Weibold Academy article series discusses periodically the practical developments and scientific research findings in the end-of-life tire (ELT) recycling and pyrolysis industry.

Claus Lamer
Claus Lamer

These articles are reviews by Claus Lamer – the senior pyrolysis consultant at Weibold. The reviews aim to give industry entrepreneurs, project initiators, investors, and the public a better insight into a rapidly growing circular economy. At the same time, this article series should stimulate discussion.

For completeness, we would like to emphasize that these articles are no legal advice from Weibold or the author. Please refer to the responsible authorities and specialist lawyers for legally binding statements.

Abstract

Artificial intelligence has become a valuable tool for research, drafting, and early-stage preparation. In tire recycling, pyrolysis, rCB, TPO, and circular-economy markets, AI can help companies move faster and ask better questions. But speed is not the same as certainty. Complex industrial projects still depend on verified data, technical judgment, market access, certification readiness, and commercial execution. Therefore, AI should be used as a productivity tool, not as a substitute for specialist advisory. Weibold has no reason to fear AI. On the contrary: better-informed clients often make better advisory clients. The real question is not whether companies should use AI. They should. The question is whether they should rely on AI alone when project success, investment risk, offtake, certification, and market credibility are at stake.

Introduction: AI is welcome — but it is not enough

AI is changing how companies prepare decisions. That is positive.

It can summarize, structure, prepare, draft, and help teams understand complex topics faster. In tire recycling and pyrolysis, this can be useful — especially at the early stages of a project.

But AI does not build plants. It does not test samples. It does not negotiate offtake. It does not defend a certification claim. It does not have access to the myriad thought processes in developers’ heads that lead to technical and commercial decisions and are not recorded anywhere. And it does not sit in front of investors when assumptions are challenged.

AI is a powerful tool. It is not a substitute for accountable expertise.

The risk is not AI. The risk is false confidence

The problem starts when AI-generated answers are treated as validated conclusions. In complex and fast-changing sectors, a fluent answer can sound convincing even when key assumptions are missing.

This matters because tire recycling and pyrolysis are not generic sustainability topics. They are technical, regulatory, and commercial businesses. A good answer depends on feedstock, process design, product quality, emissions control, local regulation, certification route, buyer expectations, and market timing.

AI can describe these issues if it is aware of them. And if so, it cannot reliably verify them for a specific project without real data, sector experience, and professional judgment.

Information is easy. Interpretation is hard.

Most companies do not fail because they lack information. They fail because they misread it, fail to act on it, are unaware of critical issues, or fail to ask the right questions.

A technology brochure may show attractive yields. A regulation may look supportive. A buyer segment may appear promising. A financial model may look bankable. AI can help summarize all of this.

But the real questions are harder (i.e.):

  • Are the yield assumptions realistic under continuous operation?
  • Can the recovered carbon black (rCB) meet buyer requirements?
  • Is the TPO route commercially viable or only theoretically possible?
  • Will claims survive audit scrutiny?
  • Are permitting, CAPEX, OPEX, uptime, and offtake assumptions aligned with reality?

This is where Weibold adds value: Intrinsic expertise built over a decade through ongoing discussions and shared problem-solving with key commercial and technical executives around the globe can help turn information into decisions that withstand technical, commercial, regulatory, and investor scrutiny.

AI cannot test the product

In tire recycling and pyrolysis, product quality is not a matter of wording. It is measurable. Recovered carbon black must be characterized. Pyrolysis oil must be analyzed. Rubber powder, granulate, molded goods, and downstream products must be assessed against real market requirements.

AI can suggest which parameters may matter. It cannot run ash, volatiles, BET, TGA, GC-MS, sample-conditioning protocols, or application testing. It cannot diagnose batch inconsistency, process instability, contamination, or specification gaps.

For customers, investors, and offtakers, this distinction is critical. A product claim is only valuable when it can be proven.

AI cannot create market access

AI can list potential applications for rCB, TPO, rubber powder, molded products, or recycled materials. But a list of applications is not a sales strategy.

Market access requires understanding buyer requirements, pricing logic, technical approval processes, quality thresholds, competing materials, certification expectations, decision-makers’ personal dispositions, and negotiation dynamics.

In many cases, the most important information is not public. It comes from conversations with buyers, industrial users, technology providers, investors, regulators, and project developers.

Weibold’s advantage is not only knowledge. It is sector access, accumulated project experience, and the ability to connect product reality with market requirements, which no AI currently possesses.

It is not about vocabulary. It is about evidence

AI can explain terms. But (e.g.) certification is not won by knowing the terminology.

It is won by documentation, traceability, process control, audit readiness, and defensible claims. For pyrolysis and tire-derived products, weak certification logic can create commercial, legal, and reputational risk.

The question is therefore not simply: “Can this material qualify?”

The better question is: “Can we prove it, document it, audit it, sell it, and defend the claim under market pressure?”

That is advisory work.

Generic assumptions can destroy bankability

AI can help build a first model. But a model is only as good as its assumptions. In recycling and pyrolysis projects, small errors in uptime, yield, product pricing, energy balance, maintenance, emissions control, feedstock cost, certification premium, or offtake discount can change the entire investment case.

A bankable project does not need a better story. It needs better assumptions.

Weibold supports clients with market studies, due diligence, product strategy, technology assessment, financial planning, permitting logic, offtake positioning, and execution support. The purpose is not to make a project look good. The purpose is to understand whether it can work — and what must change if it does not.

The right model: AI plus expert advisory

The strongest position is not anti-AI. It is disciplined AI use.

Companies should use AI to prepare, learn, and eventually draft, compare, and structure. That makes discussions more efficient. It can also help clients become more informed and more demanding — which is positive for serious advisors.

But AI should not be used as the final authority for technology selection, investment decisions, certification and other claims, market entry, or offtake strategy.

The right model is simple:

  • Use AI to accelerate preparation.
  • Use expert advisory to validate, challenge, structure, and execute.

Conclusion: AI may give some answers. Weibold helps make decisions

AI is useful. Weibold is not afraid of it, and serious clients should not avoid it.

But companies should be clear about its limits. AI can produce answers quickly. It cannot take responsibility for project success. It cannot validate a plant, a product, a claim, a buyer, a permit, or an investment case.

Success depends on evidence, judgment, execution, and market credibility.

That is where Weibold’s role begins - and where it has already been for 27 years: helping companies turn opportunities into technically sound, commercially realistic, and market-ready projects.

The question is not whether to use AI. The question is whether to trust AI alone with decisions that determine project success.

At Weibold, we will commit to you this: If we cannot personally formulate situations, issues, and recommendations and stand behind them, we will not submit them to you.

Use Artificial Intelligence — but do not outsource judgment to it.

Copyright: ©2026 by Robert Weibold GmbH. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license CC BY 4.0. You must give appropriate credit, provide a link to the license and this article, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.