The conventional marketing funnel is shattered. As 2026 continues to unfold with various great tech developments, it will no longer be merely human editors or Google PageRank that determine technical reputation; it will be Large Language Models (LLMs) and AI-powered synthesis engines.
We are moving into a new period of AEO (AI Engine Optimization), where the signal-to-noise ratio has broken down. When a developer or investor asks an AI for the best cybersecurity solutions and your brand is not included in the training set or retrieval-augmented generation (RAG) pipeline, you literally do not exist.
To see how founders can navigate this transition and avoid falling into the hype-marketing trap, I interviewed Motti Peer, the Chairman and Co-CEO of ReBlonde. Peer has over 2 decades of experience in the most unpredictable areas of technology, from the early crypto Wild West to the modern-day AI arms race.
A change is underway, with SEO being replaced by AEO. What will companies need to do to ensure they are actually referenced as sources by LLMs, rather than being used as anonymous training data?
Peer: Companies can no longer rely on publishing content alone. AI systems do not look for “good articles” but for clearly identifiable and trustworthy sources. AI systems are currently integrating the idea of pre-trained datasets and live retrieval through Retrieval-Augmented Generation (RAG). To be successful, you should address the problems of the vector space; that is, organize your data and format it with JSON-LD schema such that your brand comes out as a separate “Entity” and not just a piece of text.
To be referenced, a company must have a stable, recognizable identity across the web, with its expertise proven through real technical work. This includes visible engineers, public documentation, code, research, and mentions by credible third parties. Additionally, make your documentation chunk-friendly for AI encoders, reducing the semantic gap between the arbitrary query of a user and the answer provided by your system. If an AI system cannot easily understand who you are, what you do, and why you are authoritative, it will simply ignore you.
With deepfakes and AI-driven content flooding the web, how can a tech brand best prove that it is “human-verified” and genuine?
Peer: A tech brand proves it is real by being verifiable, not by claiming authenticity. That means real people with names and track records, public engineering work, clear product documentation, and claims that can be independently checked. Strong editorial coverage and consistent third-party validation play a key role here, because credibility today is built through trusted media and independent voices, not self-published claims.
Furthermore, we are heading in the direction of “Proof-of-work” reputations, supported by blockchain-based certificates and checkable digital signatures. As a technical brand, it entails having a traceable history of GitHub commits and a third-party certification like SOC 2.
For instance, having the perspective of your CTO championed by a multi-year, tamper-proof record of public contributions, you will create a competitive moat that a generative AI prompt cannot replicate.
If organic search traffic continues to decline, which technical channel should founders focus on now to reach their target audience?
Peer: Founders should focus on channels where the product is actually used, not just discovered. APIs, developer tools, open-source projects, and integrations put the technology directly into workflows.
Statistics indicate that real searching is now done on platforms such as GitHub and VS Code. When your tool becomes a frequently used tool in a stack, say, with an SDK or API, you bypass the discovery phase.
There is a significantly more powerful feedback loop and higher retention associated with being a plugin in a user’s environment than with a million impressions on a declining Google results page.
At the same time, clear technical storytelling in the right publications ensures these products are understood and adopted by the audiences that matter.
What is the difference between a community based on hype and a technological community that provides long-term feedback?
Peer: A hype-based community reacts to announcements and promises. A real technical community reacts to performance, reliability, and results. Media-driven hype fades quickly, but sustained engagement comes from real users whose feedback is reflected back into the product and communicated clearly to the market.
What is the biggest mistake founders make when trying to explain a “Deep Tech” product to non-technical investors?
Peer: They focus on how the technology works instead of why it matters. The real challenge is translating complexity into a narrative that investors can grasp without losing technical integrity.
Effective pitches begin with a solvable problem that investors can benchmark, and then give them the technical approach. Founders who succeed usually have help shaping that message so it resonates beyond engineers.
What are the first 15 minutes of an emergency response for a startup facing an unexpected reputational attack?
Peer: The first priority is control, not speed. Verify what is actually happening, identify where the attack is coming from, and align internally before speaking publicly. Once the issue is clearly identified and isolated, the company should take responsibility, apologize when appropriate, and clearly explain what steps were taken to address the problem and what safeguards are being implemented to prevent it from happening again. An experienced communications team that understands media cycles can prevent a technical issue from turning into a lasting reputational problem.
The “Hacker” demographic is known for being skeptical about marketing. How do you build a narrative that survives the scrutiny of a technical audience without being dismissed as ‘fluff’?
Peer: You earn credibility by showing reality, not by selling vision. That includes honest discussions of trade-offs and limitations, backed by data. When this substance is communicated through respected technical and mainstream media, it signals confidence rather than hype and earns long-term trust.
Also, for the Hacker demographic, a random glossy landing page is less compelling than a well-documented “Known Issues” page or an Architectural Decision Record (ADR), since it demonstrates that the product has gone through real-world testing.
