Smarter Scaling Laws, The Risks of AI-Generated Code, and Data Engineering’s 2025 Reality Check

Biweekly Data & Analytics Digest: Cliffside Chronicle

Author

Josh Miramant
08 Oct • Estimated Reading Time: 8 minutes

Why Scaling Laws Are the Hidden CFO of AI

MIT researchers are rethinking how we train large language models. Instead of throwing endless compute at bigger datasets, they propose a framework that balances accuracy with efficiency by optimizing “scaling laws.” Most teams overtrain or misallocate resources because they rely on simplistic heuristics about model size and data. Their work introduces methods for budget-aware training (essentially teaching us how to extract more performance per dollar by choosing the right model-to-data ratio instead of scaling blindly).

This research should make mid-market teams sit up. The hyperscalers (OpenAI, Anthropic, Google) can afford brute force, but most enterprises cannot. When your AI roadmap hinges on building or fine-tuning LLMs, budget-maximization approaches are basic survival. Databricks and Snowflake are both moving to support more efficient training workflows, but too many companies still default to “bigger is better.”

What MIT is showing is that smarter scaling (data curation, right-sizing models, balancing training runs) can deliver competitive performance without burning through budgets.

AI Agents Will Upend More Than Just Processes

AI agents are set to transform business operations. They can coordinate multiple steps, pull from diverse data sources, and even collaborate with other agents to solve real problems, unlike chatbots or task-specific automations. The next evolution of enterprise AI, capable of moving us past static dashboards and manual triggers into continuous, intelligent operations.

Agent frameworks are emerging in Databricks, Microsoft Fabric, and Snowflake. The real question isn’t if they’ll reshape business, but how you’ll govern, monitor, and connect them to your data stack. Most enterprises will stumble not because agents can’t deliver, but because they bolt them onto brittle data foundations.

Agentic AI will only be as good as the semantics, lineage, and governance beneath it.

Coding Is Still the Beating Heart of AI Progress

Despite all the noise around agents, multimodality, and alignment, coding is still the most powerful benchmark and driver of AI advancement. Models that excel at code reasoning (think Codex, AlphaCode, or Claude’s code generation) tend to generalize better across tasks. That’s because coding is the purest form of logic and constraint. There’s a right answer, it can be tested automatically, and it forces models to grapple with structure. In short, progress in code is progress in AI.

The ability to handle code is a leading indicator of whether an LLM can support real enterprise workload. Databricks knows this, which is why they’re leaning on code-first evals in Mosaic AI. Snowflake is following suit with Cortex code completion, and even OpenAI keeps tuning on code-heavy datasets.

The open question for leadership is: will the next leap in AI come from “smarter agents,” or simply from models that code better than we do?

Vibe Coding Is Cool—Until the Breach Hits

AI-generated code isn’t inherently secure. As tools like GitHub Copilot, ChatGPT, and Replit Ghostwriter churn out production-ready snippets, the security surface is exploding. These systems often replicate insecure patterns from training data or skip hardening steps entirely. The article argues that vibe coding could accelerate velocity while leaving gaping vulnerabilities across enterprise applications.

This may be the elephant in the room for mid-market teams racing to adopt AI copilots. Security by design is already tough with human developers—layer in stochastic model outputs and you multiply the problem. Databricks’ push toward secure, governed AI workflows and Microsoft Fabric’s integration with Defender are moves in the right direction, but most orgs still treat security as an afterthought.

If your devs are pasting in AI-generated code without automated scanning, dependency checks, and policy guardrails, it can be a gamble.

2025 Data Engineering: The Backbone AI Can’t Ignore

InfoQ’s trend piece surveys the state of AI/ML and data engineering in 2025, and the message is clear. The future isn’t just about bigger models, it’s about the plumbing underneath. The article highlights three big movements: agentic AI creeping into data workflows, the rising dominance of semantic layers as the new abstraction for BI/AI, and the renewed focus on governance as enterprises wake up to the risks of “move fast” AI adoption. In short, the tools are changing fast, but the core challenges remain: scale, trust, and usability.

You’ve got Databricks betting hard on the unified lakehouse with Mosaic AI, Microsoft Fabric pushing Copilot into every workflow, and Snowflake trying to abstract complexity with Cortex and Polaris. But the truth is that none of these trends erase the hard engineering work of making data usable, reliable, and governed. Agentic AI won’t rescue a broken pipeline. A semantic layer can’t cover for bad lineage. Governance tools mean nothing if your culture ignores them.

2025 is the year when data engineering either levels up into AI’s backbone or stalls enterprise adoption.

AI Governance in Insurance: From Compliance Checkbox to Competitive Edge

Databricks lays out how insurers are rethinking AI governance as they push generative AI into underwriting, claims, and customer experience. In a highly regulated industry, governance can’t just be about avoiding fines, it has to be woven into model development, deployment, and monitoring from the start. With frameworks for lineage, explainability, and access controls now maturing, the real opportunity is to turn compliance into trust-building and operational resilience.

Insurers will win when they treat governance as product strategy. Unity Catalog, for example, makes sure every model decision can be traced, explained, and defended. The same applies to Snowflake Cortex’s responsible AI tooling and Microsoft Fabric’s integration with Purview.

In practice, strong governance is the only way insurers will convince regulators, customers, and internal risk officers that AI belongs in critical workflows.

Blog Spotlight: Elevate Your Business with Advanced Intelligent Process Automation

Intelligent Process Automation is becoming a growth driver. By combining RPA with AI and machine learning, IPA moves beyond repetitive task automation to tackle higher-order processes like decision support, fraud detection, and customer engagement. For mid-market companies, this means automation is unlocking speed, accuracy, and resilience at scale. The message is clear: those who still see automation as a “nice-to-have efficiency play” risk being outpaced by competitors who treat IPA as a strategic lever for transformation.

“The real challenge in machine learning is not building the model, it’s getting the right data in the right shape at the right time.”

― Andrew Ng