What is "Chatgpt Definitely Uses Google"?
"ChatGPT definitely uses Google" is a phrase highlighting the reality that most generative AI tools, while powerful, rely on underlying web data, public information, and often human-created knowledge that originates from sources like Google Search. For business leaders, it emphasizes that AI is not a magic box but a tool dependent on the quality of its inputs and training data.
This creates a critical pain point: businesses risk making poor software, data, or service procurement decisions if they treat AI outputs as infallible, proprietary intelligence rather than as synthesized, potentially outdated, or biased compilations of existing public information.
- Data Provenance — The origin and history of the data used to train an AI model, which impacts the reliability and fairness of its outputs.
- Model Hallucination — When an AI generates plausible but incorrect or fabricated information, a major risk for factual business decisions.
- Knowledge Cut-off — The date after which an AI model's training data ends, limiting its awareness of recent events, tools, or market shifts.
- Source Verification — The process of checking an AI's claims against primary, authoritative sources before acting on them.
- Vendor Transparency — The degree to which a software or service provider openly discloses the data sources, training methods, and limitations of their AI tools.
- Procurement Due Diligence — The structured evaluation of an AI-powered tool's foundations, not just its marketing claims, before purchase.
This topic is crucial for procurement leads, product teams, and founders who are responsible for selecting AI-driven tools. Understanding this principle solves the problem of over-reliance on AI recommendations, preventing costly investments in solutions that are effectively just repackaged, unverified public data.
In short: Recognizing that AI tools often rely on public web data forces a more critical, verification-based approach to using them for business decisions.
Why it matters for businesses
Ignoring the dependence of AI on existing public data leads to strategic missteps, wasted budgets, and operational risks, as decisions are based on unverified, generic, or obsolete information.
- Wasted software budget → By critically evaluating if an "AI-powered" tool offers unique logic or just a slick interface on common data, you avoid paying a premium for functionality you could access more directly.
- Poor vendor fit → Understanding an AI's training data helps assess if it truly understands your niche industry's jargon and processes, or if it provides generic, irrelevant outputs.
- Compliance and security risks → AI tools trained on unclear data sources may inadvertently introduce IP or GDPR violations; due diligence on data provenance mitigates this legal exposure.
- Strategic lag → Relying on an AI with a stale knowledge cut-off causes you to miss emerging competitors or tools; a verification habit keeps you current.
- Erosion of trust → If your team or customers discover your AI-driven features produce "hallucinated" information, it damages credibility; source checking preserves integrity.
- Inefficient procurement → Without this lens, you compare tools based on feature lists alone; with it, you compare the quality and uniqueness of their underlying intelligence.
- Missed innovation → Over-reliance on AI summaries can cause you to skip deeper research, missing subtle details and genuine market gaps that a tool might overlook.
- Operational dependency → Building a process on an AI's unverified output creates single points of failure; designing with verification checkpoints builds resilience.
In short: Acknowledging AI's reliance on existing data transforms it from an oracle into a starting point, forcing the verification that protects budgets, ensures fit, and drives sound strategy.
Step-by-step guide
Navigating AI tool selection and use can feel overwhelming due to technical claims and marketing hype; this structured process removes the guesswork.
Step 1: Define the specific business problem
The obstacle is starting with a tool, not a task. This leads to buying solutions in search of a problem. Clearly articulate the exact process gap or decision you need support with, such as "We need to pre-qualify software vendors faster" or "We need to summarize technical feedback from user interviews."
Step 2: Deconstruct the AI's claimed value
The risk is taking "AI-powered" at face value. Ask the provider: what specific data sources train your model? Is it a general model (like GPT) fine-tuned, or a proprietary dataset? The goal is to move from a vague claim to a tangible data foundation.
Step 3: Test with niche, current queries
Generic questions yield generic answers that mask limitations. To verify relevance and knowledge recency, test the tool with queries specific to your field from the last 6-12 months.
- Quick test: Ask it to list the key features of a software category that emerged or significantly changed after its stated knowledge cut-off date.
- How to verify: Cross-check the AI's answer with recent industry reports, news, or official vendor changelogs.
Step 4: Demand transparency in procurement
The pain is opaque sales processes. During vendor evaluation, formally request documentation on data sourcing, training methodologies, and update cycles. Treat lack of clear answers as a major red flag, not a technical detail.
Step 5: Establish a human-in-the-loop protocol
Automating decisions based solely on AI output is high-risk. Design a mandatory verification step where a team member uses primary sources (like official vendor websites, trusted review platforms, or direct demos) to confirm key facts before action.
- For vendor lists: Visit 3-5 recommended company sites directly.
- For feature comparisons: Check the vendor's own documentation.
Step 6: Audit outputs for bias and gaps
AI can amplify biases in its training data, leading to skewed recommendations. Regularly review the tool's outputs for patterns—does it consistently favor large, well-known brands over niche players? This audit ensures you see a full market view.
Step 7: Plan for continuous reassessment
The tech landscape changes faster than most AI models retrain. Schedule quarterly reviews to ask: Is this tool's knowledge still current? Have new, better-dataified competitors emerged? This prevents strategic stagnation.
In short: Solve AI over-reliance by defining your need first, rigorously testing the tool's knowledge foundation, and building mandatory human verification into your workflow.
Common mistakes and red flags
These pitfalls persist because AI interfaces are designed to feel authoritative, discouraging critical inquiry.
