The Coming AI Infrastructure Shift, The Hidden Poison in Model Data, and Why Analysts Still Get the Last Word

Biweekly Data & Analytics Digest: Cliffside Chronicle

Author

Josh Miramant
22 Oct • Estimated Reading Time: 8 minutes

Why Evaluation Is Still the Hardest Problem in AI

There’s four primary approaches to evaluating large language models (human evaluation, automated metrics, preference modeling, and behavioral testing), and each has its own trade-offs. While automated metrics like BLEU or ROUGE remain fast but shallow, human evaluations bring nuance at the cost of scale. Preference models (like those used in RLHF) bridge the gap but risk encoding subjective bias, and behavioral tests aim to measure what models do rather than how they do it. The takeaway is that the field is still missing a unified standard for measuring intelligence, reliability, and usefulness in LLMs.

Building models is no longer difficult. The true challenge is knowing when they’re “good enough”. What we’ve seen across clients is a push toward domain-specific evaluation frameworks, often integrating synthetic test sets and human-in-the-loop feedback. Without them, teams either overspend on tuning or deploy blind. Leaders shouldn’t be asking “Which model should we use?” Instead they need to ask, “How will we know it’s performing well (and for whom)?”

In the AI stack, governance and monitoring may soon matter more than model architecture itself.

7 Questions Every Data Team Should Ask, But Usually Doesn’t

Seven deceptively simple questions reveal whether a data team is actually aligned with business outcomes. These range from “Who are our users?” to “What’s our data ROI?”. They’re questions that expose the frequent gap between building pipelines and driving value. Many teams obsess over tooling and performance but ignore foundational aspects: why the data exists, how it’s consumed, and who’s accountable for results.

The real maturity test for data organizations is not whether you’re on Databricks or Snowflake, but whether you can articulate your value chain. Most teams can list every DAG in Airflow but can’t tell you which one impacts revenue. That’s a symptom of poor alignment, not bad engineering. Frameworks like these seven questions are a forcing function to reset priorities.

As budgets tighten and AI workloads expand, the teams that can justify their data decisions in business language will be the ones still standing.

Snowflake’s Managed MCP: The Infrastructure Shift Powering Secure AI Agents

Snowflake just unveiled MCP. It’s essentially serverless infrastructure for custom agent workloads, without sending data outside Snowflake’s trust boundary. The architecture enables developers to build RAG systems, inference pipelines, or even full agent workflows with data governance and isolation included. In short, it’s Snowflake’s answer to the “where do we safely run AI logic on sensitive data?” problem.

Snowflake is becoming the execution layer for intelligent workloads. There has been a tension between flexibility and control. Teams want to run open models but can’t risk data egress or shadow compute. MCP effectively closes that loop. Compared to Databricks’ model-serving or Fabric’s semantic layer integration, Snowflake’s angle is containment: bring compute to the data, not the other way around.

Architecture that treats data proximity as a security feature is becoming the new competitive edge.

The Barbarians Are at the Gate: What Happens When AI Eats the Software Stack

“The Barbarians at the Gate: How AI is Upending Systems Research” by Cheng, Liu, Pan, et al paints a sobering picture of what happens when AI stops being a tool and starts being the infrastructure. The next wave of disruption won’t come from startups building with AI. It’ll come from AI itself replacing the software ecosystem entirely. The “barbarians” are not new frameworks or competitors, but the language models that can generate, debug, and deploy code at scale. Once coding becomes automated and self-reinforcing, traditional abstractions (APIs, SDKs, even microservices) start to look like legacy scaffolding for a world that no longer needs them.

It’s right to call this an inflection point, but maybe not for the reasons most assume. In enterprise data and AI platforms, there is a quiet collapse of the build-vs-buy distinction. When AI can generate code and optimize itself, the value shifts from software assets to data context and control layers. Databricks, Snowflake, and Fabric are already evolving in that direction (consolidating orchestration, storage, and governance into unified ecosystems).

The barbarians aren’t coming; they’re already writing the next release.

The Subtle Poison in AI Data: Why Small Samples Can Break Big Models

A critical vulnerability has been exposed in modern LLMs: tiny amounts of malicious or biased data can drastically alter a model’s behavior. Even a handful of “poisoned” samples (think a few dozen bad training examples in a corpus of millions) can steer outputs in targeted, consistent ways. Because models internalize statistical correlations rather than discrete rules, these small signals can have outsized, persistent influence. This study underscores how difficult it is to ensure safety and alignment when data pipelines stretch across opaque, open-source, and human feedback loops.

Data quality control hasn’t caught up to model scale, and this research proves the risk isn’t theoretical. Tools like Databricks’ Unity Catalog or Snowflake’s Horizon are steps toward lineage and observability, but few orgs are actually validating semantic drift or checking for adversarial contamination.

The future of trustworthy AI might depend less on red-teaming and more on supply chain hygiene.

AI Governance in Insurance: From Compliance Checkbox to Competitive Edge

This predictive maintenance case study perfectly illustrates AI’s limits in real-world analytics. While models can detect anomalies and forecast failures, they often miss context (i.e. the messy, human-driven variables like sensor miscalibration, maintenance logs, or undocumented process changes). Even a highly accurate ML model can fail to outperform a veteran analyst who understood the system’s quirks. Why? Because prediction without domain grounding is just probabilistic guesswork dressed in math.

This is what’s missing in the “AI will replace analysts” narrative. Across manufacturing, healthcare, and logistics, models can excel at pattern recognition but fail at causal reasoning. You can train on petabytes in Databricks or deploy models through Fabric’s AI pipelines, but if the feedback loops between data engineers, domain experts, and operators aren’t tight, your accuracy won’t translate to reliability.

The analysts don’t need to be replaced because they’re the weak link. In reality, they’re the sanity check, and we need to build systems that amplify their judgment.

Blog Spotlight: Essential Guide to the Data Integration Process

This blog post breaks down how modern organizations can unify data across systems to unlock real-time insights and automation. The piece walks through every phase (from ingestion and transformation to validation and orchestration) highlighting how cloud-native architectures and tools like Databricks, Snowflake, and MS Fabric have made scalable, governed integration achievable for mid-market teams. The real takeaway is that integration is more than a technical milestone. It’s a maturity signal that shows the moment when your business shifts from collecting data to using it intelligently. The companies winning with AI ones who’ve actually connected with their data.

“You can’t analyze what you can’t integrate. The hardest part of analytics is making data make sense together.”

― Joe Reis