Introduction

“Innovation trigger”, “Peak of Inflated Expectations”, “Trough of Disillusionment”, “Slope of Enlightenment”, “Plateau of Productivity” – all areas along the Gartner Hype Cycle model journey. We are rapidly heading towards the “Peak” if you agree with Gartner’s prediction that over 40% of Agentic AI projects will be canceled by then end of 2027. But are we already past that and entering the “Trough of Disillusionment.” To arrive at a view on this I decided to look into how the leading LLM providers approach lifecycle management of their LLMs and what gaps could lead to “AIdiocy.”

What is AIdiocy

I’ve defined AIdiocy as the point where the value an LLM delivers has been diluted through failure to ingest fresh data on a timely basis and update its reasoning to remain business vertical, culturally, and intellectually relevant to the people, process, and technologies that consume it. Some euphemisms that were once clever now are now outdated, confusing, to younger generations such as:

“Page me if you need me”

“Rewind the tape” – A VHS-era staple. Still used metaphorically, but actual tape is long gone.

“Drop a dime” – Originally meant to make a call from a payphone which cost 10 cents at one time and later became slang for snitching.

Technological shifts have phrases tied to obsolete tech (like pagers or VHS) lose meaning fast. For Generational slang what was once felt clever can now feel like a linguistic fossil. Language evolves with culture, and euphemisms are like time capsules. Many current Large LLMs have captured the past but what about dealing with changes go forward such as a new meaning to “Drop a dime” and the like or if it gets dropped altogether.

What about evolution of discrete and component technologies where a recently released product delivers innovation and must be learned at an accelerated rate. For companies sensitive to evolution may find themselves require building their own LLM or multiple LLMS – more on this later.

LLM Provider Lifecycles

This all leads me to ask how LLM providers manage and maintain the lifecycles of their LLM and the criteria followed for copyright protection, privacy, and avoiding bias. All essential for users to make informed decisions about AI deployment. While all top the providers state their commitment to managing these aspects, the level of detailed publication and the specific criteria followed vary significantly.

Here’s a breakdown of what I found in how the major LLM providers generally approach transparency on these topics:

Anthropic (Claude)

Lifecycle Management: Anthropic positions itself with a strong emphasis on “Constitutional AI” and AI safety. They’ve published their “Responsible Scaling Policy (RSP),” which outlines technical and organizational protocols for managing risks as AI systems become more capable. This includes defining AI Safety Levels (ASLs) with associated safety measures, rigorous evaluation, and continuous monitoring.

Copyright Protection: Anthropic is also involved in copyright lawsuits. A recent ruling (Bartz v. Anthropic) found their training to be “transformative fair use” but did not protect the acquisition of pirated copies. Their public statements reflect their reliance on fair use for training, and they have also started licensing content. Their commitment to “Constitutional AI” focuses more on output safety and helpfulness rather than explicit copyright compliance from training data, though it indirectly aims to prevent direct copying.

Privacy: Anthropic tends to have a more privacy-preserving default for user data. For instance, their consumer Claude AI generally doesn’t use your data for training by default unless you provide explicit feedback. They aim for transparent, preset privacy controls rather than extensive user toggles.

Bias: Bias mitigation is central to Anthropic’s “Constitutional AI” approach, which uses an AI system to review and revise its own outputs against a set of principles, including those related to fairness and reducing harmful stereotypes. They highlight their research in alignment science and aim for more robust, less biased outputs by design.

Google (Gemini, PaLM)

Lifecycle Management: Google has a well-established “Responsible AI Principles” framework and a “Responsible Generative AI Toolkit.” These resources detail their approach to designing, building, and evaluating AI models responsibly across the lifecycle. This includes safety alignment, model evaluation (for safety, fairness, and factuals), and the deployment of safeguards. They discuss red teaming, ethical reviews, and incident response.

Copyright Protection: Google is also heavily involved in copyright litigation concerning LLM training. Their defense primarily relies on fair use in the US, similar to OpenAI. They have historically been involved in digitizing books (Google Books), which involved its own fair use battles. They are also actively pursuing licensing deals with publishers. Their developer documentation discusses how their models are trained and sometimes highlights limitations regarding output originality.

