“Something has gone completely wrong. The basic view among enterprises in this country is: ‘I am going to chillax and waste my time with tokens. I am going to get no value, and they are going to get my IP.'” – Alex Karp – Palantir CEO

Enterprise AI adoption is colliding with a harsh reality: many large organisations feel they are paying heavily for experimental systems while surrendering control over their most valuable asset, their intellectual property, to external model providers.¹ In the US corporate environment Alex Karp describes on CNBC, the prevailing mood among sophisticated buyers is not excitement about cutting-edge models but frustration, distrust, and a growing sense that the economic bargain being offered by frontier AI labs is structurally misaligned with enterprise interests.^1,7 That dislocation between value delivered and IP risk is the underlying tension driving both the quote and the strategic repositioning now under way across the AI ecosystem.

From Model Hype To Enterprise Disillusion

The immediate factual context is Karp’s appearance on CNBC’s “Squawk Box” in July 2026, where he argues that leading AI labs have “completely” mis-sold AI to enterprises by focusing on token-based access to frontier models rather than on controlled, outcome-focused deployments.^1,16 In his account, many US enterprises have trialled generative AI services priced on usage tokens, only to discover that pilots rarely progress into production systems that materially improve manufacturing, logistics, pharmacological research, or other complex operations.^1,4 Instead, budgets are consumed by experimentation while the providers accumulate fine-tuning data, usage patterns, and business process know-how which can be re-embedded into their own models, effectively harvesting the clients’ alpha-the distinctive decision logic and competitive edge embedded in their data and workflows.^4,19 This perceived asymmetry-“no value” for the buyer, strategic data for the seller-is what Karp frames as something having gone “completely wrong”.¹

Token-based commercial models were originally marketed as democratising access: pay per token, experiment rapidly, avoid the capital expenditure of owning GPU clusters or building internal model pipelines.^7,2 Yet for complex enterprises operating in regulated environments or on the battlefield, capability without sovereignty quickly becomes a liability. They may obtain powerful generative capabilities but lack negotiated guarantees about where prompts are cached, how outputs are logged, and whether fine-tuning pipelines will internalise their proprietary domain knowledge.^1,20 As concerns about model training data, reinforcement learning from human feedback, and long-term retention policies have intensified, token pricing has ceased to look like operational flexibility and instead resembles an opaque rent charged on access to infrastructure that may ultimately compete with the client.

IP Sovereignty And The Fear Of “Alpha Theft”

Central to the statement is a specific fear: that external labs will not only see sensitive data, but also infer and appropriate the core decision-making patterns that constitute an enterprise’s alpha.^1,4 In financial language, alpha denotes excess return above a benchmark; in Karp’s usage, it extends to any proprietary operational edge-manufacturing recipes, logistics heuristics, risk scoring rules, or targeting doctrines-that can be implicitly reconstructed from usage data.¹ The worry is not merely that sensitive documents or customer records might leak, but that the lab, by observing queries and feedback at scale, can build a generalised representation of how the client makes high-value decisions, then reuse or productise that representation for other customers or its own ventures.^4,22 In this view, enterprise AI based on external closed models risks functioning as a one-way knowledge transfer: the client pays for tokens and experimentation, the provider quietly accumulates a distilled map of the client’s decision landscape.

These concerns intersect with a broader security discourse on AI systems. Research on emerging AI security risks notes that large-scale models and agentic systems introduce new attack surfaces, including prompt injection, model theft, and indirect prompt attacks that can exfiltrate or reconstruct sensitive patterns from interaction histories.^11,20 When enterprises are unsure who controls the weights, where model states are stored, or how fine-tuning data is segregated, the threat is not confined to adversarial actors; it includes the legitimate provider using aggregated insights to strengthen its own competitive position. Recorded analyses of AI security risk emphasise the need for zero-trust principles and strict governance for agent identities and model access paths.^11,20 Karp’s intervention translates that abstract security language into a blunt commercial accusation: some labs are effectively imposing a “wealth tax”-charging high fees while appropriating data-driven alpha that rightly belongs to the enterprise.⁴

