Author here. I co-founded GrackerAI, which monitors AI citations for B2B companies, so I have a bias worth disclosing upfront.
The core finding that surprised me: we looked at cross-platform citation data and the overlap between engines is tiny.
- Only 11% of domains get cited by both ChatGPT and Perplexity.
- Less than 1% for specific queries.
- Perplexity leans heavily on Reddit (47% of top citations).
- ChatGPT favors direct authoritative sources with strong recency signals.
- Google AI Overviews has 76% overlap with Google's top 10.
The practical implication for anyone building a B2B product: the AI engine your team uses for research is probably not the same one your enterprise buyer uses during procurement.
Wharton-GBK 2025 data shows ChatGPT at 67% enterprise adoption and Copilot at 58%, while Perplexity sits at roughly 18%.
The conversion data is also worth noting. Across 42 B2B sites studied, ChatGPT referral traffic converted at 15.9% vs 2.8% for traditional organic Google traffic. AI compresses the research phase, so visitors arrive further down the funnel.
Are you seeing similar patterns? And for those building developer tools or B2B products, which AI engines are you actually seeing referral traffic from?
I ran into this when building a kids' education app a few years ago. We explored a bunch of options, from asking for the last four digits of their parents' SSN (which felt icky, even though it's just a partial number) to knowledge-based authentication (like security questions, but for parents).
Ultimately, we went with a COPPA-compliant verification service, but it added friction to the signup process.
It's a trade-off between security and user experience, and there's no perfect solution, unfortunately.
Interesting project. IIRC, one of the biggest challenges with the TI-99/4A was its TMS9900 processor. It was a 16-bit CPU, but had a really awkward memory architecture that made it difficult to write efficient code.
The lack of dedicated registers meant a lot of memory access, which slowed things down considerably. This is probably why it never gained the same traction as the 6502-based systems like the Apple II or Atari.
I'm curious to see how this UNIX-like OS addresses those limitations. It's a pretty neat accomplishment if it can provide a usable environment on that hardware.
We are building the infrastructure for the post-SEO era. As B2B buyers shift from traditional search to AI assistants like Perplexity, ChatGPT, and Gemini, the old playbooks for visibility are breaking. GrackerAI provides the analytics and automation layer for Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO).
We help brands track their real-time AI visibility scores and automate the creation of high-intent content that these engines actually cite. We’re looking for a Senior Backend Engineer to help scale our data ingestion pipelines and LLM-based evaluation systems. You’ll be working on the core engine that benchmarks brand mentions against competitors across various model architectures.
We are a small, results-driven team focused on high-intent lead generation for SaaS. If you're interested in the intersection of search behavior and automated content strategy, we’d love to hear from you. I am the founder and will be personally reviewing and responding to all applications.
GrackerAI - A Generative Engine Optimization (GEO) platform, addressing a critical gap as businesses discover they’re invisible in AI-powered search engines despite heavy investments in traditional SEO
I’ve been looking into how AI agents and “vibe coding” are changing the way we think about digital identity. The problem is shifting from verifying who someone is to understanding who actually performs an action, especially when both humans and AI share access.
Two trends stand out:
1. Identity systems are starting to use behavioral and contextual signals instead of just passwords or keys.
2. Our current trust models break when adaptive AI systems act independently.
As AI agents become more autonomous, is it time to design identity systems that verify intent, not just identity?
I’ve spent years selling and evaluating cybersecurity solutions from both sides of the table. This post dissects why traditional outbound tactics almost never land with CISOs and what actually drives buying behavior.
Two key observations: trust is built through peer networks and practitioner validation, not polished outreach; and most CISO buying decisions emerge from internal problem framing long before vendors contact them. Interestingly, data shows over 80% of deals start from an internal referral or known tech stack gap.
Question to the readers: ow have your enterprise sales or procurement experiences aligned—or conflicted—with this behavior pattern?
I’ve been exploring how identity systems need to evolve as enterprises become increasingly autonomous — with AI agents and APIs making decisions without human intermediaries. The article argues that static credentials like passwords and API keys can’t scale in these environments. Instead, identity must be dynamic, context-aware, and cryptographically verifiable.
Three key areas stood out: decentralized identity standards, just-in-time credential issuance, and autonomous trust negotiation between agents.
I’m curious — how are others approaching machine identity lifecycle management when both issuance and trust decisions must happen autonomously in real time?
This piece explores why users perceive secure authentication as friction rather than protection. Three key insights stood out:
1. loss aversion drives users to avoid perceived effort
2. mental models of “security” lag behind actual threat models
3. familiarity bias favors weak but habitual patterns
Empirically, usability testing shows rejection rates rise sharply when authentication adds more than two new steps.
I’d love to hear from others—what design trade-offs have you found most effective in aligning user convenience with real security gains?
I’ve been exploring how AI is reshaping top-of-funnel growth for B2B SaaS. A few patterns stand out:
1. AI is driving hyper-personalization at scale, especially in outbound messaging where dynamic content adapts in real time to prospect behavior.
2. Predictive intent models are proving more reliable than traditional firmographic filters, helping teams prioritize accounts with higher conversion likelihood.
3. Multi-channel orchestration is shifting from manual sequencing to algorithmic optimization—AI selects touchpoints and cadence based on historical engagement data.
The challenge isn’t lack of tools but integrating these systems without fragmenting data flows. How are you managing the integration between AI-led demand gen systems and legacy CRMs?
I wrote this piece to explore how tech debt accumulates and why it often undermines scaling efforts if left unresolved.
Two points stood out during my analysis: first, that debt isn’t inherently bad—it’s a trade-off that enables speed, but compounding interest appears when teams skip foundational fixes. Second, systemic failures emerge when architectural shortcuts stack, making later feature work exponentially harder.
In practice, addressing debt early often means pausing new development to refactor core data models, testing harnesses, or CI/CD pipelines.
For those here who’ve scaled fast-growing systems, how do you balance debt repayment against product velocity?
I’ve been thinking about the paradox where technically strong products succeed early but later stagnate under non-technical leadership. A specific challenge I’ve seen is when executives optimize for speed-to-market while ignoring long-term maintainability, creating technical debt that compounds later.
For those building or leading technical companies, how do you balance short-term execution with leadership that truly understands technical depth?