Behind the sleek interface of a chatbot lies a tangle of statistics, human choices and engineering trade-offs — and that tangle is precisely what the Oman Observer piece was pointing to when it said modern chatbots “work beautifully, even when their creators don’t quite know how.” The reality is more nuanced: today’s assistants are the result of well‑understood algorithms and enormous engineering effort, yet they also rely on emergent behaviors and opaque datasets that make some of their outputs surprisingly hard to trace or guarantee.
Large language models (LLMs) such as OpenAI’s ChatGPT family and Google’s Gemini are built from the same basic ingredients: transformer architectures trained on massive corpora of text, system design that blends automated optimization with human feedback, and product engineering that pairs models with tools, safety filters and integrations. On the technical side, they are statistical sequence predictors: at heart they compute which next token (word-piece) is most probable given a prompt and internal state. This foundational architecture — the autoregressive transformer — was popularized in the GPT‑3 work and remains the underlying mechanism for many contemporary assistants. But the product these systems become is also shaped by human-in-the-loop processes. Companies fine‑tune raw pre‑trained models with human demonstrations and rankings (a process formalized as Reinforcement Learning from Human Feedback, or RLHF), and they layer retrieval, tool calls and safety filters on top to reduce obvious failure modes. The InstructGPT / RLHF line of research showed how human preference data can dramatically change a model’s behavior — it makes responses more aligned with what people call “helpful” or “safe,” even when that alignment does not equal human‑level understanding. At the same time, researchers have not yet mapped these systems into neat, human‑readable algorithms. The study of mechanistic interpretability — trying to find the circuits inside models that compute particular functions — has made promising early progress, but the field openly accepts it is far from explaining how large, production LLMs make most decisions. The result is a hybrid reality: the training pipeline, compute and design choices are well documented; the emergent mapping from input to a specific sentence often is not.
For organizations and users, the responsible path is straightforward and non‑glamorous: pilot aggressively where productivity gains are clear, require human verification for high stakes decisions, negotiate contractual data protections when needed, and demand auditable evidence for any automation that affects compliance, safety or legal exposure. Meanwhile, continue to follow mechanistic interpretability work — it promises powerful tools for accountability, but those tools are not yet mature enough to remove human oversight.
Artificial intelligence in 2025 is a paradox: it’s a set of carefully engineered statistical machines that deliver remarkably human‑like performances, yet those performances are scaffolded by design choices, human preferences and datasets whose provenance is not always transparent. The right mental model for users, IT professionals and policymakers is neither techno‑utopian awe nor fatalistic dismissal: instead, treat LLMs as powerful, high‑utility tools that demand disciplined governance, human oversight and continued scientific inquiry into how and why they work. The more we pair practical guardrails with rigorous interpretability research, the better our chances of turning today’s awe into tomorrow’s reliable infrastructure.
Source: Oman Observer How do ChatGPT and Gemini think?