Privacy: Google provides extensive controls for user data, including options for managing Gemini Apps Activity, personalizing responses (often opt-in), and deleting conversation history. For enterprise clients (via Google Cloud), there are strong guarantees that customer data won’t be used to train their foundational models.

Bias: Google is leading in AI ethics research and is very transparent about its bias mitigation efforts. Their Responsible AI Toolkit explicitly addresses bias amplification and provides guidance, tools (like LLM Comparator), and benchmarks for evaluating fairness. They acknowledge that language models can inadvertently amplify biases and provide insights into their fairness analyses, including limitations related to language and subgroups.

Granite (IBM)

Lifecycle Management: Granite models are trained on business-relevant datasets from five domains: internet, academic, code, legal, and finance. IBM emphasizes a “curated for business use” approach, with a strong focus on data governance. They aim for auditable links from a trained model back to the specific dataset version employing an end-to-end process for building and testing, starting with data collection and extending to responsible deployment tracking. IBM deploys internal and external red teaming, along with tools like FMEval and unitxt, to identify potential weaknesses and vulnerabilities post-training. They also emphasize continuous monitoring and updating to ensure peak performance.

Copyright Protection: IBM takes a distinct stance on copyright compared to some other providers, emphasizing client protection. A key differentiator for IBM is its standard contractual intellectual property protections for IBM-developed watsonx models. This means IBM provides indemnification for customers against potential copyright or intellectual property claims arising from their use of IBM’s generative AI systems. This shifts legal liability to IBM, providing a significant level of reassurance for enterprise clients. IBM states that its training data is “curated for business use” and emphasizes filtering for objectionable content and avoiding websites known for pirating materials. IBM is more transparent than many competitors by publishing details about the datasets used to train its Granite models. This allows clients to understand the model’s lineage and potentially assess risks

Privacy: IBM’s focus on enterprise clients in regulated industries drives strong privacy commitments for Granite LLMs. For clients using Granite models, particularly in self-hosted or private cloud environments (which Granite supports), IBM emphasizes that data input on the Granite family model will never be shared with Red Hat, IBM, or any other Granite users, and the models don’t continuously train on client data. This is a critical privacy feature for businesses dealing with sensitive information. IBM provides methods and guidance for anonymizing sensitive data (e.g., PII, credit card numbers) within Retrieval-Augmented Generation (RAG) systems used with Granite, through techniques like masking, tokenization, and format-preserving encryption. This ensures sensitive information is not exposed to the LLM.

Bias: IBM has a robust framework for addressing bias in its AI, deeply integrated into its Responsible AI principles and Granite development: IBM adheres to its long-standing “Pillars of Trustworthy AI,” which underpin its approach to AI ethics, including fairness and bias. IBM’s process for incorporating data into its “IBM Data Pile” for training involves a defined governance, risk, and compliance (GRC) review process. This includes scrutinizing data using tools like their proprietary “HAP detector” (for hateful and profane content) and benchmarking against internal and public models. Comprehensive Risk Detection: IBM’s Granite Guardian is a collection of models specifically designed for safeguarding LLMs by detecting a wide range of potential harms, including social bias, hate speech, toxicity, and hallucinations. Granite Guardian models are instruction fine-tuned Granite models themselves, demonstrating a layered approach to safety. Evaluation and Benchmarking: IBM conducts extensive testing, including human evaluation and internal/external red teaming, to identify potential weaknesses related to bias.¹¹ They benchmark Granite models against industry and academic standards, including specific safety benchmarks. Empowerment for Clients: IBM acknowledges that enterprises have their own values and regulations. They aim to “empower enterprises to personalize their models according to their own values (within limits),” providing tools for clients to fine-tune models and apply their own safeguards. This implies that while IBM provides a baseline, clients are also responsible for further mitigating bias in their specific applications.

In essence, IBM’s approach with Granite is characterized by its strong enterprise focus, emphasizing contractual indemnification for IP, granular privacy controls (especially for client data not being used for training), a transparent and curated data pipeline, and a multi-layered bias mitigation strategy including specialized “Guardian” models and rigorous evaluation. Their open-weight approach for some Granite models also allows for community scrutiny and customization for specific bias needs.

