Large language models (LLMs) are advancing toward systems that can reason across text, images, audio and code, use external tools, and complete supervised multi-step workflows. The practical way to prepare is not to guess which model will dominate, but to build strong data practices, run task-specific evaluations, control agent permissions, and choose systems by capability, cost, privacy, reliability and governance.
What LLMs are likely to do next
LLMs are foundation models trained on very large collections of text. The next wave combines language generation with several surrounding capabilities rather than treating a model as a stand-alone chatbot.
Multimodal understanding and generation
Future systems will increasingly accept combinations of text, images, audio, video and structured files. That enables tasks such as reading diagrams alongside specifications, interpreting a recorded support call, or producing code from a visual interface description. Availability and quality will vary by model, region and deployment, so test the exact inputs your workflow uses.
Stronger reasoning and coding
Progress is likely to focus on longer chains of reasoning, more dependable code generation, and the ability to check intermediate work. These improvements should be treated as measured capabilities on defined tasks, not as proof of human-level general intelligence.
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Tool use and workflow agents
An agent links a model to software tools, databases or APIs and lets it decide which action to take next. This can automate research, data transformation, ticket handling or software operations, but it also introduces permission, security and audit requirements that do not exist in a simple question-and-answer interface.
Scientific and engineering discovery
Stanford’s 2024 AI Index cites AlphaDev’s work on algorithmic sorting and GNoME’s work on materials discovery as visible examples of models contributing to scientific problems. They show a direction for innovation, not a guaranteed timetable for every field.
Evidence that capability and access are accelerating
Several indicators reported by Stanford HAI show why LLM planning needs to account for rapid change:
| Indicator | Reported finding | Qualification |
|---|---|---|
| Industry participation | Nearly 90% of notable AI models in 2024 originated in industry. | Stanford HAI, 2025; “notable” follows that report’s definition. |
| Training compute | Compute used for notable AI models was doubling approximately every five months. | Stanford HAI, 2025; an approximate trend, not a forecast for every model. |
| Training data | LLM training-dataset sizes were doubling approximately every eight months. | Stanford HAI, 2025; dataset size alone does not establish data quality. |
| Training power | Power required for training was doubling annually. | Stanford HAI, 2025; energy use depends on hardware, location and training design. |
| New model releases | The number of new LLMs released worldwide in 2023 doubled from the previous year. | Stanford’s 2024 AI Index; release counts do not measure usefulness or reliability. |
| Query cost | A model scoring the equivalent of GPT-3.5 (64.8 on MMLU) fell from $20.00 to $0.07 per million tokens. | Stanford HAI, 2025; November 2022 to October 2024, comparing the lowest reported price for that capability level. |
The cost figure is especially important for adoption: an application that was uneconomical at 2022 prices may be practical at 2024 prices. It does not mean every model or workload costs $0.07 per million tokens; context length, output tokens, hosting, tools and volume discounts can change the bill.
How to prepare your organization or project
- Map decisions and workflows. List tasks where language, vision, coding or document analysis creates measurable value. Separate low-risk drafting from decisions that affect money, safety, access or legal rights.
- Build a representative test set. Save real, permissioned examples, including difficult cases, uncommon terminology, multilingual inputs and known failure modes. Define acceptable answers and unacceptable actions before comparing models.
- Prepare data boundaries. Classify confidential, personal and regulated information. Decide what may be sent to an external provider, what must be masked, how long prompts and outputs may be retained, and who can retrieve logs.
- Design for model replacement. Keep prompts, retrieval logic, tool adapters and evaluation code separable from the model API. A managed LLM platform or cloud AI model service can simplify operations, but portability still depends on your interfaces and data formats.
- Pilot with human review. Start with a narrow workflow, route uncertain cases to a person, and record corrections. Expand only when quality, latency, cost and incident rates meet predefined thresholds.
