The most telling detail is not that Sam Altman finally said something flattering to non-technical founders. It is that an outlet calling itself Crypto Briefing published it with no cryptographic content whatsoever. No hashes. No market structure. No governance model. No token. No audit trail. Just a high-level business narrative resting on two claims, neither of which links to a reproducible dataset, a benchmark, or an architecture diagram.
In due diligence, this is what we call an information margin squeeze. The signal-to-noise ratio is so low that the event of publication becomes the only verifiable fact. I read the piece three times because first pass suggested I had missed an exhibit, a footnote, or at least a link to a technical paper. By the third pass I realized the absence was the analysis. Anything with this much narrative and this little falsifiability deserves a forensic-style teardown, not a summary.
The two claims, isolated like pathogens on a slide:
- Generative AI is shifting startup success from technical prowess to user insight.
- This shift will redefine founder dynamics and investment strategies.
That is the complete cargo manifest. A confidence grade of D-minus feels almost generous. E-minus is closer to the truth for several dimensions, and the gap between the strength of the assertion and the thinness of the evidence reveals an uncomfortable pattern that extends beyond this one article.
We are not dealing with journalism. We are dealing with positioning architecture, and the subject of that architecture is not the reader. It is the author's commercial ecosystem.
THE ANOMALY HOOK
Everyone in crypto claims the industry is evolving. The data suggests that crypto media is being hollowed out by generative search arbitrage. A publication specializing in blockchain assets republished an AI narrative that contains zero blockchain assets, zero protocol analysis, and zero user-level financial reasoning. That should bother us more than it does.
Why? Because the absence of blockchain content in a blockchain news outlet is not an editorial oversight. It is a signal. It tells us the article was chosen because it travels, not because it informs. Sam Altman quotes generate attention. Attention generates ad impressions. Impressions generate revenue. The underlying validity of the claim is irrelevant to that pipeline.
I have spent thirteen years watching this pattern repeat. In 2017, it was whitepapers filled with buzzwords. In 2021, it was metaverse manifestos. In 2024, it was artificial intelligence grafted onto layer-one narratives. Today, the pipeline has become so automated that the output no longer pretends to contain a thesis. It just gestures toward one.
The gesture is what we must dissect. Altman is telling a specific story: generative AI has lowered the cost of building, so the residual skill that separates winners from losers is now user insight. The story is coherent enough that many readers will nod along. That is exactly why we need math.
Let me state the original technical claim plainly. The article does not describe any AI model architecture, any transformer variant, any state-space model, any hybrid approach, any training methodology, any inference budget, or any engineering implementation detail. It is not a technology report. It is a business commentary with Sam Altman's authority attached as the credibility anchor.
This matters because crypto natives know what happens when authority anchors substitute for evidence. We watched Terra's founder anchor billions of dollars with little more than confidence and a marketing machine. We watched centralized lenders anchor retail deposits with celebrity endorsements and opaque balance sheets.
Altman is not Terra. OpenAI is a real company with real models. But the structural pattern is identical: an influential voice makes an unfalsifiable claim about the future; media amplifies it; no one asks to see the variable isolation.
CONTEXT: WHY THIS ARTICLE EXISTS
To understand why a crypto outlet would run an AI commentary with no crypto content, we need to map the incentives.
Sam Altman functions as a narrative anchor for an entire industrial complex. His public statements move capital. Y Combinator-backed founders hang on his words. OpenAI's API revenue depends on the belief that many people can build products on top of foundation models without being deep-learning researchers. That belief is commercially necessary, and Altman's statement directly reinforces it.
The argument is self-serving in a way that becomes visible once you invert it. If success shifts from technical prowess to user insight, then anyone with product intuition and an OpenAI API key can become a founder. This expands the potential customer base for Altman's own platform. It is not wrong because it is self-serving. It is wrong because the causal chain has no evidence.
We are also seeing an aggressive convergence narrative in digital assets. Project after project brands itself as "AI x Crypto" while deploying no privacy-preserving inference, no verifiable compute, and no decentralized training. In my audits of AI-crypto convergence projects, I have repeatedly found architecture claims that collapse upon inspection. One project said it offered decentralized compute but routed all jobs through a handful of centralized API gateways. The decentralization rate was functionally zero.
