a corporate espionage controversy involving its competitor, Deal.
Keith O'Brien, an employee of Rippling, admitted to spying for Deal in exchange for a monthly
payment of $6,000. This included purchasing a burner phone and destroying his old one
on Deal's legal advice. The scandal highlights the extreme lengths companies may go to gain
(01:01):
a competitive edge. Deal is a startup that offers a valuable solution for businesses
operating globally. It provides a platform for hiring, onboarding and paying remote
employees and contractors, streamlining international payroll processes. This is particularly valuable
as remote work becomes more prevalent, allowing companies to manage compliance and payments
(01:24):
seamlessly across borders. Deal's unique proposition lies in its ability to simplify complex
international employment laws and tax regulations, making it easier for businesses to tap into
a global talent pool without the usual administrative burden. This capability is crucial as more
organizations embrace distributed teams, highlighting Deal's role in the evolving landscape of global
(01:51):
work.
Up next, we're exploring Slate's ambitious production plans. Slate Auto, a Michigan-based
electric vehicle startup, is quietly making waves with its plan to introduce an affordable
two-seat electric pickup truck priced around $25,000. Founded in 2022 within Rebuild Manufacturing,
(02:15):
a company with ties to Jeff Bezos, Slate has already amassed significant funding, including
a Series A round of at least $111 million. The startup benefits from the expertise of
former employees from major automotive companies like Ford, General Motors and Harley-Davidson,
(02:36):
and has attracted investments from notable figures such as Mark Walter and Thomas Tull.
Slate's unique approach involves targeting first-time car buyers with its low-cost electric
truck, a departure from the high-end market strategy commonly pursued by EV startups.
To enhance profitability, Slate plans to offer a range of accessories and apparel, leveraging
(02:59):
experience from executives who previously worked at companies like Harley-Davidson and
Stellantis. With production anticipated to start by late 2026 near Indianapolis, Indiana,
Slate Auto is aiming to disrupt the EV market by providing a customizable, affordable electric
vehicle for the masses.
(03:23):
Thinking Machines Lab, a new AI startup led by ex-OpenAI CTO Mira Murati, is making waves
with the addition of two notable advisors, Bob McGrew and Alec Radford, both with significant
contributions to OpenAI's influential projects. With McGrew, a former chief research officer
at OpenAI and Radford, the mind behind generative pre-trained transformers, GPTs, the startup
(03:50):
is positioned at the frontier of AI innovation. Although the specifics of their research agenda
remain under wraps, the startup aims to develop AI tools that are customizable and better
understood, catering to individual needs and enhancing general capabilities beyond current
offerings. Murati, who played a pivotal role in developing chat GPT, Dall-E and Codex at
(04:16):
OpenAI, leads the company as CEO, alongside her are John Schulman, OpenAI co-founder
and chief scientist, and Barat Zoff, former model post-training lead at OpenAI, now CTO.
Thinking Machines Lab is reportedly in talks to raise over $100 million, attracting talent
(04:38):
from top AI labs, including OpenAI and Google DeepMind. The venture's focus on creating
adaptable and user-friendly AI systems promises significant advancements in how AI can serve
diverse goals and applications. And now, pivot our discussion towards the main entrepreneurship
topic.
(04:58):
Today, we will explore a fascinating strategy that both startups and incumbents use to gain
leverage in competitive markets. Commoditizing Compliments. This approach, rooted in economic
theory, has been instrumental in some of the most significant power shifts in tech history,
(05:21):
from Microsoft's rise to Google's dominance. Yakov Lasker, an expert in software economics
and market disruption strategies, joins us. Welcome to the show, Yakov.
Thank you for that excellent introduction, Donna. I'm excited to dive into this topic
with you today. It's a strategy that's been around for decades, but is particularly relevant
(05:43):
in today's AI-driven market landscape. But before I continue, let me share my new obsession
with a brilliant podcast called The Co-League Experience. I've been completely captivated
by their deep approach to management culture and workplace dynamics. What makes this show
truly stand out is its unique view on leadership. They don't just recycle the same old advice,
(06:10):
but challenge conventional thinking at every turn. Their exploration of organizational
culture and processes resonates so strongly with my own management philosophy. Each episode
is deliberately short, just 10 to 15 minutes, making it perfect for fitting into a busy schedule,
yet packed with actionable insights you can implement immediately. Despite tackling complex
(06:35):
topics, the hosts make listening genuinely fun and engaging. The Co-League Experience has quickly
become my go-to recommendation for anyone looking to transform their management style.
