Featured Stop calling it 'The AI bubble': It's actually multiple bubbles, each with a different expiration date Val Bercovici, WEKA January 18, 2026 CleoP made with Midjourney It’s the question on everyone’s minds and lips: Are we in an AI bubble? It's the wrong question. The real question is: Which AI bubble are we in, and when will each one burst? 0:06 / 14:09 Keep Watching The debate over whether AI represents a transformative technology or an economic time bomb has reached a fever pitch. Even tech leaders like Meta CEO Mark Zuckerberg have acknowledged evidence of an unstable financial bubble forming around AI. OpenAI CEO Sam Altman and Microsoft co-founder Bill Gates see clear bubble dynamics: overexcited investors, frothy valuations and plenty of doomed projects — but they still believe AI will ultimately transform the economy. But treating "AI" as a single monolithic entity destined for a uniform collapse is fundamentally misguided. The AI ecosystem is actually three distinct layers, each with different economics, defensibility and risk profiles. Understanding these layers is critical, because they won't all pop at once. Layer 3: The wrapper companies (first to fall) The most vulnerable segment isn't building AI — it's repackaging it. These are the companies that take OpenAI's API, add a slick interface and some prompt engineering, then charge $49/month for what amounts to a glorified ChatGPT wrapper. Some have achieved rapid initial success, like Jasper.ai, which reached approximately $42 million in annual recurring revenue (ARR) in its first year by wrapping GPT models in a user-friendly interface for marketers. But the cracks are already showing. These businesses face threats from every direction: Feature absorption: Microsoft can bundle your $50/month AI writing tool into Office 365 tomorrow. Google can make your AI email assistant a free Gmail feature. Salesforce can build your AI sales tool natively into their CRM. When large platforms decide your product is a feature, not a product, your business model evaporates overnight. The commoditization trap: Wrapper companies are essentially just passing inputs and outputs, if OpenAI improves prompting, these tools lose value overnight. As foundation models become more similar in capability and pricing continues to fall, margins compress to nothing. Zero switching costs: Most wrapper companies don't own proprietary data, embedded workflows or deep integrations. A customer can switch to a competitor, or directly to ChatGPT, in minutes. There's no moat, no lock-in, no defensibility. The white-label AI market exemplifies this fragility. Companies using white-label platforms face vendor lock-in risks from proprietary systems and API limitations that can hinder integration. These businesses are building on rented land, and the landlord can change the terms, or bulldoze the property, at any moment. The exception that proves the rule: Cursor stands as a rare wrapper-layer company that has built genuine defensibility. By deeply integrating into developer workflows, creating proprietary features beyond simple API calls and establishing strong network effects through user habits and custom configurations, Cursor has demonstrated how a wrapper can evolve into something more substantial. But companies like Cursor are outliers, not the norm — most wrapper companies lack this level of workflow integration and user lock-in. Timeline: Expect significant failures in this segment by late 2025 through 2026, as large platforms absorb functionality and users realize they're paying premium prices for commoditized capabilities. Layer 2: Foundation models (the middle ground) The companies building LLMs — OpenAI, Anthropic, Mistral — occupy a more defensible but still precarious position. Economic researcher Richard Bernstein points to OpenAI as an example of the bubble dynamic, noting that the company has made around $1 trillion in AI deals, including a $500 billion data center buildout project, despite being set to generate only $13 billion in revenue. The divergence between investment and plausible earnings "certainly looks bubbly," Bernstein notes. Yet, these companies possess genuine technological moats: Model training expertise, compute access and performance advantages. The question is whether these advantages are sustainable or whether models will commoditize to the point where they're indistinguishable — turning foundation model providers into low-margin infrastructure utilities. Engineering will separate winners from losers: As foundation models converge in baseline capabilities, the competitive edge will increasingly come from inference optimization and systems engineering. Companies that can scale the memory wall through innovations like extended KV cache architectures, achieve superior token throughput and deliver faster time-to-first-token will command premium pricing and market share. The winners won’t just be those with the largest training runs, but those who can make AI inference economically viable at scale. Technical breakthroughs in memory management, caching strategies and infrastructure efficiency will determine which frontier labs survive consolidation. Another concern is the circular nature of investments. For instance, Nvidia is pumping $100 billion into OpenAI to bankroll data centers, and OpenAI