The Strategic Role Of Ontology And The Application Layer

Technically, Karp positions his organisation’s ontology and application layer as the antidote to this perceived mis-selling.^1,18 An ontology in modern enterprise AI is a structured, machine-readable representation of the entities, relationships, and actions that define a business domain.^3,15 It encodes not merely metrics but the semantic and operational logic of an organisation: what a “shipment” is, how it relates to “warehouse”, “route”, and “risk score”, and what should happen when a shipment is delayed or a risk threshold is exceeded.^3,21 Sources on ontology describe it as the central nervous system of an enterprise AI stack, integrating data, business logic, and stateful decision processes into a unified model that both humans and AI agents can query and update.^3,18 Palantir’s documentation explicitly frames its Ontology system as modelling decisions through the integrated representation of data, logic, action, and security.¹⁸

By situating the large language model behind such an ontology-driven application layer, Karp argues that enterprises can make frontier or open models “safe, useful and precise” without exposing underlying data or decision logic to uncontrolled caching or replication.^1,18 The ontology constrains interactions: the model does not directly traverse raw databases or ungoverned knowledge graphs, but operates within a guardrailed semantic context where each read and write is mediated by the ontology’s policies and security rules.^3,18 This architecture allows enterprises to swap models-closed or open-while retaining ownership of the domain representation and controlling which signals are fed back into training pipelines.^6,21 In effect, sovereignty is pushed up a layer: instead of negotiating at the level of tokens and prompts, enterprises assert control through a persistent context model that defines the vocabulary, relationships, and permissible actions of their AI systems.

Independent analysis of ontologies and semantic layers reinforces this framing. Multiple sources distinguish semantic layers (which standardise metrics and calculations) from ontologies (which model domain entities and relationships to support reasoning and multi-agent workflows).^15,6 In more advanced architectures, an enterprise context layer extends the ontology with policy and judgement, enabling agentic AI to act with appropriate authority while remaining grounded in governed context.^6,21 This layered approach speaks directly to Karp’s critique: token-based access without such context mechanisms leaves enterprises reliant on system prompts and ad hoc safeguards, whereas moderated access through an ontology enables granular control over what the model sees, what it can infer, and how its outputs propagate through operational systems.^3,18

Tokens, Pricing, And The True Cost Of Enterprise AI

Behind the rhetoric lies a concrete economic dispute about how AI is priced and measured. Token-based billing makes sense for consumer chatbots or simple developer experiments, but it maps poorly onto the complex cost structure of enterprise AI deployments.^2,7 Detailed breakdowns of total cost of ownership for enterprise AI highlight multiple categories beyond mere inference: infrastructure, data engineering, specialised talent, model maintenance, compliance, and integration can each account for substantial portions of spend.² For example, GPU clusters and multi-cloud infrastructure can run from 200 000 to well over 2 000 000 annually; data engineering and pipeline maintenance often consume 25 to 40% of the total budget; and version control, monitoring, and retraining add further overhead.² Against that backdrop, token fees imposed by labs are only one component of cost, yet they are often the most visible and least justified from a business outcome perspective.

Karp’s point about “bad financials and growth while losing money” is that many frontier-lab-centric enterprises are stuck in a pattern where token usage rises, experimental deployments proliferate, but revenue-impacting applications fail to reach scale because clients will not pay the “true cost” for hardened systems integrated into core operations.^1,23 Independent CIO analysis supports this diagnosis: most enterprise AI programmes struggle not because models are inadequate but because operating models, data governance, and decision workflows are not restructured to exploit intelligence effectively.²³ Pilots remain isolated, metrics are vague, and AI doesn’t become a “core business capability” driving measurable outcomes.²³ In that environment, token spend looks like speculative experimentation rather than capital investment; when combined with fears about IP appropriation, the result is the disillusionment Karp reports-organisations feel they are “chillaxing” with models that burn compute and budget while shifting long-term advantage towards the providers.

