For the past few years, AI development has often been measured by one question: which model is the smartest? However, competition in 2026 is starting to shift. The measure is no longer just the ability to answer difficult questions, write code, or understand images, but rather how much it costs AI to complete a task thoroughly.
This change is significant because an AI that is slightly smarter but takes a long time and costs a lot may not be useful for most people. Conversely, a model that is smart enough, fast, stable, and inexpensive can be integrated into more products—from customer service, document analysis, to programming assistants.
From Smart Models to Efficient Work Machines
On August 25, 2026, OpenAI announced the first measurement results for its custom inference chip called Jalapeño. Inference is the process when an AI model executes user requests and generates answers. According to OpenAI, the chip shows higher throughput per kilowatt and lower token latency compared to commercial systems in the InferenceX benchmark.
What’s interesting is not just the chip's name. OpenAI is trying to control more parts of the AI technology chain: models, presentation software, chips, memory, networks, and data centers. From a business perspective, this approach makes sense. If each part can be optimized together, the company can reduce costs and speed up responses without always having to create much larger models.
OpenAI also mentioned that GPT-5.6 Sol achieved high results on the Artificial Analysis Coding Agent Index with 54 percent less output token usage compared to certain benchmark models. This figure comes from claims and benchmarks provided by the company, so it should not be considered a universal measure for all types of tasks. However, the direction is quite clear: token usage efficiency is starting to become an important feature, not just a technical detail.
Read OpenAI's explanation about the Jalapeño chip and AI efficiency.
Cheaper Models Can Change the Types of Tasks Considered Worth Automating
Imagine a company has 100,000 customer documents. If the cost of analyzing each document is too high, the project may only be done by taking a small sample. But when processing costs decrease and the results are accurate enough, the company can analyze all documents, identify complaint patterns, and make more specific product improvements.
The same applies to small businesses. Previously, creating daily sales reports, checking product catalogs, or tailoring marketing messages for several customer segments required a lot of manual time. With cheaper AI, these tasks can be done more frequently.
This does not mean that all automated tasks will immediately generate profits. Efficiency on one side can drive usage on the other. When AI is cheaper, people may actually request more analysis, create more content variations, or run more experiments. Thus, cost savings do not automatically mean that company expenditures decrease. Work capacity could increase much faster.
Agents Become the Reason Why Efficiency is Increasingly Important
Another concurrent development is the increasing focus on AI agents. Unlike ordinary chatbots that wait for a single question and then provide one answer, agents are designed to perform multiple steps: reading context, using tools, checking results, and then proceeding to the next action.
Google openly refers to 2026 as the “agentic Gemini era” and positions agents as an important part of Gemini's development. In practice, this concept means AI not only helps draft emails but can also assist in searching for information, processing data, planning steps, or interacting with other applications.
Anthropic also positions Claude Opus 5 as a model for long and multi-step tasks. The company claims this model approaches the capabilities of the Fable 5 model at half the price, emphasizing performance in coding, knowledge work, and business automation. Such benchmark claims should still be read carefully as some evaluations come from internal testing or materials chosen by the model creators.
See the direction of agentic AI discussed by Google at I/O 2026 and Anthropic's explanation of Claude Opus 5.
The Risks: Longer Tasks Mean Longer Room for Errors
Agents that can do more certainly offer benefits but also amplify the impact of errors. If a chatbot misexplains a formula, users may notice it immediately. If an agent misreads data and then sends an email to a customer, updates a system, or executes a business process, the error could spread to many places.
Therefore, AI capabilities need to be paired with authority limits. Agents can draft documents, but humans must still approve the sending. Agents can find changes in code, but deployment to production systems requires checks. Agents can propose transactions, but should not have unrestricted access to accounts or databases.
Another issue is prompt injection, which refers to hidden instructions in documents or web pages that attempt to alter agent behavior. Models capable of using many tools will have a broader risk surface. The greater the access, the more important it is to log activities, restrict permissions, and check results.
What This Means for Us
For regular users, this change means AI will increasingly be present as part of the applications already in use, not just as separate chatbot sites. We may not always see the model or brand, but we will feel the benefits through more contextual searches, administrative assistance, document summaries, and automation features in work applications.
For workers, the most valuable skills are not just knowing how to write prompts. More importantly, it is the ability to break down tasks into clear steps, provide the right data, and check AI results before use. Those who understand the workflow process are usually better prepared to leverage agents compared to those who are just chasing the latest models.
What Can Be Done Now
- Choose measurable tasks. Start with repetitive tasks like summarizing reports, categorizing customer questions, or drafting documentation.
- Limit AI access. Do not immediately grant permission to send messages, alter data, or execute transactions without human approval.
- Measure results based on completed tasks. Pay attention to time saved, number of revisions, error rates, and cost per task—not just the quality of seemingly impressive answers.
- Keep a decision trail. Record data sources, instructions used, AI results, and changes made by humans so that the process can be audited.
The next phase in AI is likely not determined by the model that creates the most impressive demos. The winner will be the technology that can complete real tasks at reasonable costs, with verifiable results, and manageable risks.
For users, this is good news as well as a reminder. AI will become more useful, but important decisions still require humans who understand the context, goals, and consequences of each action.
Sources & Further Reading
- The full stack behind abundant intelligence — OpenAI
- I/O 2026: Welcome to the agentic Gemini era — Google
- Introducing Claude Opus 5 — Anthropic
– Rio Yotto @rioyotto