is then filling those facilities with Nvidia's chips. Nvidia is essentially subsidizing one of its biggest customers, potentially artificially inflating actual AI demand. Still, these companies have massive capital backing, genuine technical capabilities and strategic partnerships with major cloud providers and enterprises. Some will consolidate, some will be acquired, but the category will survive. Timeline: Consolidation in 2026 to 2028, with 2 to 3 dominant players emerging while smaller model providers are acquired or shuttered. Layer 1: Infrastructure (built to last) Here’s the contrarian take: The infrastructure layer — including Nvidia, data centers, cloud providers, memory systems and AI-optimized storage — is the least bubbly part of the AI boom. Yes, the latest estimates suggest global AI capital expenditures and venture capital investments already exceed $600 billion in 2025, with Gartner estimating that all AI-related spending worldwide might top $1.5 trillion. That sounds like bubble territory. But infrastructure has a critical characteristic: It retains value regardless of which specific applications succeed. The fiber optic cables laid during the dot-com bubble weren’t wasted — they enabled YouTube, Netflix and cloud computing. Twenty-five years ago, the original dot-com bubble burst after debt financing built out fiber-optic cables for a future that had not yet arrived, but that future eventually did arrive, and the infrastructure was there waiting. Despite stock pressure, Nvidia’s Q3 fiscal year 2025 revenue hit about $57 billion, up 22% quarter-over-quarter and 62% year-over-year, with the data center division alone generating roughly $51.2 billion. These aren’t vanity metrics; they represent real demand from companies making genuine infrastructure investments. The chips, data centers, memory systems and storage infrastructure being built today will power whatever AI applications ultimately succeed, whether that’s today’s chatbots, tomorrow’s autonomous agents or applications we haven’t even imagined yet. Unlike commoditized storage alone, modern AI infrastructure encompasses the entire memory hierarchy — from GPU HBM to DRAM to high-performance storage systems that serve as token warehouses for inference workloads. This integrated approach to memory and storage represents a fundamental architectural innovation, not a commodity play. Timeline: Short-term overbuilding and lazy engineering are possible (2026), but long-term value retention is expected as AI workloads expand over the next decade. The cascade effect: Why this matters The current AI boom won't end with one dramatic crash. Instead, we'll see a cascade of failures beginning with the most vulnerable companies, and the warning signs are already here. Phase 1: Wrapper and white-label companies face margin compression and feature absorption. Hundreds of AI startups with thin differentiation will shut down or sell for pennies on the dollar. More than 1,300 AI startups now have valuations of over $100 million, with 498 AI "unicorns" valued at $1 billion or more, many of which won't justify those valuations. Phase 2: Foundation model consolidation as performance converges and only the best-capitalized players survive. Expect 3 to 5 major acquisitions as tech giants absorb promising model companies. Phase 3: Infrastructure spending normalizes but remains elevated. Some data centers will sit partially empty for a few years (like fiber optic cables in 2002), but they'll eventually fill as AI workloads genuinely expand. What this means for builders The most significant risk isn't being a wrapper — it’s staying one. If you own the experience the user operates in, you own the user. If you're building in the application layer, you need to move upstack immediately: From wrapper → application layer: Stop just generating outputs. Own the workflow before and after the AI interaction. From application → vertical SaaS: Build execution layers that force users to stay inside your product. Create proprietary data, deep integrations and workflow ownership that makes switching painful. The distribution moat: Your real advantage isn't the LLM, it's how you get users, keep them and expand what they do inside your platform. Winning AI businesses aren't just software companies — they're distribution companies. The bottom line It’s time to stop asking whether we're in "the" AI bubble. We're in multiple bubbles with different characteristics and timelines. The wrapper companies will pop first, probably within 18 months. Foundation models will consolidate over the next 2 to 4 years. I predict that current infrastructure investments will ultimately prove justified over the long term, although not without some short-term overbuilding pains. This isn't a reason for pessimism, it's a roadmap. Understanding which layer you're operating in and which bubble you might be caught in is the difference between becoming the next casualty and building something that survives the shakeout. The AI revolution is real. But not every company riding the wave will make it to shore. Val Bercovici is CAIO at WEKA. Welcome to the VentureBeat community! Our guest posting program is where technical experts share insights and provide neutral, non-vested deep dives on AI, data infrastructure, cybersecurity and other cutting-edge