Trust, Governance, And Who Owns The Risk

Layered through the backstory is a governance argument: enterprises are demanding clear answers to basic ownership questions that many labs have deferred or obscured.^1,17 Who owns the data once it flows through prompts and logs? Where is it cached, and under what retention and deletion policies? Are fine-tuned weights shared, siloed, or reused across customers? Who is accountable if model behaviour evolves into risky territory due to accumulated training signals? Governance frameworks for AI risk increasingly emphasise cross-functional responsibility-security, legal, ethics, and operations convened in AI risk committees or similar bodies-to manage these questions systematically.^5,17 Yet Karp suggests that labs have tried to bypass this institutional maturity by appealing to trust and speed, effectively asking enterprises to accept “I have never lied” narratives rather than auditable guarantees.^1,11

Studies on AI accountability in enterprises show that ownership is often fragmented: surveys find that a significant share of organisations cannot identify a single C-suite executive accountable for AI-related risks.^8,17 Regulatory shifts, such as US federal guidance on Chief AI Officers, are slowly pushing towards clearer responsibility structures, but many enterprises are still negotiating basic guardrails.^8,17 In that milieu, labs that offer generic indemnities or vague privacy commitments are increasingly out of step with the expectations of regulated industries and national security customers. Recorded Future and other security-focused analyses argue for continuous AI governance, validation, and monitoring, moving beyond traditional detection-first security towards active control over model access, prompt engineering, and behavioural auditing.^11,20 Karp’s narrative fits within that emerging consensus: he channels the frustration of enterprises that feel their governance questions are brushed aside in favour of token counters and marketing demos.

Open Models, Compute Control, And The Battle Over Means Of Production

A key strategic pivot Karp advocates is towards open-weight models, owned compute, and internal control over the “means of production” in AI.^1,10 Rather than relying on external frontier labs to provide both models and infrastructure, he argues that sophisticated enterprises-and especially defence customers-should own their GPUs, run open-source or internal models, and maintain direct control over weights.^1,10 Analysts observing his CNBC interview note that he positions this as an AI sovereignty agenda: enterprises should not depend on consensus views in Silicon Valley to run battlefields or critical infrastructure.^4,22 This resonates with broader trends: open models from US, European, and Chinese providers, as well as model ecosystems associated with major hardware vendors, are increasingly attractive because they allow weight-level control and deployment into sovereign environments.^7,16

The partnership he describes with NVIDIA is emblematic of this shift.^1,16 Rather than simply consuming NVIDIA’s hardware indirectly through cloud platforms, the arrangement is framed as a way to build custom AI systems where enterprises own their compute, models, data stack, and alpha.¹ Industry commentary highlights that technical customers now ask first-order questions about whether they can switch models, retain weights, and localise training within their own security perimeter.^6,18 Open-weight models, combined with ontology-driven application layers, offer a pathway: enterprises can assemble model-plus-context stacks where the underlying infrastructure is under their control, and external labs become optional rather than central. Karp’s blunt criticism of “deploy-co” structures-entities that merely deploy tokens while transferring alpha to third parties-captures his belief that the old model of centralised AI provision is being superseded by a more federated, sovereign architecture.^1,22

The Political And Geopolitical Dimension

Although the quote focuses on enterprise sentiment, it sits within a broader political and geopolitical frame. Karp warns that overselling AI to enterprises while refusing secure, controllable deployments for departments of defence or war is “effing insane” from a national security standpoint.^1,4 He juxtaposes the willingness of labs to release powerful models to global adversaries with their reluctance to provide weight control and data sovereignty to allied governments, calling into question the alignment between commercial AI strategies and Western security priorities.^1,4 Geopolitical analysis identifies America, China, and Israel as the key tech centres in this domain, with China acting as a peer adversary and building its own AI ecosystems without the same internal political and cultural frictions.^1,16 In that context, he argues that Western debates about restricting AI for domestic governments while leaking capability to adversaries are strategically incoherent.