Altman's commentary feeds this convergence, but not through technical depth. It feeds it through vocabulary. "Founder dynamics," "investment strategies," and "user insight" are words that fit neatly on a pitch deck, even when no protocol exists behind them. We have seen the same rhetorical pattern in DAO governance literature. Every organization claims transparency; few publish grant committee voting records that can withstand independent review.
Optimism's RetroPGF remains the rare example where public-goods funding has an actual paper trail. RetroPGF allocates rewards based on measurable impact, and the mechanism is public enough that independent analysts can contest the results. The contrast is instructive. When a system is serious, it produces artifacts you can audit. When it is performative, it produces press releases.
The original article produced only press-release energy.
CORE: SYSTEMATIC TEARDOWN OF THE ASSERTION
Let me walk through each dimension I would check if this were a due diligence target. I do this not to humiliate a single author but to demonstrate a repeatable method. A piece of content is like a protocol: its true quality appears only when you test its claims against observable reality.
Dimension One: Technical Architecture
There is no architecture here. The article does not describe how generative AI lowers the cost of building. It does not mention AI code assistants, autonomous agents, or human-in-the-loop frameworks. It provides no mechanism through which "user insight" becomes the decisive variable.
This matters because the mechanism is everything. If AI code assistants like Cursor and GitHub Copilot reduce the time required to produce a functional prototype from six months to six days, then technical founders lose some of their former advantage. That is testable. GitHub has released data on Copilot adoption. Developer surveys repeatedly show productivity gains in certain coding tasks. None of that appears in the article.
If, conversely, AI agents now handle an increasing share of user research, then the term "user insight" becomes ambiguous. Does the founder still perform the insight, or does the AI discover the segment and the founder merely validates the prompt? The article does not say.
My experience auditing early-stage ventures tells me that the reality is messier than Altman's narrative. Generative AI lowers the cost of the first draft, not the cost of understanding a market. Coffee shops are not full of founders who turned a ChatGPT prompt into a scalable business. The prototypical successful AI-era founder I observe has three characteristics: domain depth, distribution familiarity, and the patience to iterate against a real audience.
Coding skill is becoming less differentiating at the shallow end and more differentiating at the deep end. That is an important nuance. Founders no longer need to hand-code an entire backend to test a product hypothesis. But founders who cannot reason about retrieval pipelines, data leakage, latency budgets, or security boundaries will produce products that collapse in production. The article treats software engineering as a commodity that has been fully abstracted. It has not.
The deeper knowledge gap is psychological. The article claims that startup success hinges on user insight, but it never defines what user insight means in a measurable way. Is it qualitative research? Quantitative behavioral analysis? A/B testing velocity? Churn decomposition?
If user insight is measurable, then we should see correlation studies linking founder-level insight scores to organic retention curves. No such study appears in the article.
If user insight is not measurable, then Altman's claim is dangerously close to what we call survivorship attribution. We observe successful founders and retroactively assign them "user insight" because they succeeded. The variable explains the outcome only because we defined the variable after seeing the outcome.
In forensic analysis, this is called circular reasoning. And circular reasoning is the native language of narrative-driven markets.
Dimension Two: Commercialization
The article argues that investment strategies are being redefined, yet it provides no commercial path, no pricing logic, no target customer profile, and no gross margin analysis. We are told that founders who prioritize user insight will succeed, but not how their companies will monetize.
In my line of work, a claim without a commercial path is a claim without a terminal value. You cannot discount future cash flows from a vague shift in founder archetypes.
Consider what a rigorous version of this thesis would include:
First, an analysis of whether AI-era startups should charge per user, per workflow, or per outcome. The article is silent.
Second, an examination of closed-source versus open-source strategies. If insight matters more than code, do brands need proprietary models, or can they build on open-weight systems? The article does not engage.
Third, a sustainability model. If the only differentiator is product taste, then the market may become flooded with shallow wrappers. A thesis that predicts a world of many thin, insight-led companies should also explain how those companies avoid gross margin compression. It does not.
The absence of commercialization logic is not an omission. It is a sign that this is a story about founders, not a story about businesses. Stories about founders attract media attention. Spreadsheets about gross margins do not.
Dimension Three: Industry Impact The original article fails to estimate any industry impact. There is no timeline for how quickly the founder archetype shifts. There is no assessment of which sectors will see the deepest change. There is no discussion of software development, content production, customer support, or vertical AI applications.
I would expect an argument this large to come with a time gradient. A claim that technical founders are losing their edge is meaningless without specifying whether that thesis applies in six months, eighteen months, or five years. The article treats founder dynamics as if they exist outside of time.