- Train the people around the system. Users need instruction on verification, sensitive-data handling, prompt injection and escalation. Engineers need secure tool design, monitoring and rollback procedures.
How to compare competing LLMs
There is no universally best LLM. Select the system that performs your important tasks within your risk and operating limits.
| Comparison axis | Questions to test | Evidence to retain |
|---|---|---|
| Capability and domain fit | Does it solve your actual tasks, terminology and languages? Can it use images, code or structured data when required? | Blind test results on your representative set, with error categories. |
| Price and latency | What are input and output rates, minimum commitments, rate limits and response times at your expected volume? | A workload-based cost model and latency measurements under realistic concurrency. |
| Context limits | How much source material can it process reliably, and does quality degrade near the advertised limit? | Tests using your longest documents and multi-turn conversations. |
| Privacy and retention | Are prompts used for training? Where is data processed? Can retention, encryption and deletion be configured? | Current contractual terms, configuration records and access reviews. |
| Reliability and evaluation | How often does it hallucinate, refuse valid requests, break formats or change behavior after an update? | Version-pinned evaluations, regression results and monitoring alerts. |
| Integration | Does it support your identity system, APIs, retrieval stack, observability tools and required output formats? | Integration tests, failure handling and recovery time. |
| Governance and incident response | Can you audit prompts, tool calls and approvals? Is there a documented process for abuse, outages and unsafe outputs? | Logs, ownership assignments, escalation contacts and post-incident reviews. |
Public leaderboards are useful starting points, but Stanford notes that evaluation and responsible-AI reporting are not standardized enough for simple rankings. Your own tests should decide the production choice.
Risks of relying on AI agents
Incorrect actions at machine speed
An agent can confidently call the wrong API, alter a record or send a message. Require least-privilege credentials, explicit approval for irreversible actions, transaction limits and an emergency stop.
Prompt injection and untrusted content
Instructions hidden in a web page, document or email can conflict with the user’s goal. Treat retrieved content as data, not authority; isolate tools, validate parameters and keep secrets outside model-visible text.
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Agents may combine information from sources that were individually permitted but jointly sensitive. Enforce source-level access checks, redact sensitive fields, restrict retention and review logs for exposed data.
Overtrust and automation bias
Fluent output can make people accept unsupported claims. Display provenance where possible, require human review for consequential decisions and measure error rates by user group and task type.
Drift and unexpected incidents
Model updates, changing data and tool failures can alter behavior without a code change. Pin versions when possible, rerun regression tests after updates, monitor production outcomes and maintain a rollback path.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Governance practices that make adoption safer
NIST’s AI Research and Development (ARIA) program evaluates risks through model testing, red-teaming and field testing. Its stated aim is: “The program will result in guidelines, tools, methodologies, and metrics that organizations can use for evaluating their systems and informing decision making regarding positive or negative impacts.”
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NIST’s Generative AI Profile (NIST AI 600-1, published July 26, 2024) provides a risk-management reference for organizations deploying generative AI. Apply that mindset throughout the system lifecycle:
- Before launch: document intended use, affected people, data sources, threat scenarios, evaluation methods and acceptance thresholds.
- During operation: log model versions, prompts, outputs, tool calls, approvals and failures while limiting access to sensitive logs.
- After incidents: preserve evidence, contain the affected workflow, notify responsible owners, correct the cause and update tests before re-enabling automation.
- At review points: reassess the system when the model, prompt, retrieval corpus, tool permissions or business purpose changes.
What remains uncertain
Capability, pricing and deployment patterns are changing quickly. Forecasts about artificial general intelligence, permanent job losses or universal autonomous agents remain contested; they are not reliable planning assumptions. Build reversible projects, use measurable outcomes and keep a human accountable for consequential decisions.
The most durable preparation is organizational rather than brand-specific: clean data, well-defined tasks, repeatable evaluations, modular integrations and strong controls. Those foundations let you adopt better models as they arrive without surrendering privacy, reliability or accountability.
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