Background / Overview
Large language models (LLMs) such as OpenAI’s ChatGPT family and Google’s Gemini are built from the same basic ingredients: transformer architectures trained on massive corpora of text, system design that blends automated optimization with human feedback, and product engineering that pairs models with tools, safety filters and integrations. On the technical side, they are statistical sequence predictors: at heart they compute which next token (word-piece) is most probable given a prompt and internal state. This foundational architecture — the autoregressive transformer — was popularized in the GPT‑3 work and remains the underlying mechanism for many contemporary assistants. But the product these systems become is also shaped by human-in-the-loop processes. Companies fine‑tune raw pre‑trained models with human demonstrations and rankings (a process formalized as Reinforcement Learning from Human Feedback, or RLHF), and they layer retrieval, tool calls and safety filters on top to reduce obvious failure modes. The InstructGPT / RLHF line of research showed how human preference data can dramatically change a model’s behavior — it makes responses more aligned with what people call “helpful” or “safe,” even when that alignment does not equal human‑level understanding. At the same time, researchers have not yet mapped these systems into neat, human‑readable algorithms. The study of mechanistic interpretability — trying to find the circuits inside models that compute particular functions — has made promising early progress, but the field openly accepts it is far from explaining how large, production LLMs make most decisions. The result is a hybrid reality: the training pipeline, compute and design choices are well documented; the emergent mapping from input to a specific sentence often is not. What the Oman Observer got right — the factual core
1) LLMs are statistical sequence predictors, not classical rule engines
The Observer described model output as the result of a “probability game” that predicts likely word sequences. That is a correct, widely accepted description: modern LLMs are autoregressive models trained to predict next tokens, a paradigm set out in the GPT‑3 work and subsequent research. This probabilistic framing explains why models can produce fluent, context‑sensitive prose without having an internal, symbolic model of the world in the way a human might.2) Human feedback steers behavior without guaranteeing “understanding”
The article correctly highlights RLHF and thousands of annotators guiding tone, ethics and helpfulness. OpenAI’s InstructGPT paper documents a workflow of human demonstrations and ranked outputs used to fine‑tune base models; this approach produces outputs that look aligned even though the underlying model is still a statistical generator. RLHF changes what the model prefers to output more than why it produces an answer from first principles.3) Data opacity is real and consequential
The Oman Observer noted that engineers often do not see every data source used during pretraining and that proprietary/licensed datasets supplement web scrapes. This is a documented reality: models are trained on mixed public, private and licensed corpora, and product teams commonly work with pre‑processed, curated corpora that mask the fine‑grain provenance of particular facts. That opacity complicates provenance tracing and makes exact attribution of learned content difficult.4) Interpretability research is making progress but hasn’t solved the puzzle
The piece mentions “concept neurons” and mechanistic interpretability efforts that reveal some neuron groups consistently respond to abstract categories. That mirrors the circuits research program, which has identified specific circuits and interpretable directions in smaller models and in controlled experiments — proof that some structure is discoverable — while also warning that scaling and “superposition” (many features stored in overlapping activations) remain major obstacles.Deconstructing the claims: technical verification and caveats
This section verifies several of the most consequential technical claims in the Oman Observer piece against public research and reporting.How large are these models?
- GPT‑3 was a 175‑billion‑parameter autoregressive transformer — a hard documented fact from the original publication. That anchors the “hundreds of billions” statement.
- Research on sparse Mixture‑of‑Experts models (e.g., Switch Transformer) has shown architectures with up to trillions of parameters are feasible by activating only parts of the network per token; this justifies the phrase “sometimes trillions.” But it is important to distinguish dense models (most public flagship chat instances) from sparse, expert‑style models: trillion‑parameter sparse models can be trained while keeping per‑token compute reasonable.
What does RLHF actually do?
- The InstructGPT experiments show that supervised fine‑tuning on demonstrations plus a reinforcement learning step using human rankings can change model preferences and improve measured helpfulness and safety. RLHF is therefore an alignment tool that tunes outputs toward human judgments rather than teaching formal symbolic reasoning.
Do these systems “understand”?
- The deterministic answer depends on how one defines “understand.” From a functional, neuroscientific or philosophical viewpoint, models manifest behaviors that mimic human understanding across many tasks; from a strict symbolic semantics viewpoint they are pattern learners. The community uses terms like “synthetic cognition” or “stochastic parrot” to highlight this ambiguity: the model can generate behaviorally convincing, context‑sensitive output without necessarily possessing human‑style grounding. Emily Bender and colleagues’ critique framed LLMs as statistical mimics with social and ethical risks — a framing that helps explain why fluency should not be conflated with comprehension.
Why these systems feel “intelligent” — the engineering explanation
- Language is highly structured; predicting the next token in a coherent context often produces outputs that look like reasoning because language encodes causal chains, rhetorical devices and formal patterns. Scaling models to billions of parameters amplifies this pattern recognition so reliably that many outputs pass a Turing‑like plausibility test in casual settings.