Meanwhile, in the real economy, technical depth remains concentrated at the model frontier. Training-run costs have escalated. Infrastructure ownership is consolidating. Algorithmic research is as competitive as it has ever been. "Idea guy" founders who stand on top of this stack may indeed build valuable products, but the stack itself was built by deeply technical teams with enormous capital reserves.
A more honest framing would be: user insight is becoming more important at the application layer, while technical complexity is increasing at the infrastructure layer. Those two forces coexist. They do not cancel each other out.
The article also ignores employment effects. If generative AI lets one founder do what a team of five engineers used to do, then the venture labor market will absorb a structural shock. For crypto, the extension is obvious: DAOs once promised to remove gatekeepers and enable global coordination. If crypto fails to differentiate itself from centralized AI platforms, it risks becoming redundant.
Dimension Four: Competitive Landscape and the False Promise of the "Idea Guy"
Let me address the elephant in the room. When Altman talks about user insight, he is speaking from the top of the most privileged observatory in artificial intelligence. OpenAI occupies a unique position in the competitive landscape. That position shapes his perspective in ways he may not fully disclose.
From that position, technical differentiation looks less important than product adaptation. But from the perspective of a founder raising a seed round, OpenAI is not a neutral actor. OpenAI is both the platform provider and a potential competitor. Consider the structural advantage OpenAI has when a founder builds on top of OpenAI APIs. The founder captures the domain insight, but OpenAI captures the workflow data.
This is where I see the deepest danger. Altman's narrative encourages founders to believe their user insight is intellectual capital. In reality, every time an AI startup interacts with a centralized model provider, behavioral traces flow to the platform. Over time, the provider learns the startup's users, usage patterns, and potential pivot points. This does not mean founders should avoid OpenAI, but it means the "insight-driven founder" narrative obscures a new dependency layer.
Your alpha is someone else's aggregate training data.
The original article does not mention this dynamic because doing so would expose the commercial self-interest underneath the thesis. A true analysis of founder dynamics in the AI era must account for platform risk. The article does not.
Dimension Five: Ethics, Safety, and Privacy
This dimension receives zero attention. There is no mention of hallucinations, bias, jailbreak risk, model alignment, or the social consequence of storytelling that encourages non-technical founders to rush into AI markets.
Let me be specific. A founder who trusts generative AI to identify user needs but cannot detect a hallucinated focus-group summary is not a founder armed with insight. That founder is a liability. Generative models are not objective arbiters of user desire. They are probabilistic machines trained on patterns in historical data. They will confidently produce product ideas that are plausible but wrong.
The article does not warn against this. High-quality commentary on startup strategy should include the operational risk of generating synthetic user signals. The absence is especially noticeable because executives in AI often claim to care about alignment.
There is also a privacy dimension. If "user insight" becomes the defining success variable, then the ability to detect and analyze user behavior becomes the defining strategic asset. Will founders collect behavioral data with informed consent, or will they quietly scrape and infer? The article does not address this, and that silence is not innocent.
We need only look at Altman's own history regarding human data as a sensitive asset. Project Worldcoin, which Altman has publicly supported, sought to build a global identity network using iris scans. The ethos behind Worldcoin is controversial precisely because biometric data is difficult to anonymize, difficult to revoke, and difficult to trust when collected and stored by a corporate entity.
The crypto community has a natural defense mechanism against this: decentralized identity, zero-knowledge proofs, and on-chain transparency. But if we accept Altman's narrative at face value, we may overlook the regulatory and ethical architecture required to make user insight trustworthy in the first place.
Dimension Six: Investment and Valuation
The article implies that investor preferences are changing but provides no standard deviation, no valuation multiples, no deal-flow data, and no benchmarks. In my audits of crypto ventures, I have learned to treat valuation claims without comparable data as a form of storytelling, not analysis.
Let me offer an alternative view based on observable trends in startup financing. It is true that investors increasingly want founders who understand users. But they also want technical credibility, capital efficiency, and defensibility. Those factors rarely exist independently.
The most successful AI-era founders I have met can articulate trade-offs at the architecture level and the product level. They know when to fine-tune a model, when to rely on retrieval-augmented generation, when to design around a context window, and when to avoid a model altogether. This is not user insight replacing technical skill. It is technical skill becoming fluent enough to serve user insight.