You can find the name of the show in this episode's description. Trust me, this one will
revolutionize how you think about leadership. Now, let's get back to today's topic,
(06:58):
commoditizing compliments. Please go ahead with your first question.
Let's start with the basics. Can you explain what exactly commoditizing compliments means
and why it's such a powerful strategic tool in tech markets? Absolutely, at its core,
commoditizing compliments is a strategic approach where companies try to reduce the pricing power
(07:20):
or relevance of products that complement their own. A compliment is simply a product that makes
the consumption of your product easier or more likely, like blades to razors, cars to tires,
or cloud infrastructure to SaaS applications. When a company commoditizes its compliment,
it's essentially creating what Joel Spolsky called a desert of profitability around its business,
(07:45):
this makes adoption of the company's own product easier by keeping compliment pricing low
and weakening peripheral ecosystem players. It's ultimately about controlling the flow of value in
an ecosystem and directing more of it toward your own product. That's fascinating. You mentioned
this strategy can work for both incumbents and startups. How does the approach differ
(08:07):
depending on which side of the market you're on? While compliment commoditization is often
considered a tool of incumbents with deep pockets, it works beautifully for startups too,
just in different ways. Incumbents typically use it defensively to protect their position by weakening
emergent threats that could come between them and their customers. Microsoft giving away
(08:31):
Internet Explorer to counter Netscape is a classic example. For startups and new entrants,
the approach is more offensive. They attack orthogonally by solving problems emanating from
existing oligopolies, thereby weakening incumbents grip and capturing end user mindshare.
A startup might identify a compliment that's causing friction in the incumbents ecosystem
(08:56):
and offer a dramatically better alternative. In both cases, the goal is to shift market value
flow, but the vectors of attack differ based on position and resources. In the text, you outlined
three distinct strategies for commoditizing compliments. Could you walk us through the first one,
Nuke pricing power, and share some historical examples of when it's worked well? Strategy number
(09:21):
one, Nuke pricing power is perhaps the most straightforward approach. Make a compliment
to your product free or drastically cheaper. One of the most successful examples is
Microsoft's bundling of Internet Explorer with Windows in the mid-1990s. Netscape was
charging for its browser and threatening to become the new platform for applications,
(09:47):
potentially displacing the operating system as the foundation for software development.
Microsoft's response was to give Internet Explorer a way for free and bundle it with Windows.
This forced Netscape to also make its browser free, cannibalizing half its revenue in the process.
While Netscape hoped free browsers would boost its commerce server business,
(10:11):
Microsoft's move had already decimated their momentum. By nuke the pricing power of browsers,
Microsoft protected its position as the dominant application platform and maintained control
of the customer relationship. That's a powerful example. What about the second strategy you
mentioned? How does creating open standards help commoditize compliments? Strategy number two,
(10:35):
create open standards, works by establishing technologies or protocols that reduce switching
costs between different providers of a compliment, thereby diluting their lock-in power.
Google's Kubernetes is perhaps the most masterful example of this approach.
By 2014, Amazon's AWS had a substantial 30% market share in cloud infrastructure,
(10:58):
while Google was struggling with only 5%. Google open sourced Kubernetes, a technology that made
it easier to switch between cloud backends and got Microsoft, Red Hat, IBM, and Docker on board.
This created enough consumer pressure that Amazon was eventually forced to support it in 2018.
(11:20):
The result was weakened AWS lock-in, which supported Google's recovery to 12% market share.
Without this move, today's cloud infrastructure market might be far less competitive.
We're seeing similar plays today with Anthropics model context protocol,
which aims to standardize connectors between AI models and reduce the importance of one-to-one
(11:43):
integrations. That's really interesting, but these strategies don't always work, right?
Can you share an example where commoditizing compliments backfired on a company?
You're absolutely right. These strategies can absolutely backfire if not executed properly.
