Why Metaverse Failed, AI Succeeds

“Metaverse” just never seemed to catch on, and the issue is “why?” While it is always possible to argue that the concept was simply “ahead of its time,” perhaps there were other issues as well. 

For starters, metaverse was a push toward more immersive, higher-fidelity digital environments. But as with other proposed advancements in digital media, it did not solve a broad, urgent problem for most users.

Television or movies presented in “three dimensions” also arguably are “more immersive” or “realistic,” but that never is enough to create demand. 

But some thought something more was at stake. The metaverse, some thought, would become the next computing platform, the successor to the mobile internet. 

But the technology was not compelling enough; the friction was too high; the value way too limited. The ecosystem, content base and network effects were not there. 

Compare metaverse to artificial intelligence, a general-purpose capability that can attach to almost any cognitive workflow and business process.

Use case	Metaverse value	AI value	Relative usefulness
Virtual meetings / collaboration	Moderate: better spatial presence, but often not better than video calls.	High: summarizes, transcribes, drafts follow-ups, and speeds decisions.	AI
Employee training / simulation	High in physical or risky environments, where immersion helps.	High: creates training content, coaches, quizzes, and personalizes learning.	Tie, slightly AI
Customer support	Low to moderate: immersive support is niche.	Very high: chatbots, agent assist, routing, and automated resolution.	AI
Sales / product demos	Moderate: strong for 3D visualization and experiential demos.	High: personalizes outreach, generates content, and qualifies leads.	AI
Entertainment / gaming	High: this is one of metaverse’s best-fit domains. dreamsoft4u	High: generates content, NPC behavior, personalization, and moderation.	Tie
Education	Moderate to high for immersive labs, historical reconstruction, or anatomy. dreamsoft4u	High: tutoring, summarization, feedback, and adaptive learning.	AI
Healthcare / therapy	Moderate: useful for exposure therapy, rehab, and visualization.	High: triage, documentation, diagnostics support, and patient messaging.	AI
Remote field assistance	Moderate: useful when a remote expert needs the user’s visual context. techtarget	High: guides workers, interprets data, and generates instructions.	AI
Marketing / brand experiences	Moderate: immersive campaigns can be memorable but narrow. dreamsoft4u	Very high: segmentation, content generation, ad optimization, and personalization.	AI
Commerce / shopping	Moderate: 3D storefronts help some categories like real estate or furniture.	Very high: recommendation, search, pricing, and conversational shopping.	AI
Design / visualization	High: strong when spatial understanding matters.	High: concept generation, variants, and analysis, though not always spatial.	Tie
Knowledge work / office tasks	Low to moderate: metaverse mainly changes the interface.	Very high: directly improves writing, coding, analysis, planning, and review.	AI

Where metaverse mainly extends what digital media can look and feel like, AI extends what software can do. 

A virtual world can make communication, entertainment, and simulation more realistic, but it still stays within the realm of mediated experience. 

AI, by contrast, is increasingly useful wherever humans are reasoning, drafting, classifying, predicting, summarizing, planning or making decisions.

AI adds value even when the interface stays ordinary, because it upgrades the work itself rather than just the container around the work.

Metaverse even if all the other issues had not been present) is “only” the next evolution of realism in electronic media, while AI is the next evolution of cognition. 

Perhaps virtual reality will someday deepen immersion for users. 

But AI, in principle, can affect almost every cognitive task because it can assist with language, judgment, memory, analysis, and creativity in almost every domain. 

And adoption barriers are quite low: people can use it right now, with low friction and no new hardware requirements. 

If metaverse was about the realism of the interface, AI  is about the augmentation of cognition itself. 

I don’t recall anybody arguing that metaverse was a general-purpose technology on the scale of electricity, in terms of impact,for example. It’s pretty hard to find anybody arguing AI is less than that.

Will Claude Mythos Preview Help or Harm Security Suppliers?

It now seems almost routine that some new language model emerges to further disrupt some part of the computing industry. First it was chips, processors and memory. Then it was enterprise software. Now it seems to have extended to edge networks. 

The impact on security suppliers is less clear.

Claude Mythos Preview is Anthropic’s most capable frontier AI model to date, announced April 7, 2026), and seems poised to affect security software suppliers, although the direction and magnitude seem unclear. 

Many climbed on the day of the announcement, then retreated afterwards. 