A smarter hypothesis for investors is that the bar for AI founders is rising on both dimensions, not shifting from one to the other. The age of the autodidact who can write a month-end script to validate an idea may be passing, but the age of the founder who ignores technical detail is not arriving.
Dimension Seven: Infrastructure and Compute
The original article does not mention compute, inference cost, data-centers, GPU supply, or energy consumption. Again, this is not an incidental omission. It is a structural blind spot that reinforces the narrative that software is becoming easier.
Software is becoming easier to write. Compute is not becoming easier to own. Cloud costs remain a near-universal pressure on AI startups. Distributed training is still overwhelmingly centralized in practice.
When I evaluated a batch of blockchain projects claiming decentralized compute, I found a common problem: the projects had no cost advantage over cloud hyperscalers. They had a token supply and a governance layer, but their latency, reliability, and security model depended on Amazon Web Services or Google Cloud. The pretence of infrastructure innovation had collapsed under even mild load testing.
If generative AI truly shifts value from technology to user insight, then the market should show abundant venture funding flowing to non-technical founders who spend a small amount on cloud computing and a large amount on research. I am not seeing that pattern in a consistent way. Instead, I see successful teams using AI to compress the development cycle while retaining deep technical competence.
The infrastructure story is also critical for the crypto x AI intersection. If the future of AI is a small number of centralized platforms holding compute and user data, crypto's relevance is minimal. If the future involves verifiable provenance, auditable inference, decentralized fine-tuning, and user-owned data, then technical infrastructure becomes the most important part of the equation.
Altman's narrative points toward a world where user insight is so powerful that the architecture below it barely matters. That is a comfortable story for a platform provider. It is a dangerous story for anyone who values autonomy.
THE CONTRARIAN ANGLE: WHAT THE BULLS GOT RIGHT
Criticism is incomplete if it only attacks. I want to be fair and give credit where the overarching argument has merit.
The first legitimate point is that crypto and Web3 markets have historically been led by technical ideologues who never bother to observe the user. There is a pattern where founders build infrastructure no one uses and call it failure of adoption when it is actually failure of understanding.
Uniswap succeeded not because it introduced the first automated market maker concept, but because its developer recognized how much capital inefficiency lived inside traditional order books. OpenSea found initial traction because it understood that collectors wanted social proof, not merely ERC-721 metadata. In too many protocols, user experience is treated as an afterthought.
A narrative shift toward user insight would improve the industry. It would encourage founders to account for onboarding friction, token design incentives, governance participation costs, and the cognitive overload that plagues wallet users.
Second, the original article is right that generative AI compresses the early engineering cycle. This has observable consequences. A two-technical-founder team with a very specific production experience can no longer assume it has years of time before catching the attention of larger organizations. Prototypes are faster. Competitors clone faster. In that environment, real user obsession is a defensible advantage.
Third, the psychological framing has value. If we tell ourselves that only people who can build AI infrastructure from scratch can create meaningful AI products, we will reduce innovation to incumbents. We need more founders with strong intuition about niche markets, vulnerable populations, and overlooked workflows.
But we also need to hold those founders to engineering and safety standards. The answer to technical arrogance is not naive user evangelism. It is disciplined product engineering combined with responsible deployment.
I would rephrase Altman's thesis for a serious audience:
Generative AI is shifting startup differentiation from the ability to implement ideas to the ability to identify which ideas are worth implementing. That shift does not equalize everyone, and it does not remove the responsibility to understand models. It reallocates the bottleneck.
A better version of this article would have examined the bottlenecks concretely. Which lower model improvements will allow an idea-focused founder to spend more time on user research? What infrastructure layers still require specialists? What does a modern research stack look like? None of that appears in the original output.
THE BLIND SPOT OF CRYPTO x AI
The deeper lesson is about how media degenerates when it refuses to maintain product integrity.
Crypto media outlets know that AI content outperforms blockchain content in open web traffic. AI promises economic transformation; blockchain sounds like complicated infrastructure. Outlets respond by publishing more AI and less crypto.
But when an AI article appears without any mention of trust minimization, transparency, or decentralized verification, it does not belong in a blockchain publication. There is a direct contradiction between the platform's identity and the article's ideological posture.
The stated promise of crypto is that we can reduce reliance on centralized authorities. The actual promise of Altman's OpenAI ecosystem is to become the reliable centralized authority of AI products. One of these promises is likely to fall, and the resolution matters for the future of both fields.