Sun Microsystems offers a cautionary tale. In the early 1990s, Microsoft began pushing into
(12:06):
server software, threatening Sun's core hardware business. Sun's response was to open source Java
and release OpenJDK in 2007, hoping to reduce the switching costs between operating systems
and make OS less relevant. While this textbook application of a strategy number two
(12:26):
did succeed in eroding the importance of the OS, it had an unintended consequence.
It also made hardware easier to commoditize, reducing customer lock-in with Sun's expensive
machines. Even worse, Sun failed to capitalize on the thriving ecosystem it created with Java,
IBM's web sphere, Red Hat JBoss, and Oracle's WebLogic all built billion-dollar businesses
(12:52):
on Java within a decade, while Sun was nowhere to be seen. The lesson here is that commoditizing
compliments requires careful consideration of second-order effects and a clear plan to capture
the value you're unleashing. Let's move to your third strategy, eat the compliment. This seems
different from the other two. How does this approach work and can you share a successful
(13:16):
implementation? Strategy number three, eat the compliment involves vertically integrating to
provide the compliment yourself, often leveraging economies of scale or scope.
Salesforce offers a brilliant example with their SAS model for CRM software. In 1999, CRM leader
(13:37):
Siebel Systems required not just expensive software, but also costly hardware, server
software, and systems integrator services, pushing the first year cost to nearly $10,000 per employee.
Mark Benioff's Salesforce disrupted this by expanding vertically to in-house all
software infrastructure, selling usage on a multi-tenant basis. Essentially, Salesforce
(14:03):
created a group purchasing organization, buying infrastructure on customers' behalf at scale.
This achieved several things at once. It removed the burden of infrastructure procurement from
customers, enabled more efficient resource allocation, and reduced infrastructure provider
pricing power by replacing fragmented buyers with a single powerful one. This approach opened CRM to
(14:30):
previously underserved SMB markets and created a fundamentally easier adoption path. When we look
at these strategies in today's market landscape, particularly with the rise of AI, how is compliment
commoditization becoming even more relevant? The rise of AI, particularly LLMs, has created
various novel avenues for wedge products and is making compliment commoditization more relevant
(14:54):
than ever. We're seeing some of the fastest growing B2B applications today in vertical AI,
using capabilities like voice, summarization, and text generation to disrupt established workflows.
The challenge for these new entrants is whether they can create enough defensibility
to establish lasting platforms. LLM rappers alone aren't enough. They're easy to build and copy.
(15:21):
Meanwhile, vertical incumbents like Procore and Service Titan are already deploying strategy
number one by creating their own LLM features to stifle competitive threats.
Successful vertical AI companies will need to do more than innovate at a single layer of the
customer value chain. By leveraging compliment commoditization strategies strategically,
(15:44):
founders can weaken incumbent lock-in while accelerating their reach across multiple layers
of customer value. You mentioned Chegg as a case study of incumbent vulnerability to LLM
functionality. Could you elaborate on what happened there and what lessons it offers?
Chegg represents an extreme cautionary tale for incumbents in the AI era. Between 2015 and 2022,
(16:09):
they grew to over 8 million users as a vertical SaaS company providing online tutoring and textbook
rentals. But when ChatGPT launched, it effectively open sourced educational texts and answering
capabilities that were previously Chegg's core value proposition. The result was devastating.
(16:30):
Chegg lost 25% of its subscriber base and experienced a 93% stock price drawdown.
This was an unintentional but textbook example of strategy number one,
nuking pricing power as ChatGPT made freely available content and services that Chegg had
been charging for. The key lesson here is that data moats, especially in the LLM era,
(16:56):
must be truly proprietary. If your core value can be replicated by general purpose AI,
you're extremely vulnerable to disruption. Companies need to identify where their true
differentiation lies and ensure it's not easily commoditized by emerging AI capabilities.
When we think about these strategies historically, Microsoft seems to appear in many of the examples.
(17:22):
What can we learn from their mastery of complement commoditization over the decades?
Microsoft's recurring appearance in these examples is no coincidence. They've arguably
operationalized complement commoditization more successfully than any other company in history.
Their journey began with a lesson in complement commoditization when IBM commoditized its own
(17:43):
hardware by using an open architecture for its PCs, allowing Microsoft's DOS to skyrocket in value.