- Tooling and retrieval systems extend raw pattern matching into grounded behavior. When a model executes a search, runs a calculator, or executes code in a sandboxed runtime, it supplements statistical generation with deterministic subroutines that constrain hallucination and improve factuality. Both OpenAI and Google use tool chains to this effect in production products.
- RLHF and instruction tuning shape the model’s output distribution so that, statistically, it prefers answers that humans rate as helpful. The effect can be dramatic: users consistently prefer RLHF‑tuned outputs, even if those outputs can still be confidently wrong on factual points.
Strengths and practical benefits (what these chatbots do well)
- Speed and accessibility: They compress days of drafting, coding exploration and summarisation work into seconds. For many users and teams this transforms productivity in drafting, research triage and routine automation.
- Versatility: A single model can switch from writing marketing copy to drafting code to summarizing legal text. This multipurpose capability reduces tool sprawl for many tasks.
- Human‑facing alignment: RLHF and instruction tuning make models more predictable and safer to use in consumer settings, reducing obvious toxic outputs and improving tone control. That makes assistants suitable for customer service, drafting and many non‑high‑stakes jobs.
- Ecosystem integration: Gemini’s embedding inside Google Workspace and ChatGPT’s plugin and API ecosystems make the assistant not just a chat box but a workflow component that can read documents, extract facts, automate simple tasks and generate deliverables inside apps people already use. Those product integrations are often the biggest productivity multiplier, sometimes more impactful than raw model IQ.
Risks and failure modes — what to worry about, and why
- Hallucinations: Confident but incorrect statements remain among the most important practical hazards. Models can fabricate citations, invent statistics or assert false chronology with persuasive fluency. This is intrinsic to probabilistic generation and is only mitigated — not eliminated — by tooling and post‑hoc verification.
- Bias and harmful outputs: Training data contains human bias. RLHF may reduce overtly harmful outputs, but is not a panacea; alignment depends on the cultural and demographic profile of annotators and the exact reward definition. The “helpful” label is not value‑neutral.
- Data governance and privacy: Consumer defaults for data retention and training differ across vendors; Google’s consumer defaults historically allowed usage of user data to improve models unless activity controls were changed, while OpenAI exposes opt‑outs and enterprise contracts with non‑training clauses. For regulated data, enterprise contracts and private deployment options are necessary.
- Provenance and IP risk: Because pretraining mixes public, licensed and proprietary corpora, it can be difficult to trace whether generated content reproduces copyrighted material or sensitive proprietary text. That uncertainty creates legal and compliance headaches for enterprises.
- Tooling and plugin attack surface: Extending models with plugins, browser access or execution sandboxes increases capability but also creates additional points of failure and vectors for data exfiltration. Admin controls and whitelists are essential to limit exposure.
- Explainability and auditability shortfalls: Interpretability research is advancing but not yet at the point where production deployments can produce human‑readable, auditable explanations for complex outputs. For high‑stakes or regulated decisions, a human‑in‑the‑loop and audit logs remain mandatory.
What mechanistic interpretability has discovered so far — and what it hasn’t
Mechanistic interpretability researchers have made visible progress on narrow, targeted problems. Examples include:- “Concept” or “feature” directions: In smaller or abridged models, researchers have found directions in activation space that correlate with semantic classes (e.g., city names, melody tokens) and can be manipulated to change output behavior.
- Simple circuits for specific tasks: Under controlled training tasks, teams have identified small circuits that implement discrete operations (for example, certain forms of syntactic agreement or coreference resolution). Those findings show that LLMs sometimes implement near‑modular computations that researchers can trace.
- Superposition at scale: As models grow, many features are stored in overlapping subspaces, making it hard to isolate a single neuron or head and say it ‘does’ a concept across contexts. This superposition complicates the search for clean explanations.