Consider what Ethereum-style self-custody means for user insight. If users own their data in a self-sovereign identity system, they can choose which applications can observe behavior. Genuine user insight becomes negotiated rather than harvested. That is a dramatically more responsible foundation than a model endpoint that goes to a lab that then fine-tunes its next product on your behavioral trace.
This is where a serious article would draw the line: technical founders in AI are losing leverage not because code is easy, but because the platform that supplies intelligence is becoming increasingly closed. The way to restore leverage is not more user interviews. It is verifiable open infrastructure.
The original text was published right through that contradiction without noticing.
MY OWN AUDIT BIAS
I need to disclose my bias. I have seen too many DAO grant committees operate without transparency, and I have seen too many AI-x-crypto startups fake decentralization by hosting a frontend and a governance token on AWS. This experience produces an instinct. When someone claims power is shifting from technical builders to user sensemakers, I immediately ask where the auditability lives.
In crypto, we quantify trust. A token distribution schedule is auditable. A smart contract code path is auditable. A reward treasury can emit a Merkle proof. None of that culture appears in the generic AI startup commentary.
There is something almost malicious about telling non-technical founders that their user intuition can be an alternative to understanding model limitations. This narrative suits the age of content production, not the age of ownership. We are producing thousands of news posts each day, but we have fewer verifiable facts per article than we did in the age of longform reporting.
If I were conducting a due diligence review of this article as a piece of content, I would flag it exactly as if it were a whitepaper with no token mechanics.
THE MEDIA ACCOUNTABILITY PROBLEM
The platform that published this analysis has a responsibility to its readers. Crypto audiences are optimization-oriented. They want to know if a trend will survive contact with capital market realities. That readership deserves an editor asking whether a claim is falsifiable.
Instead, the content industry is optimized to produce shareability. Generative AI has made it even easier to write plausible commentary that contains no durable insight. The result is a growing inventory of articles that mirror the market's desire for certainty without delivering the evidence needed to support that certainty.
I think about this in the same terms as Bitcoin. For years, critics dismissed Bitcoin ordinals as a useless novelty. I found that perspective superficial. Whatever one thinks of the inscriptions, they injected fee revenue and attention into Bitcoin at the very moment its security model needed non-block-reward incentives. The ordinals phenomenon forced people to account for actual transaction demand. That was a mathematical reality.
The lesson is to measure demand, not to worship or dismiss narrative. The original Altman piece offered no transaction demand, no metric layer, and no measurement method. It was a narrative without a denominator.
WHAT A REAL ARTICLE WOULD HAVE LOOKED LIKE
Let me spend a moment imagining what would have made this claim creditable. Any serious analysis of founders and generative AI would need at least four artifacts.
First, a level-setting analysis of coding abstraction. The article should explain how code generation tools, AI-assisted architectural design, and agent-driven debugging actually change the velocity of an early-stage team. It would acknowledge that rapid prototyping reduces funding requirements at the pre-seed stage but does not eliminate the need for technical founders in regulated sectors.
Second, a robust empirical measurement of user insight. The article should define user insight as a composite of user retention, session depth, organic referrals, and practitioner-interview quality. It should compare a cohort of technical founders with a cohort of product-led founders and show which metric best correlates with long-term revenue.
Third, a comparative view across verticals. A fintech founder requires more regulatory understanding and security rigor than a consumer social content founder. A healthcare AI startup cannot rely on raw empathetic observation because it must navigate clinical validation. The founder skill stack varies by vertical.
Fourth, a scenario analysis. The article should present at least three possible futures. In the first, model commoditization makes user insight the decisive variable. In the second, frontier models consolidate power and technical gatekeepers capture most value. In the third, a hybrid emerges where technical founders who can integrate several model providers become the new elite. Many credible market analysts see the third scenario as most likely.
The original article simply assumes the first scenario. In that respect, its analytical discipline is lower than that of a conservative memecoin prospectus.
STRUCTURAL PATTERNS IN THE TEXT THAT EXPIRE QUICKLY
Let me now highlight the signatures of generated, low-information content that blockchain readers should learn to recognize.
First, authority anchoring: the text borrows credibility from a name. Sam Altman is famous, so the text does not need to prove anything. This is a well-known heuristic for lazy communication.
Second, metaphor substitution: the piece calls a broad shift positively without providing a concrete variable. The reader feels they learned something about the world when they have only received a translation of current VC sentiment.