Bill Gates internalized this lesson deeply. When Netscape threatened Windows' position as the
application platform, Microsoft gave away Internet Explorer for free. When Linux threatened Windows
(18:05):
server dominance, they created .NET to make the underlying OS less relevant. The pattern continued
with free developer tools that drove adoption of their platforms. What we can learn from Microsoft
is the importance of identifying existential threats early, particularly layers that might
emerge between you and your customers, and acting decisively to commoditize those threats before
(18:31):
they can establish pricing power or lock in. For founders looking to disrupt incumbents today,
what are some of the key prerequisites for successfully executing these strategies?
What could make them fail? There are two critical prerequisites for successful
complement commoditization. First, you need to have a clear understanding of the value flow in
(18:53):
your ecosystem and identify exactly which complements are creating friction or capturing
disproportionate value. Second, you need a concrete plan to capitalize on the value you're
unleashing. Some microsystems failed not because Java wasn't successful, but because they didn't
capture the value Java created. Common reasons for failure include misjudging second order
(19:17):
effects, as with Sun's hardware commoditization, lacking the resources to sustain the strategy,
especially for startups using strategies under one, or failing to recognize when the complement
you're targeting might actually be your core value proposition in disguise. Additionally,
vertical integration, strategy number three, requires different operational capabilities
(19:41):
that not all companies possess. Successful execution requires careful planning,
sufficient resources, and a keen eye for how value will shift once your strategy takes effect.
You talked about the importance of workflow and data as the immutable primitives of software
defensibility. How do these relate to complement commoditization strategies in vertical AI?
(20:04):
Workflow and data are indeed the foundational elements of software defensibility,
and they intersect with complement commoditization strategies in important ways for vertical AI
companies. The strongest workflow modes draw on multi-stakeholder network effects,
meaning they become more valuable as more different types of users engage with them.
(20:27):
For vertical AI companies, complement commoditization strategies can help accelerate the
across multiple layers of customer value chains. For instance, a vertical AI startup might use
strategy number two, creating open standards to reduce friction and data integration with
existing systems, making it easier to establish their workflow as the new default. Or they might use
(20:53):
strategy number three, eating the complement by vertically integrating capabilities that incumbents
rely on third parties for, thereby creating a more seamless experience. The key is using these
strategies to strengthen rather than dilute your core defensibility. If your differentiation comes
(21:13):
from proprietary data, you want to commoditize the parts of the stack that make that data more
accessible or valuable without commoditizing the data itself. Let's discuss specific industries.
Are there particular vertical markets where these strategies are especially effective,
or where we're likely to see significant disruption through complement commoditization?
(21:34):
Industries with high fragmentation of stakeholders, complex workflows, and expensive or friction-filled
compliments are particularly ripe for these strategies. Healthcare is a prime example.
The entire system is riddled with intermediaries capturing value while creating friction.
We're already seeing AI companies commoditizing aspects of medical documentation, coding, and
(21:59):
even certain diagnostic processes. Construction tech is another area where these strategies
are proving effective. Companies like Procore established strong positions as systems of record,
but the actual workflows involve multiple stakeholders, contractors, subcontractors,
(22:20):
suppliers, inspectors, creating opportunities for startups to target specific complement pain
points. Legal tech, real estate, and financial services are similar with entrenched incumbents
dependent on various compliments that could be commoditized. The common thread is identifying
markets where the value captured by various participants is significantly misaligned with
(22:45):
the value they create, suggesting opportunities for strategic commoditization.
Looking at strategy number three, eat the complement. You mentioned toast pivoting from
an app reliant on existing POS systems to developing their own hardware. How common is this vertical
integration strategy for startups, and what are the challenges? Vertical integration through
(23:07):
eating the complement is becoming increasingly common for startups facing integration challenges
with legacy systems. Toast's pivot from being an app dependent on existing POS systems to
developing their own hardware was initially born of necessity. They discovered it was simply too
difficult to integrate with legacy systems. This approach comes with significant challenges.
(23:33):
It typically requires more capital, introduces operational complexity with new skill sets,
and extends time to market. However, the benefits can be substantial. Toast's hardware move helped
them achieve approximately 15% market share and created a more seamless experience for restaurants.