Meta (Llama series)

Lifecycle Management: For their open-source Llama models, Meta provides detailed technical papers on their development, training data, and architecture, which implicitly covers aspects of lifecycle. They also have an overarching “Responsible AI” initiative, publishing research and tools related to safety, fairness, and transparency in AI development.

Copyright Protection: As Llama models are open-source and widely used, Meta is also facing copyright lawsuits. Their defense relies on similar fair use arguments to OpenAI and Google. While they publish technical details of their models, explicit policies on copyright for training data for their own use are less directly publicized than those for their consumer-facing products. However, the open-source nature means the community often scrutinizes the training data.

Privacy: For their internal use and products, Meta has standard privacy policies. For Llama, as an open-source model, the user deploying the model is largely responsible for their own privacy compliance regarding input data. Meta’s publications on responsible AI sometimes touch on privacy-preserving ML techniques.

Bias: Meta is a major research contributor to bias detection and mitigation, particularly for large models. They publish numerous academic papers on fairness, accountability, and transparency in AI. Their Llama models come with “Responsible Use Guides” that caution against potential biases and suggest best practices for developers using the models.

Mistral AI

Lifecycle Management: Mistral AI, a European provider, emphasizes a “privacy-first” and “open-weight” approach. They are relatively newer but have published commitments to “preventative and proactive principles” in their development and deployment. They’ve joined initiatives focused on preventing misuse, particularly against child sexual abuse material (CSAM), and discuss stress testing and responsible hosting.

Copyright Protection: Mistral, being European, operates under the EU’s Text and Data Mining (TDM) exceptions, which allow TDM provided rightsholders haven’t opted out. They are subject to the EU AI Act, which reinforces the TDM opt-out mechanism. Their transparency tends to be more around their open-weight models and the control they offer to users, implicitly providing a different legal footing than US providers.

Privacy: Mistral explicitly highlights “privacy by design,” “data minimization,” and transparency as core principles.They emphasize their compliance with GDPR and their non-subjection to the US CLOUD Act, making them appealing for privacy-sensitive applications. They offer self-hosting options, giving users full control over their data.

Bias: Mistral, while emphasizing efficiency and performance, also includes commitments to “responsible sourcing” of training datasets to avoid harmful content like CSAM. Their public statements suggest an awareness of bias, and their open-weight nature allows for community scrutiny and fine-tuning for specific bias mitigation.

OpenAI

Lifecycle Management: OpenAI extensively documents its “Preparedness Framework” and responsible AI practices. This framework outlines their approach to identifying, measuring, and safeguarding against severe risks (including misuse and unintended harmful capabilities) throughout the model development and deployment lifecycle. They discuss red teaming, safety evaluations, and phased rollouts.

Copyright Protection: OpenAI acknowledges copyright concerns and is a defendant in several major copyright lawsuits. They emphasize the “transformative” nature of LLM training under fair use. While they don’t publish explicit, detailed criteria for how they “protect” copyright during training (e.g., specific filters), they are increasingly pursuing content licensing agreements. They also offer copyright indemnification for enterprise customers. Their terms of service clarify data usage for model improvement, often with opt-out mechanisms for enterprise users.

Privacy: OpenAI provides clear privacy policies outlining how user data is handled. For their API and enterprise offerings (like ChatGPT Enterprise), they generally commit to not using customer data for model training by default, which is a key privacy control. They offer controls for users to manage their chat history and decide if their data can be used for model improvement.

Bias: OpenAI is quite transparent about its efforts to mitigate bias. They discuss their use of Reinforcement Learning from Human Feedback (RLHF) to align models with human values and acknowledge that models can still reflect biases from their training data. They publish research on bias detection and mitigation, and often release model cards or system cards that describe known limitations and biases.

No LLM provider can definitively claim “no bias.” Instead, they publish varying levels of detail on their strategies and commitments to mitigate bias, manage privacy, navigate copyright, and ensure responsible lifecycle management. Look for:

• Dedicated Responsible AI sections/frameworks.
• Research publications on fairness, safety, and transparency.
• Clear privacy policies, especially regarding enterprise data use.
• Discussions of their data curation, RLHF, and red teaming processes.
• Commitment to external audits or adherence to emerging regulations (like the EU AI Act).

The more detailed and independently verifiable these claims are, the more confidence you can have in their approach to responsible LLM lifecycle management.