Third, zero negative space: no counterargument, no competing framework, no mention that foundation labs might prefer high-signal academic AI researchers and carefully selected product designers. The absence of tension is the tell.
Fourth, platform mismatch: materials written for an AI general audience running on a crypto outlet mean the author and editor simply did not track their content taxonomy. In content management, taxonomy mismatch is the same thing as reporting error.
Every one of these signatures appears in this piece. A rigorous analyst and a discerning crypto reader needs to look past the credibility of the authorized voice and ask for data underneath.
DOES THIS CHANGE MY VIEW OF OPENAI?
No. OpenAI builds remarkable systems. The topic of the original article is not a direct technical assessment of OpenAI, and there is no reason to doubt the existence of genuinely useful applications. I simply resist the hidden implication. I do not believe the world needs founders who abandon technical depth because large platforms will handle the complexity for them.
We have been here before in the history of software. Visual Basic promised to make everyone a developer. WordPress promised to make everyone a publisher. Mobile platforms promised to make everyone a designer. Each shift made certain categories of work accessible to more people, but professional depth still mattered. The people who benefited most were those who combined domain empathy with enough engineering fluency to understand platform constraints.
The same dynamic is happening now with generative AI. The arrival of the interface does not end the importance of the foundation underneath it. It just obscures the foundation for those who do not open the hood.
WHERE THE INDUSTRY GOES FROM HERE
We are entering a phase where companies will be judged by whether their decentralization claims match their dependency architecture. The phrase "decentralized AI" is too often a token with a roadmap, not a routing table. We need models that can run in enclaves, datasets that commit to public hashes, and inference that verifies without exposing user data.
If crypto acts as a counterweight, the outcome could be quiet optimism. A future with user-owned data and open-source inference led by genuinely user-focused founders would be radically better than one where every product funnels through a brand-new API monopoly.
But to build that future, we must maintain a clinical skepticism of any article that confidently predicts who will win. History is rarely a straight line from technical capability to user insight. It is a zigzag of platform shifts, competitive responses, regulatory interventions, and accidental discoveries.
An article that takes Sam Altman's two-claim thesis and offers no method to test it is the purest symbol of our current dilemma. We have generated more ways to express opinions and fewer ways to verify them.
TAKEAWAY: DEMAND CONTENT WITH A REPRODUCIBLE PROTOCOL
If I can offer one principle to everyone in this industry, it is this: treat media commentary like a smart contract. If the code has a function that promises a result, run it against test vectors. If the function is missing entirely, do not accept the transaction.
Ask what real mechanism generates the article's claim. Ask what data would disprove it. Ask which assets and actors are aligned with the claim and which are quietly opposed.
The original article about generative AI and startup founders does not pass these checks. That does not mean Sam Altman is wrong about everything. It means the text is not an information product. It is a religious statement about how software should evolve, and religious statements belong somewhere other than an investment bulletin.
Your alpha is someone else's narrative drift. The more words deployed to explain a market shift without sharing a dataset, the faster you should run toward your own charts, local logs, and user interviews.
Crypto was founded on the idea that blind trust is poison. The same principle should apply to industry commentary. We refuse to accept an unverified bridge, but we will happily accept a Sam Altman paraphrase on a blockchain news site with no addressable audit trail.
We should demand more. We should demand that content be as verifiable as the protocol it claims to understand.
If generative AI is really moving startup success away from technical skill, the market will show it through survival data. Let us wait for the data rather than accept the assertion. In the meantime, the best founders I know are those who remain suspicious of easy narratives because they understand that building a business is a technical act, a relational act, and a psychological act all at once. Reducing the story to user insight is as dangerous as reducing it to code.
Cold analysis does not need to insult the reader. It simply needs to show respect for the reader as an agent capable of verifying claims. I am not saying generative AI has no effect on founder dynamics. I am saying the article under review is not evidence for that effect. It is an artifact of media incentives, circulating through a publication channel designed to monetize attention rather than convey knowledge.
Let that be our caution. We are surrounded by content that feels like analysis and operates like advertising. The purpose of due diligence is not to make every conclusion more optimistic. The purpose is to make every claim more honest.
In the long term, the model that survives is the narrative that can be tested. The investor who survives is the investor who demands tests. The founder who survives is the founder who understands that insight without implementation is merely a mood.
Now, go measure something real.