This geopolitical lens reinforces the enterprise IP concerns. If frontier labs spread powerful models widely, train them on cross-enterprise data, and retain unilateral control over their evolution, they accumulate a transnational reservoir of operational knowledge that may be difficult to govern or align with any particular state’s interests. Policy analysts increasingly call for AI sovereignty: nations and major enterprises should maintain clear lines of control over critical models, training data, and deployment pipelines.^21,11 Ontology-based architectures and sovereign compute fit this agenda, providing mechanisms to confine sensitive decision logic and operational knowledge within national or organisational boundaries while still exploiting shared model innovation where appropriate.^18,3 Karp’s argument surfaces the uncomfortable possibility that without such measures, enterprises are unwittingly contributing to a shared AI commons dominated by private labs whose strategic aims diverge from their own.

Debates, Objections, And Why It Matters

There are, of course, objections to Karp’s framing. Supporters of frontier labs argue that token-based models enable rapid innovation, allow smaller firms to access capabilities they could never build themselves, and that strict privacy and data segregation policies prevent the kind of alpha appropriation he describes.^7,19 They point out that most labs publish privacy guarantees, offer enterprise-grade instances, and in some cases commit not to train core models on customer data without explicit consent. Furthermore, independent commentators note that Karp is simultaneously warning about mis-selling and promoting his own stack as the solution, raising the question of how much of his critique is principled and how much is commercial positioning.^22,4

Yet even critics acknowledge that his intervention crystallises real anxieties in the market. CIO surveys repeatedly show that enterprise AI programmes struggle to progress beyond pilots, and that business stakeholders demand clearer ownership, risk management, and measurable outcomes.^23,8 Security analysts document new attack vectors and emphasise the need for specialised AI governance rather than generic cybersecurity controls.^11,20 Ontology and context-layer specialists underline that without a structured domain model, large language models will remain brittle, hallucination-prone, and difficult to integrate safely into mission-critical systems.^3,21 In that sense, Karp’s colourful language functions as a signal: the easy phase of model hype and token experimentation is ending, replaced by a more demanding era where enterprises insist on value, sovereignty, and trustable architecture.

Why it matters is straightforward. If large organisations continue to see AI as a high-cost, low-trust experiment, adoption will stall, and the transformative potential of integrated AI decision systems will be realised only in pockets-by those who build sovereign stacks with strong ontologies and controlled models.^23,18 The competitive gap between enterprises that own their alpha and those that bleed it into shared clouds will widen. National security doctrines will be shaped by whether governments can deploy agentic AI that respects classified boundaries while reasoning effectively. And the structure of the AI industry itself will be determined largely by how this dispute over tokens, IP, and ownership is resolved: either frontier labs maintain a centralised, rent-extracting role, or the ecosystem rebalances towards open-weight, ontology-driven, sovereign architectures where labs are important but not dominant. Karp’s remark is a snapshot of that inflection point, capturing a moment when the market is re-evaluating what it is willing to pay for-and what it is no longer willing to give away.

 

References

1. “Palantir CEO Alex Karp says ‘something has gone completely wrong’ with how AI is sold” – https://www.cnbc.com/video/2026/07/01/palantir-ceo-alex-karp-says-something-has-gone-completely-wrong-with-how-ai-is-sold.html

2. Palantir CEO Alex Karp Says AI Labs Are Chasing ‘Tokens’ – Benzinga – 2026-07-02 – https://www.benzinga.com/markets/tech/26/07/60238118/palantir-ceo-alex-karp-says-ai-labs-are-chasing-tokens-while-enterprises-fear-for-their-ip-something-has-gone-completely-wrong

3. Total cost of ownership for enterprise AI: Hidden costs | ROI factors – 2025-11-11 – https://xenoss.io/blog/total-cost-of-ownership-for-enterprise-ai

4. Operationalizing AI Ontologies – Hiflylabs – 2025-06-10 – https://hiflylabs.com/blog/2025/6/11/ai-ontologies-in-practice

5. Palantir Billionaire Karp Blasts AI Industry As ‘Effing Insane’ – Forbes – 2026-07-01 – https://www.forbes.com/sites/tylerroush/2026/07/01/palantir-billionaire-alex-karp-calls-ai-industry-effing-insane-in-heated-interview/