- Scaling to production models: Most interpretability breakthroughs are in research models or constrained tasks; applying the same approaches to multi‑billion‑ or trillion‑parameter, tool‑augmented production systems is an ongoing challenge. The community calls this “scalability” of interpretability and treats it as the core open problem.
Practical guidance for IT teams and power users
- Define the problem before choosing the model. For in‑document automation inside a single productivity stack, an ecosystem‑embedded assistant may reduce friction; for cross‑platform APIs and vendor neutrality, an independent model and API often make more sense.
- Treat outputs as draft artifacts, not authoritative sources. Add human verification in the loop for legal, financial, medical or safety‑critical content.
- Lock down data use via contractual terms if you work with regulated content. Ask for explicit non‑training clauses and defined retention windows. Vendor defaults differ; confirm them.
- Pilot with clear metrics: hallucination rate, verification time, latency, cost and user satisfaction. Use identical prompts across contenders to measure effective accuracy and operating cost.
- Harden integrations: whitelist plugins, disable third‑party connectors by default, and require tenant‑level governance for enterprise deployments.
The strategic picture: productization vs pure research
Two concurrent engineering stories are playing out:- The product story: vendors are packaging models into assistants with toolchains, multimodal media features, and workspace integrations that deliver immediate productivity value (e.g., Google’s Gemini integrations into Search and Workspace or OpenAI’s ChatGPT tools and plugins). Those product layers amplify usefulness and help mask some model deficiencies through retrieval, tool use and post‑processing.
- The research story: mechanistic interpretability, RLHF methodology research, and work on scaling sparse models are pushing the scientific envelope. Success in these areas could produce models that are more reliable, more explainable and cheaper to operate — but the timeline remains uncertain.
Final verdict — balanced and pragmatic
AI chatbots are not magic in the mystical sense — they are engineered systems with clear mathematical and procedural foundations. Yet they are also not fully demystified artifacts: emergence and data opacity mean engineers cannot yet produce crisp, end‑to‑end causal explanations for every fluent reply. The Oman Observer’s portrayal of a tool that “works beautifully, even when its creators don’t quite know how” is a fair, if slightly rhetorical, encapsulation of today’s state: functional systems built on strong engineering, steered by human preferences, and accompanied by an active field working to explain and control them.For organizations and users, the responsible path is straightforward and non‑glamorous: pilot aggressively where productivity gains are clear, require human verification for high stakes decisions, negotiate contractual data protections when needed, and demand auditable evidence for any automation that affects compliance, safety or legal exposure. Meanwhile, continue to follow mechanistic interpretability work — it promises powerful tools for accountability, but those tools are not yet mature enough to remove human oversight.
Appendix: cautionary notes on unverifiable claims
- Any public claim about exact production model parameter counts, or specific context window sizes for particular paid tiers, should be verified against the vendor’s own documentation at the point of procurement. Vendors sometimes advertise capability classes rather than hard numbers, and free/paid tier limits vary by region and over time. Treat press or community reports as directional, not definitive.
- Claims that a given model “understands” human values or moral judgment in the human sense are philosophical and empirical assertions that exceed the current evidence. Interpret such language as shorthand for “produces outputs that align with human‑judged helpfulness” rather than proof of human‑like comprehension.
Artificial intelligence in 2025 is a paradox: it’s a set of carefully engineered statistical machines that deliver remarkably human‑like performances, yet those performances are scaffolded by design choices, human preferences and datasets whose provenance is not always transparent. The right mental model for users, IT professionals and policymakers is neither techno‑utopian awe nor fatalistic dismissal: instead, treat LLMs as powerful, high‑utility tools that demand disciplined governance, human oversight and continued scientific inquiry into how and why they work. The more we pair practical guardrails with rigorous interpretability research, the better our chances of turning today’s awe into tomorrow’s reliable infrastructure.
Source: Oman Observer How do ChatGPT and Gemini think?