technologies shaping the future of enterprise. Read more from our guest post program — and check out our guidelines if you’re interested in contributing an article of your own! Subscribe to get latest news! Deep insights for enterprise AI, data, and security leaders VB Daily AI Weekly AGI Weekly Security Weekly Data Infrastructure Weekly VB Events All of them By submitting your email, you agree to our Terms and Privacy Notice. Get updates You're in! Our latest news will be hitting your inbox soon. CleoP made with Midjourney Why reinforcement learning plateaus without representation depth (and other key takeaways from NeurIPS 2025) Every year, NeurIPS produces hundreds of impressive papers, and a handful that subtly reset how practitioners think about scaling, evaluation and system design. In 2025, the most consequential works weren't about a single breakthrough model. Instead, they challenged fundamental assumptions that academicians and corporations have quietly relied on: Bigger models mean better reasoning, RL creates new capabilities, attention is “solved” and generative models inevitably memorize. Maitreyi Chatterjee,Devansh Agarwal January 17, 2026 Image credit: VentureBeat with ChatGPT How Google’s 'internal RL' could unlock long-horizon AI agents Researchers at Google have developed a technique that makes it easier for AI models to learn complex reasoning tasks that usually cause LLMs to hallucinate or fall apart. Instead of training LLMs through next-token prediction, their technique, called internal reinforcement learning (internal RL), steers the model’s internal activations toward developing a high-level step-by-step solution for the input problem. Ben Dickson January 16, 2026 Shimon Ben-David, CTO, WEKA and Matt Marshall, Founder & CEO, VentureBeat Breaking through AI’s memory wall with token warehousing As agentic AI moves from experiments to real production workloads, a quiet but serious infrastructure problem is coming into focus: memory. Not compute. Not models. Memory. VB Staff January 15, 2026 CleoP made with Midjourney Why your LLM bill is exploding — and how semantic caching can cut it by 73% Our LLM API bill was growing 30% month-over-month. Traffic was increasing, but not that fast. When I analyzed our query logs, I found the real problem: Users ask the same questions in different ways. Sreenivasa Reddy Hulebeedu Reddy January 12, 2026 Partner Content How DoorDash scaled without a costly ERP overhaul Presented by NetSuite VB Staff January 12, 2026 Credit:Image generated by VentureBeat with FLUX-2-Pro Nvidia’s Vera Rubin is months away — Blackwell is getting faster right now Nvidia has been able to increase Blackwell GPU performance by up to 2.8x per GPU in a period of just three short months. Sean Michael Kerner January 9, 2026 CleoP made with Midjourney Why AI feels generic: Replit CEO on slop, toys, and the missing ingredient of taste Right now in the AI world, there are a lot of percolating ideas and experimentation. But as far as Replit CEO Amjad Masad is concerned, they're just "toys": unreliable, marginally effective, and generic. Taryn Plumb January 8, 2026 Image credit: VentureBeat with ChatGPT New ‘Test-Time Training’ method lets AI keep learning without exploding inference costs A new study from researchers at Stanford University and Nvidia proposes a way for AI models to keep learning after deployment — without increasing inference costs. For enterprise agents that have to digest long docs, tickets, and logs, this is a bid to get “long memory” without paying attention costs that grow with context length. Ben Dickson January 6, 2026 CleoP made with Midjourney 'Intelition' changes everything: AI is no longer a tool you invoke AI is evolving faster than our vocabulary for describing it. We may need a few new words. We have “cognition” for how a single mind thinks, but we don't have a word for what happens when human and machine intelligence work together to perceive, decide, create and act. Let’s call that process intelition. Brian Mulconrey, Sureify Labs January 4, 2026 CleoJ made with Midjourney. Why “which API do I call?” is the wrong question in the LLM era For decades, we have adapted to software. We learned shell commands, memorized HTTP method names and wired together SDKs. Each interface assumed we would speak its language. In the 1980s, we typed 'grep', 'ssh' and 'ls' into a shell; by the mid-2000s, we were invoking REST endpoints like GET /users; by the 2010s, we imported SDKs (client.orders.list()) so we didn’t have to think about HTTP. But underlying each of those steps was the same premise: Expose capabilities in a structured form so others can invoke them. Dhyey Mavani January 3, 2026 Nvidia just admitted the general-purpose GPU era is ending Nvidia’s $20 billion strategic licensing deal with Groq represents one of the first clear moves in a four-front fight over the future AI stack. 2026 is when that fight becomes obvious to enterprise builders. Matt Marshall January 3, 2026 CleoP made with Midjourney Why Notion’s biggest AI breakthrough came from simplifying everything When initially experimenting with LLMs and agentic AI, software engineers at Notion AI applied advanced code generation, complex schemas, and heavy instructioning. Taryn Plumb January 2, 2026
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