Is AI a general-purpose technology, or a high-cost tool that is not yet mature? This question has been raised repeatedly over the past year.
As an enterprise long engaged in website development and digital services, we have seen both the practical value of AI at the efficiency level and its current capability boundaries. The following is a review from two dimensions: “current technological reality” and “possible directions of evolution.”
I. Current Technological Reality
Large models: broad knowledge, but lacking true understanding. Current large language models perform well in knowledge coverage and reasoning, and can handle a large number of pattern-matching tasks. But their reasoning is probabilistic in nature, which leads to two problems: first, in critical scenarios they may generate content that seems plausible but is actually wrong; second, different phrasings of the same question may yield contradictory answers.
In commercial services such as website building, this means AI can generate fluent copy, but it is difficult for it to make strategic judgments based on an understanding of the business model; it can apply design specifications, but it is difficult for it to independently make interaction decisions aimed at improving conversion. What it gives is an answer that “looks right,” not a solution that “actually works.”
Agents: probabilistic process orchestration. Agents use models as their engine and execute tasks according to preset workflows. Unlike traditional programs, they are probabilistic—the same input does not necessarily produce the same output. Flexibility increases, and reliability decreases accordingly. This determines that they are suitable for low-risk tasks whose results can be manually verified, such as information summarization, draft generation, and brainstorming; they are not suitable for high-precision, high-reliability scenarios, such as financial reconciliation, automated deployment, and contract review.
The limits of memory. Current memory solutions mainly rely on conversation history caching and vector retrieval; in essence, this is “retrieval-based replay,” not true accumulation of experience. Human memory has the ability to forget, reconstruct, and transfer across scenarios, while AI memory at this stage tends to keep expanding, leading to degraded performance and higher costs in long conversations. Capabilities learned in one scenario usually cannot be directly transferred to another.
Cost constraints. The cost of using AI services follows the rule that “the longer the context, the more calls, the higher the cost.” Complex tasks are split into multiple calls and billed separately; each additional business scenario requires extra maintenance of prompts, memory banks, and test cases, and marginal costs rise rather than fall. So far, there is no commercial agent service plan on the market with a “fixed fee and unlimited usage”; this is determined by physical computing costs.
II. What Enterprises Can Do at This Stage
Can be adopted: copy drafts, email polishing, brainstorming, meeting minutes, preliminary design assistance. Such tasks have clear boundaries, verifiable output, and low failure costs.
Should be cautious: replacing human customer service teams, integrating with core business systems, and developing proprietary large models.
Criteria: whether the output can be manually verified; whether the impact of task failure on the enterprise is controllable; whether the data involves sensitive information.
III. What We Are Waiting For
Inference costs are falling. Over the past year, the inference costs of mainstream models have dropped noticeably, and some specialized small models can already run on end devices; optimization of attention mechanisms is also pushing the computational complexity of long-sequence processing from quadratic to linear. The cost curve may not continue to rise exponentially, but is gradually flattening. Capabilities that were unaffordable a year ago are becoming economically viable.
Agents are beginning to have planning capabilities. In research, agents are evolving from executing fixed workflows to autonomously decomposing tasks and dynamically selecting tools; through self-verification and multi-agent collaboration, probabilistic behavior is being gradually converged. The scope of application is also expanding from low-risk tasks to medium-risk business.
Memory systems may see a breakthrough. Long-term memory technology is advancing, and the concept of tiered storage—distinguishing working memory, long-term memory, and reflective summaries—has been initially realized in experimental environments. If this path continues, AI will improve markedly in scenario adaptability and cross-task generalization.
The compounding effect of laying the groundwork early. Technological change often has a “crossing the chasm” phase. The purpose of early investment is not to obtain immediate returns, but to accumulate data, cultivate organizational capabilities, and complete process adaptation. If an enterprise starts building an internal knowledge base now and tries AI assistance in non-core scenarios, half a year later it will at least have: structured enterprise knowledge assets, tuning experience for its own business, employees’ collaboration habits, and a clear judgment of automation feasibility.
IV. Huali Cloud’s Practice
In our own business, AI has been applied in several evaluated scenarios: during customer website building, it assists in generating industry-specific content frameworks and first drafts of copy, while final decisions and review are still completed by humans; internally, we use a knowledge base together with AI tools to improve the efficiency of finding technical documentation and case solutions; and we are testing a collaboration model of “AI first draft + deep human adjustment,” exploring efficiency optimization while ensuring delivery quality.
We remain actively attentive to technology, but insist on advancing applications in a controllable and evaluable way. We do not chase concepts, nor do we avoid change.
Conclusion
Back to the original question. Our judgment is: at this stage, AI is closer to a tool with limited functions but significant efficiency when used properly; in the future, it may grow into a more complete portfolio of solutions. We have seen the boundaries of its capabilities, and we have also seen the direction of its evolution. No matter how technology changes, the subject of decision-making and judgment should still be humans.