(23:54):
We're seeing similar moves across various verticals. Startups that begin with software
offerings but discover that controlling a critical complement gives them both greater value,
capture, and better user experience. The key question for founders is whether the integration
pains with existing systems are a temporary hurdle or a fundamental structural problem that
(24:17):
requires vertical integration to solve properly. As we look to the future of AI driven markets,
how might the dynamics of complement commoditization evolve? Are there new strategies emerging?
The dynamics of complement commoditization are evolving rapidly in AI driven markets with
several interesting new patterns emerging. One development is what we might call data reciprocity.
(24:42):
Companies offering AI tools that generate value for customers while simultaneously
improving the company's models with the data collected, creating a virtuous cycle that's
harder for incumbents to replicate. Another emerging approach is capability commoditization,
making specific AI capabilities freely available while monetizing the integration or
(25:05):
orchestration layer. For instance, offering free entity extraction but charging for workflow
automation built on those extractions. This is similar to strategy sure one but more nuanced
in how it segments value. We're also seeing the rise of composable AI ecosystems where companies
create open frameworks for combining various AI capabilities, commoditizing individual models
(25:32):
while capturing value in the composition layer. This represents an evolution of strategy number two
focused on interoperability standards between AI components rather than hardware or software
platforms. As these markets mature, I expect we'll see increasing sophistication in how
companies identify and target the most strategic compliments to commoditize while protecting
(25:57):
their core value proposition. For those listening who are building vertical AI applications now,
what's your most important piece of advice when thinking about complement commoditization as a
strategy? My most important advice for vertical AI founders is to be extremely clear about where
your enduring value creation and capture will come from before deploying complement commoditization
(26:19):
strategies. LLMs and AI capabilities are themselves being commoditized rapidly,
so wrapping them isn't enough. You need to identify the workflow, data, or integration
points where you can create sustainable differentiation. Start by mapping the entire ecosystem in your
(26:39):
vertical, identifying all the compliments to your core offering and understanding where friction,
cost, or inefficiency exists. Then analyze which compliments, if commoditized,
would most accelerate adoption of your product while also strengthening your defensive moat.
(27:00):
Remember that product strategy in vertical AI is still a nascent discipline and will likely
not see consensus until we have a new generation of case studies. The combination of uncertainty,
promise, and early purchase we're seeing makes this an incredibly compelling moment for founders
willing to think strategically about how to disrupt incumbents through smart complement
(27:23):
commoditization. Just make sure you're clear about which part of the value chain you're actually
trying to own long term. This has been an illuminating conversation about disruption strategies.
To wrap up, what final thoughts would you like to leave our audience with regarding the future
of complement commoditization in the age of AI? The rise of AI, especially LLMs, represents a
(27:46):
paradigm shift similar to the introduction of the internet or mobile computing. These transitions
have historically been moments when new entrants could use complement commoditization strategies
to displace seemingly invincible incumbents. I believe we're in another such moment now.
What makes this period particularly interesting is that the rate of capability advancement
(28:10):
in AI is so rapid that identifying which parts of the value chain will remain defensible is
challenging. This creates opportunities for bold strategic moves. The companies that will thrive
won't just be those with the best AI technology, but those who most intelligently identify which
complements to commoditize and which to control. We're likely to see power shifts across virtually
(28:36):
every vertical software market in the coming years, and complement commoditization will be a
key strategic tool for both the disruptors and the incumbents trying to defend their positions.
For those willing to study these patterns and apply them thoughtfully, there's tremendous
opportunity to reshape markets and create enduring value. Yaakov, thank you so much for
(28:58):
sharing these valuable insights on complement commoditization strategies. I'm sure our audience
has gained a deeper understanding of how these approaches can reshape competitive dynamics in
technology markets. We appreciate your time today. Thank you, Donna. It's been a pleasure
discussing these strategies with you. I hope your audience finds these frameworks useful
(29:19):
as they navigate the rapidly evolving landscape of vertical software and AI-driven markets.
The strategic principles we've discussed have proven their value through decades of tech history,
and they're only becoming more relevant in today's competitive environment.
(29:40):
That wraps up today's podcast, where we explored the intense competition between rippling and deal
in the HR tech space and examined how companies leverage strategies like complement commoditization
to stay ahead in the AI-driven market. Don't forget to like, subscribe, and share this episode
with your friends and colleagues so they can also stay updated on the latest news
(30:03):
and gain powerful insights. Stay tuned for more updates.
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