- Treating the first answer as final → This leads to action on incomplete or incorrect data. Fix: Always use follow-up prompts to challenge assumptions and ask for sources.
- Procuring based on demo perfection → Demos use curated, simple examples. Fix: Insist on a pilot using your own complex, real-world business scenarios.
- Confusing interface with intelligence → A beautiful UI can mask a simple data lookup tool. Fix: Ask what the tool does that a skilled person with a Google Search API could not.
- Ignoring the knowledge cut-off date → This results in strategies based on outdated market maps. Fix: Note the date prominently and never use the tool for analysis of recent events without verification.
- Assuming neutrality → Training data has commercial and cultural biases. Fix: Actively prompt for alternatives (e.g., "also show me smaller European vendors").
- Neglecting data security questions → Inputting proprietary data into a tool may leak IP or violate GDPR. Fix: Before sharing sensitive info, confirm the provider's data processing agreement and retention policies.
- Vendor lock-in via proprietary formats → Getting your data "into" an AI system is easier than getting clean data "out." Fix: Prior to commitment, request a data export sample to ensure you retain your insights.
- Using AI for ethical or legal decisions → AI cannot assume liability. Fix: Restrict AI use to information gathering only for high-stakes areas; final decisions must have human accountability.
In short: Avoid costly errors by challenging AI outputs, testing with your own data, verifying recency, and never outsourcing final accountability.
Tools and resources
Selecting the right category of tool is essential to address the specific gap between AI-generated suggestions and verified business decisions.
- AI-Powered Market Intelligence Platforms — Use these for initial landscape scanning and trend identification. They address the problem of manual market research but require source verification.
- Verified B2B Marketplaces — Platforms with human-vetted provider profiles solve the problem of unverified AI vendor lists by offering a baseline of credibility and structured comparisons.
- Web Monitoring and Alerting Tools — These compensate for AI knowledge cut-offs by tracking real-time changes on competitor websites, software changelogs, and news sources.
- Data Provenance and Lineage Tools — Crucial for in-house AI projects, they address the opacity of training data by tracking the origin and transformation of datasets.
- Traditional Analyst Reports — While not real-time, they provide a curated, deep analysis that can serve as a benchmark to check against broader AI-generated summaries.
- Procurement Software with RFx Capabilities — Use these to systematically collect validated information directly from potential vendors, creating a primary source dataset.
- Collaborative Note-Taking and Wiki Apps — Essential for documenting the verification process, linking AI suggestions to primary sources, and creating a shared knowledge base.
- API-First Data Providers — For building custom solutions, these offer direct, structured access to high-quality datasets (like company financials) as an alternative to scraping public web data.
In short: Combine AI aggregators with primary source tools and verification platforms to create a robust, trustworthy information workflow.
How Bilarna can help
Bilarna addresses the core frustration of not knowing which software or service providers to trust after an AI tool generates a list of potential options.
The Bilarna marketplace connects businesses with verified providers, applying a layer of human validation to the procurement process. This directly complements the critical step of verifying AI-generated vendor suggestions. You can use AI for broad discovery, then use Bilarna to assess vetted options in a structured, comparable format.
Its AI-powered matching suggests providers based on your project's specific requirements, but these recommendations are grounded in a verified provider programme. This programme checks key details, helping to mitigate the risks of "hallucinated" vendors or outdated information that can come from purely data-driven AI tools.
Frequently asked questions
Q: If ChatGPT uses Google, why shouldn't I just use Google myself?
AI synthesizes and summarizes vast amounts of search results quickly, which is valuable for initial exploration. The problem is losing visibility into source quality and recency. The next step is to use the AI's output as a draft checklist, then use your own search skills to verify and deepen the findings, focusing on authoritative sources.
Q: How can I tell if an AI tool is using truly proprietary data versus public data?
Ask the vendor directly for specific examples. A credible provider should be able to describe their unique data collection methods, such as licensed databases, proprietary surveys, or processed transactional data. If their answer is vague or only references "advanced algorithms," treat it as a red flag and prioritize tools with clearer provenance.
Q: Doesn't this verification process eliminate the time-saving benefit of using AI?
No, it reallocates time. AI saves time on initial gathering and sorting. Verification ensures time is not later wasted correcting errors or backtracking from poor decisions. The workflow becomes: AI generates a shortlist in minutes; you spend an hour verifying the top 3-5 options—still far faster than manually researching 50 from scratch.
Q: What's the one thing I should always verify when an AI recommends a software vendor?
Always verify the vendor's active existence and core offering. Visit their official website directly (do not click an AI-generated link). Check for:
- An active blog or news section from the last 3 months.
- Clear pricing or contact information for sales.
- Legitimate customer logos or case studies.
Q: How do GDPR and data privacy regulations affect using AI for procurement?
If you input personal data (e.g., employee or customer details) or sensitive business information into a public AI tool, you may violate GDPR. The safe approach is to only use generalized, non-sensitive queries in public AI. For sensitive analysis, use enterprise-grade tools with clear data processing agreements that guarantee compliance and data isolation.
Q: Can I use Bilarna to verify AI-generated vendor lists?
Yes, that is a primary use case. After getting a list from an AI tool, cross-reference the vendor names on Bilarna. A presence on Bilarna indicates a level of verification, and you can use the platform's structured comparison features to evaluate them further. It turns an unverified list into a manageable, validated shortlist.