Risks in LLM Provider Use

Building on pre-trained models from popular LLM providers can accelerate development but it comes with risks you’ll want to weigh carefully, especially if you’re deploying in sensitive, regulated, or proprietary environments.

Data Privacy & Leakage

• Training-time leakage: Some models may regurgitate memorized content from their training data, including PII (Personally Identifiable Information) or proprietary code.
• Inference-time exposure: Using APIs means your prompts and outputs may be logged or analyzed unless explicitly stated otherwise in the provider’s terms. They are also vulnerable.

Licensing & IP Ambiguity

• Opaque training data: Many models are trained on web-scraped data without clear licensing, raising copyright and compliance concerns.
• Usage restrictions: Some providers prohibit use in certain industries (e.g., legal, medical) or for specific tasks (e.g., autonomous decision-making)

Hallucinations & Misalignment

• Pretrained models can confidently generate factually incorrect or misleading content.
• Alignment with your domain or values may require significant fine-tuning or guardrails.

Security Vulnerabilities

• Prompt injection: Malicious inputs can hijack model behavior or leak sensitive data.
• Model exploitation: Attackers may reverse-engineer outputs to infer training data or system behavior.

Operational Dependence

• API reliance: If you’re using hosted APIs, you’re at the mercy of pricing changes, rate limits, outages, or policy shifts.
• Model updates: Providers may silently update models, changing behavior without notice—problematic for reproducibility or compliance.

Ethical & Regulatory Risks

• Bias and fairness: Pretrained models may reflect societal biases present in their training data.
• Auditability: Lack of transparency in training and architecture can make it hard to meet regulatory or internal audit requirements.

Mitigation Strategies

• Use open-weight models (e.g., IBM Granite Apache 2.0 licensed models, Llama, Mistral) for greater control and transparency.
• Apply quantization, fine-tuning, and local deployment to reduce reliance on external APIs.
• Implement prompt sanitation, output filtering, and human-in-the-loop review for safety.

Building LLMs

Given the aforementioned risks, here’s a starting point of how it’s done, assuming you’re aiming to train or deploy your own model from scratch or continue build on existing LLMs:

Define Scope and Scale

• Use case: Chatbot? Code generation? Domain-specific Q&A?
• Model size: Smaller (1–7B parameters) models are feasible on local setups; larger ones require clusters or cloud compute.
• Open weights or custom: Will you start with existing LLMs or train from scratch?

Data Curation

• Corpus: Massive, diverse, and high-quality text data.
• Cleaning: Remove duplicates, profanity, PII.
• Alignment data (optional): For fine-tuning (preferred datasets and instruction-following).

Training Infrastructure

• Hardware: GPUs like A100s, H100s, or consumer-grade (RTX 3090, 4090) for smaller models.
• Software stack
  • Open-source machine learning frameworks like PyTorch
  • Open source machine learning libraries like TensorFlow
•  Checkpointing, logging, evaluation: Weights & Biases, TensorBoard, or custom tools.

Training Process

• Scheduler: Learning rate warm-up.
• Budget carefully, it can cost tens of thousands of dollars for larger models.

Post-Training Tuning

• SFT (Supervised Fine-Tuning) with instruction-following data
• RLHF (Reinforcement Learning from Human Feedback) for alignment

Monitoring, Evaluation & Guardrails

• Measure and apply safety filters, rate-limiting, or human-in-the-loop review

These only a few areas before getting into deeper aspects. Building for local deployment using pretrained open-weight models like IBM Granite Apache 2.0 licensed models, LLaMA 3 with GGUF + llama.cpp, or Mistral-7B are reportedly good considerations.

Conclusion

My definition of “AIdiocy” is a symptom of failure in knowing the full chain of custody of the LLMs you use and how they are produced. While LLM provider make best efforts in transparency, they still are a “black box” to many with trust in them bordering on a leap of faith and little recourse in accountability.

If data is the fuel driving your business and AI and increasing component to deriving value and/or a source of data itself, you need to have a chain of custody model and the potential business risk at each stage. You will find it necessary to invest in building your own LLMs possibly per business line use as  a “firewall” of sorts from data leakage or business intelligence contamination so differentiating value is not diluted by external LLM consumption avoiding “AIdiocy” over time.

For what its worth

– Joe