Company	Pre-Announce Close (Apr 6)	Announce Day Close (Apr 7)	Latest Close (Apr 10)	% Change Announce Day (Apr 6 → 7)	% Change Since Announce (Apr 6 → 10)
CrowdStrike	$398.61	$423.23	$379.02	+6.2%	-4.9%
Palo Alto Networks	$161.95	$169.87	$155.73	+5.0%	-3.8%
Cisco	$80.44	$80.68	$82.22	+0.3%	+2.2%
Fortinet	$82.29	$83.72	$76.70	+1.7%	-6.8%
Zscaler	$139.52	$142.09	$118.05	+1.8%	-15.4%
SentinelOne	$13.51	$13.38	$11.94	-1.0%	-11.6%
Cyber Security ETF	$77.41	$78.55	$71.17	+1.5%	-8.1%

Claude Mythos Preview is a general-purpose large language model that shows a major leap in capabilities over predecessors like Claude Opus 4.6, particularly in software engineering, reasoning, agentic tasks, and cybersecurity.

In internal and partner testing, the model autonomously:

• Identified thousands of high-severity and critical zero-day vulnerabilities (previously unknown flaws) in every major operating system, every major web browser, and numerous open-source projects.

• Turned many of those findings into working proof-of-concept exploits (e.g., full sandbox escapes, remote code execution, privilege escalation, and chained attacks).

• Achieved step-change benchmark results, such as 83.1% on CyberGym (vs. 66.6% for Opus 4.6), 100% pass@1 on a Cybench subset, and end-to-end success on private cyber ranges that no prior model completed.

Implication	Description	Why It Matters (Rationale)	43e
Defensive Product Enhancement	Use Mythos-level AI for autonomous vuln scanning, exploit chaining detection, and code hardening in EDR, SIEM, and cloud security tools.	Model finds zero-days and generates PoCs far faster than humans or legacy scanners.	New AI-powered “Mythos-class” scanning modules; faster patch recommendations; competitive edge for partners with early access.
Offensive Threat Amplification	Future public/similar models enable low-skill actors to launch advanced, autonomous attacks (e.g., custom zero-days overnight).	Drops the expertise and time required for exploits dramatically.	Must build stronger behavioral AI detection, sandboxing, and exploit-prevention layers; shorter detection windows expected.
Partnership & Access Advantage	Launch partners (CrowdStrike, Palo Alto, Cisco, etc.) get exclusive early access and collaboration.	Direct integration into security platforms and threat-intel sharing.	Accelerated R&D; co-developed defensive tools; potential revenue from AI-augmented services. Non-partners may lag.
Open-Source & Supply-Chain Security	Providers can scan and help patch foundational software (Linux kernel, browsers, FFmpeg, etc.) via Glasswing.	Thousands of previously unknown critical flaws in core dependencies.	Contribute to/fund open-source programs; integrate supply-chain risk scoring; position as “AI defenders of the internet.”
Market & Regulatory Pressure	Increased demand for AI-native security solutions; possible new compliance rules around AI-assisted vuln disclosure.	Governments and enterprises will require defenses against AI-powered threats.	Invest in AI talent/infrastructure; lobby for standards; prepare for audits on AI usage in security products.
Cost & Resource Implications	High token pricing + need for massive compute for agentic scanning.	Frontier models are expensive to run at scale.	Budget for API credits; optimize agentic workflows; explore on-prem or hybrid deployment once safeguards improve.
Ethical/Responsibility Shift	Providers become active participants in preemptive global hardening rather than just reactive responders.	Anthropic’s explicit goal: “put these capabilities to work for defensive purposes” before they proliferate.	Public reporting on patched vulns (90-day Glasswing updates); transparency on AI usage; align with responsible AI scaling.
Long-Term Industry Equilibrium	AI will eventually make software more secure overall (model-generated hardened code, automated patching).	Transitional risk is high, but net positive expected.	Pivot product roadmaps toward AI-augmented prevention and autonomous response; prepare for reduced reliance on signature-based detection.

It is available only in a tightly gated private preview via Project Glasswing, a defensive cybersecurity consortium. 

Launch partners include Amazon Web Services, Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorganChase, the Linux Foundation, Microsoft, NVIDIA, Palo Alto Networks, and more than 40 additional organizations. 

For security software providers (antivirus/EDR vendors, firewall/endpoint firms, cloud security platforms, etc.), Claude Mythos Preview raises both the defensive opportunity and the offensive threat level.

Why it matters:

• Models can now autonomously find and exploit subtle, long-hidden vulnerabilities (some 16–27 years old) that survived millions of automated tests and human expert review 

• Defenders benefit by using Mythos Preview to scan their own products, customer environments, and critical open-source dependencies at superhuman speed and scale.

• Long-term equilibrium shifts are possible: (harder code, automated patching, faster incident response), but also increased attack volume and sophistication.

At least for the moment, investors seem unclear whether opportunity or risk is greater for incumbent suppliers of security products.