August 07, 2025

Data neutrality: Safeguarding your AI’s competitive edge

“At the bottom there is a computational layer, such as the NVIDIA GPUs, anyone who provides the infrastructure for running AI. The next few layers are software-oriented, but also impacts infrastructure as well. Then there’s security and the data that feeds the models and those that feeds the applications. And on top of that, there’s the operational layer, which is how you enable data operations for AI. Data being so foundational means that whoever works with that layer is essentially holding the keys to the AI asset, so, it’s imperative that anything you do around data has to have a level of trust and data neutrality.” ... The risks in having common data infrastructure, particularly with those that are direct or indirect competitors, are significant. When proprietary training data is transplanted to another platform or service of a competitor, there is always an implicit, but frequently subtle, risk that proprietary insights, unique patterns of data or even the operational data of an enterprise will be accidentally shared. ... These trends in the market have precipitated the need for “sovereign AI platforms”– controlled spaces where companies have complete control over their data, models and the overall AI pipeline for development without outside interference.

The problem with AI agent-to-agent communication protocols

Some will say, “Competition breeds innovation.” That’s the party line. But for anyone who’s run a large IT organization, it means increased integration work, risk, cost, and vendor lock-in—all to achieve what should be the technical equivalent of exchanging a business card. Let’s not forget history. The 90s saw the rise and fall of CORBA and DCOM, each claiming to be the last word in distributed computing. The 2000s blessed us with WS-* (the asterisk is a wildcard because the number of specs was infinite), most of which are now forgotten. ... The truth: When vendors promote their own communication protocols, they build silos instead of bridges. Agents trained on one protocol can’t interact seamlessly with those speaking another dialect. Businesses end up either locking into one vendor’s standard, writing costly translation layers, or waiting for the market to move on from this round of wheel reinvention. ... We in IT love to make simple things complicated. The urge to create a universal, infinitely extensible, plug-and-play protocol is irresistible. But the real-world lesson is that 99% of enterprise agent interaction can be handled with a handful of message types: request, response, notify, error. The rest—trust negotiation, context passing, and the inevitable “unknown unknowns”—can be managed incrementally, so long as the basic messaging is interoperable.

Agents or Bots? Making Sense of AI on the Open Web

The difference between automated crawling and user-driven fetching isn't just technical—it's about who gets to access information on the open web. When Google's search engine crawls to build its index, that's different from when it fetches a webpage because you asked for a preview. Google's "user-triggered fetchers" prioritize your experience over robots.txt restrictions because these requests happen on your behalf. The same applies to AI assistants. When Perplexity fetches a webpage, it's because you asked a specific question requiring current information. The content isn't stored for training—it's used immediately to answer your question. ... An AI assistant works just like a human assistant. When you ask an AI assistant a question that requires current information, they don’t already know the answer. They look it up for you in order to complete whatever task you’ve asked. On Perplexity and all other agentic AI platforms, this happens in real-time, in response to your request, and the information is used immediately to answer your question. It's not stored in massive databases for future use, and it's not used to train AI models. User-driven agents only act when users make specific requests, and they only fetch the content needed to fulfill those requests. This is the fundamental difference between a user agent and a bot.

The Increasing Importance of Privacy-By-Design

Today’s data landscape is evolving at breakneck speed. With the explosion of IoT devices, AI-powered systems, and big data analytics, the volume and variety of personal data collected have skyrocketed. This means more opportunities for breaches, misuse, and regulatory headaches. And let’s not forget that consumers are savvier than ever about privacy risks – they want to know how their data is handled, shared, and stored. ... Integrating Privacy-By-Design into your development process doesn’t require reinventing the wheel; it simply demands a mindset shift and a commitment to building privacy into every stage of the lifecycle. From ideation to deployment, developers and product teams need to ask: How are we collecting, storing, and using data? ... Privacy teams need to work closely with developers, legal advisors, and user experience designers to ensure that privacy features do not compromise usability or performance. This balance can be challenging to achieve, especially in fast-paced development environments where deadlines are tight and product launches are prioritized. Another common challenge is educating the entire team on what Privacy-By-Design actually means in practice. It’s not enough to have a single data protection champion in the company; the entire culture needs to shift toward valuing privacy as a key product feature.

Microsoft’s real AI challenge: Moving past the prototypes

Now, you can see that with Bing Chat, Microsoft was merely repeating an old pattern. The company invested in OpenAI early, then moved to quickly launch a consumer AI product with Bing Chat. It was the first AI search engine and the first big consumer AI experience aside from ChatGPT — which was positioned more as a research project and not a consumer tool at the time. Needless to say, things didn’t pan out. Despite using the tarnished Bing name and logo that would probably make any product seem less cool, Bing Chat and its “Sydney” persona had breakout viral success. But the company scrambled after Bing Chat behaved in unpredictable ways. Microsoft’s explanation doesn’t exactly make it better: “Microsoft did not expect people to have hours-long conversations with it that would veer into personal territory,” Yusuf Mehdi, a corporate vice president at the company, told NPR. In other words, Microsoft didn’t expect people would chat with its chatbot so much. Faced with that, Microsoft started instituting limits and generally making Bing Chat both less interesting and less useful. Under current CEO Satya Nadella, Microsoft is a different company than it was under Ballmer. The past doesn’t always predict the future. But it does look like Microsoft had an early, rough prototype — yet again — and then saw competitors surpass it.

Is confusion over tech emissions measurement stifling innovation?

If sustainability is becoming a bottleneck for innovation, then businesses need to take action. If a cloud provider cannot (or will not) disclose exact emissions per workload, that is a red flag. Procurement teams need to start asking tough questions, and when appropriate, walking away from vendors that will not answer them. Businesses also need to unite to push for the development of a global measurement standard for carbon accounting. Until regulators or consortia enforce uniform reporting standards, companies will keep struggling to compare different measurements and metrics. Finally, it is imperative that businesses rethink the way they see emissions reporting. Rather than it being a compliance burden, they need to grasp it as an opportunity. Get emissions tracking right, and companies can be upfront and authentic about their green credentials, which can reassure potential customers and ultimately generate new business opportunities. Measuring environmental impact can be messy right now, but the alternative of sticking with outdated systems because new ones feel "too risky" is far worse. The solution is more transparency, smarter tools, a collective push for accountability, and above all, working with the right partners that can deliver accurate emissions statistics.

Read more here ...