6. Finding the Right Owner for AI Risk – Optro – 2024-11-28 – https://optro.ai/blog/finding-the-right-owner-for-ai-risk

7. Semantic Layer vs Ontology: Key Differences + Enterprise Context … – 2026-05-05 – https://www.alation.com/blog/semantic-layer-vs-ontology-vs-enterprise-context-layer/

8. Palantir’s Karp bashes token-based AI model as ‘completely wrong’ – 2026-07-01 – https://www.cnbc.com/2026/07/01/palantir-karp-open-ai-anthropic-tokens.html

9. Who’s Accountable in the Enterprise for AI and its Risks? – ModelOp – 2024-04-25 – https://www.modelop.com/blog/whos-accountable-in-the-enterprise-for-ai-and-its-risks

10. Pragmatic ontology for AI agents – T-Systems – 2026-05-04 – https://www.t-systems.com/de/en/insights/newsroom/expert-blogs/pragmatic-ontology-for-ai-agents-1163094

11. Palantir Technologies CEO Alex Karp went on national television … – 2026-07-03 – https://www.linkedin.com/posts/sivasurend_palantir-technologies-ceo-alex-karp-went-activity-7478744309131845632–Y46

12. Emerging Enterprise Security Risks of AI – Recorded Future – 2026-04-21 – https://www.recordedfuture.com/research/emerging-enterprise-security-risks-of-ai

13. What Ontology, RAG and Graph data do you use to develop … – 2024-05-30 – https://community.openai.com/t/what-ontology-rag-and-graph-data-do-you-use-to-develop-intelligent-assistants/787860

14. Palantir CEO Alex Karp boldly states in an interview that claims AI … – 2026-07-02 – https://www.facebook.com/tomshardware/posts/palantir-ceo-alex-karp-boldly-states-in-an-interview-that-claims-ai-companies-ar/1431900338974380/

15. Enterprise AI Adoption Risks: Data, Access, and Model Misuse – 2026-04-24 – https://www.linkedin.com/posts/premkumaras_followus-subscribe-joinus-activity-7453629949468495872-O78-

16. Ontology vs. Semantic Layer: What’s Missing – DataHub – 2026-04-30 – https://datahub.com/blog/ontology-vs-semantic-layer/

17. Palantir CEO Alex Karp says ‘something has gone completely wrong … – 2026-07-01 – https://www.youtube.com/watch?v=0A3sGymV6kY

18. Why AI Requires Executive Ownership, Not IT-Led Initiatives – 2025-12-03 – https://www.charterglobal.com/why-ai-requires-executive-ownership-not-it-led-initiatives/

19. The Ontology system – Palantir – 2021-12-14 – https://palantir.com/docs/foundry/architecture-center/ontology-system/

20. Palantir CEO Alex Karp claims AI companies are stealing customers … – 2026-07-02 – https://www.reddit.com/r/technology/comments/1ulxqit/palantir_ceo_alex_karp_claims_ai_companies_are/

21. The Enterprise Guide to Navigating AI Security Threats – Cranium AI – 2026-01-26 – https://cranium.ai/resources/blog/the-enterprise-guide-to-navigating-ai-security-threats/

22. Ontologies as the missing layer in enterprise AI | EY – US – 2026-04-29 – https://www.ey.com/en_us/insights/consumer-products/ontologies-as-the-missing-layer-in-enterprise-ai

23. Palantir CEO Alex Karp on the false religion of frontier labs – LinkedIn – 2026-06-10 – https://www.linkedin.com/posts/palantir-technologies_palantir-ceo-alex-karp-on-the-false-religion-activity-7470573065354248192-zHWO

24. Why most enterprise AI programs fail – and how to turn them around – 2026-06-12 – https://www.cio.com/article/4184158/why-most-enterprise-ai-programs-fail-and-how-to-turn-them-around.html

25. Aviso’s Ontology Layer: The Semantic Foundation That Governs … – 2026-01-22 – https://www.aviso.com/blog/ontology-layer