Two broad themes preoccupy my thinking lately:
First, Meta’s recent earnings call and the emergence of Deepseek challenge my prediction of “an explosion of new businesses of storytelling”; and,
Second, artificial intelligence will reshape both military-industrial era strategy and its modern consulting-firm iterations.
New evidence since the holidays strengthens the first point, which merits its own essay.
My research into the second point remains preliminary, but I will outline my initial thesis below.
Key Takeaway
Why would management teams that failed to understand DTC technology and business models suddenly build effective AI strategies and operational structures? Even Netflix seems vulnerable now.
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Last summer, I received a head of strategy role offer from a media company's potential acquirer. I subsequently wrestled with two questions:
What defines a strategist's role in an era of rapid change?
What constitutes corporate strategy when AI threatens to eliminate entire company divisions?
To date, my analysis of the media marketplace has focused on the direct-to-consumer (DTC) relationship as a new “first principle” of building a media business. I have frequently cited venture capitalist Marc Andreessen's test for management teams' understanding of DTC business models and their underlying software: Examine the percentage of top 100 executives and managers with computer science degrees.
Last September, I argued that there are two conflicting theories at play in the media industry. Legacy media executives, in pursuing DTC models, have overemphasized Clayton Christensen's disruption theory while neglecting Marshall McLuhan's principle that 'the medium is the message.'"
Christensen argues that incumbents face a capital allocation choice: Generate long-term growth or adapt to change. McLuhan posits that adaptation demands understanding new mass media distribution technologies and the constraints they impose on both companies and consumers.
The middle ground between the two theories is that successful capital allocation and adaptation requires management to understand the underlying technology. Management teams that fail to comprehend these technologies will inevitably misallocate or suboptimally spend capital. I have long argued that this business logic explains why many media companies have struggled in interactive businesses like streaming and gaming.
In this light, the answer to my first question is simple: Heads of strategy at media companies must be as versed—if not more versed—in new mass media distribution technologies as they are in disruption theory.
However, the answer to my second question is less clear. Why would management teams that failed to understand DTC technology and business models suddenly build effective AI strategies and operational structures? Why would they be able to identify marketplace opportunities and allocate spending wisely?
If they could not adapt quickly (nor slowly) to DTC models, why would they suddenly develop the ability to invest at AI's rapid pace?
The AI Lords of Strategy
Management consulting businesses have emerged to help solve such questions over the past 60 years. However, their early wins focused more on data aggregation and frameworks to help management teams allocate capital more intelligently.
I am reading "The Lords of Strategy: The Secret Intellectual History of the New Corporate World" to understand the early days of management consulting firms because they emerged during a transformative period for industrial and post-industrial corporations (1960s and 1970s). Management teams struggled to comprehend the dizzying speed and scale of the significant market shifts: Government deregulated major industries, new technologies proliferated, capital markets shed past moral and legal constraints, and globalization reshaped and expanded supply chains and customer bases worldwide.
We can theorize about AI's potential changes to marketplace supply and demand, but ultimately, we do not know precisely what will happen. The implication of Andreessen's test and streaming's precedent is that a select few executives with backgrounds in computer science and language learning models will have a more nuanced understanding of AI's trajectory. They are more likely to identify and build the “right” strategies.
The challenge in the age of AI is that LLMs trained on the capital allocation frameworks—like BCG’s Matrix —can now answer strategic questions, too. Even if imperfect, these responses reveal the core principles and fundamental outlines of established strategic approaches. One must wonder how management teams will balance the cost effectiveness of using AI platforms with the need for bespoke, more imaginative consulting that those AI platforms cannot deliver.
Needless to say, management teams favor cost-effective solutions with higher potential return on investment over bespoke human consulting. The question will be, at what cost to shareholders?
Watch Netflix
I have long argued that Netflix has “won” streaming partly because its management team has backgrounds in computer science. They understood from day one that “the medium is the message” and built technology that maps to the internet’s “invisible groundrules, pervasive structures and overall patterns”. Where legacy media perceived a new channel for content distribution emerging, Netflix management saw a technological means for delighting consumers with personalized entertainment recommendations.
In last September’s “YouTube Is Forcing Netflix to ‘Break’ Its Business Model”, I argued that Netflix now seems vulnerable because YouTube has redefined “premium content” with the help of creators. It has transformed consumers' investment in content from something emotional into something financial.
Netflix now seems to be evolving more slowly than necessary. Management continues to repeat in its letter to shareholders that it accounts “for less than 10% of TV viewing in every country in which it operates." The data “suggests a long runway for growth as streaming continues to expand around the world.”
The performance of its stock—its price is up 75% year-over-year and 13% since last week’s earnings call—suggests that investors currently see this long runway and do not see those limits.
The critical question for Netflix management is whether AI can help it compete with YouTube and other "formidable competitors across traditional entertainment and big tech":
Can AI help it to capture more than 10% of TV viewing in every country?
Can AI help it to pivot towards consumers’ growing role as producers of the content they watch?
Can AI help it to adapt to consumer tastes being increasingly shaped by creators and short-form content?
Can AI help it to succeed at “a 'once in a generation’ inflection point for game development and player experiences using generative AI”, as Mike Verdu—Netflix’s VP, GenAI for Games—wrote last November?
I think the answer is no to the first three questions, and maybe to the last.
The implication is sobering: Even with the right management team, Netflix's future appears smaller and more uncertain than it should be. A recent pivot towards AI—evident in job listings from last October—seems unlikely to change this trajectory.
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