At a time when AI products are emerging one after another, why can some products quickly gain the favor of users, while others are difficult to break through? This article delves into a key metric – User Confidence in AI Outcomes (CAIR) and sheds light on how it can be a hidden variable that can make or break AI products.
Have you ever wondered why some AI products are wildly sought after by users as soon as they are launched, while others that seem to be more technologically advanced have received mediocre responses in the market? Why are AI code editors like Cursor taking the developer community by storm, while many more powerful AI tools have failed to gain user trust?
After reading the latest article from the founder of LangChain over the weekend, he summed up his experience building AI products over the past decade and observed hundreds of product launches in the industry, and found a surprising pattern: the success or failure of a product has almost nothing to do with model accuracy or technical complexity. The real difference lies in a metric we call “CAIR” – user confidence in AI results. This psychological factor is not only measurable, but also predicted and optimized. It’s not a vanity metric, but a hidden variable that determines the success or failure of your AI product, no matter how impressive your underlying technology is.
He found that the biggest obstacle to user adoption of AI products is actually fear. To get the most adoption, you need to lower your fear and boost your confidence. We need a confidence metric that can be optimized, and that’s where CAIR comes into play. CAIR measures user confidence through a simple relationship that balances the value users gain with the psychological barriers they face.
The formula is: CAIR = Value ÷ (Risk × Correction Cost).
This equation makes intuitive sense: value is the benefit users receive when AI succeeds, risk is the consequences of AI mistakes, and cost of correction is the effort required to fix AI errors. When CAIR is high, users enthusiastically embrace AI capabilities. When CAIR is low, adoption stalls, no matter how technically impressive your AI is. The following is an analysis I wrote based on the concept of his article, combined with my own understanding and thinking, hoping to bring some inspiration to everyone.
Understand the core logic of CAIR indicators
Let me explain each component of the CAIR metric in detail, as a proper understanding of these variables is key to optimizing the AI product experience. The first is value, which is the actual benefit that users receive when the AI successfully performs a task. This includes not only time savings, but also reduced cognitive load, improved work quality, and the ability to complete tasks that would otherwise be impossible. Value needs to be measured from the user’s point of view, not from a technical point of view. For example, the value of an AI that can generate code is not how advanced algorithms it uses, but how many hours of programming time it saves developers and how much repetitive work it reduces.
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Risk is the negative consequences that can arise when AI makes mistakes. The key here is to understand that risk is not just about objective losses, but more importantly, about the subjective perception of potential losses by users. The same error can create completely different risk perceptions in different scenarios. For example, the risk of generating error code in a personal project is relatively low, but the same error in a production environment can cause a system crash, affecting tens of millions of users. Risks also include various potential consequences such as reputational damage, data breaches, compliance issues, and more. Importantly, user perception of risk is often more important than actual risk, as perception drives behavior.
The cost of correction refers to the effort required by users to fix AI errors to a satisfactory state once they find them. This includes the timing of identifying errors, the difficulty of understanding the cause of errors, the complexity of actual fixes, and the process of verifying that the fixes are correct. Corrective costs include not only time but also cognitive load and emotional costs. If users need to deeply understand complex AI outputs to identify errors or redo work from scratch, the cost of correction is high. Conversely, if users can easily identify the problem and fix it quickly, the cost of correction will be low.
The genius of the CAIR metric lies in its combination of these three variables to form a framework that can directly guide product design. What’s more, CAIR is primarily determined by product design decisions, not just the underlying AI capabilities. Of course, the probability of success is important – if your AI fails most of the time, no matter how clever the product design is, it won’t save you. The probability of success is built into the value component of our equation. When AI fails, users get zero value, which naturally lowers CAIR. But we’re focused on variables that you can actually control as a product team. Model accuracy improves over time as AI vendors release better models, but what about product design decisions that determine risk and corrective efforts? These are all in your control, and it’s they that separate successful AI products that use the same underlying technology from those that fail.
Why Cursor has conquered the world of programming
Let’s dive into the importance of CAIR with Cursor, the AI-powered code editor that has taken the developer world by storm. In theory, code generation should generate extremely low user confidence – bad code could crash systems serving millions of users, causing huge financial losses and reputational damage. In financial transaction systems, medical device controls, or the core services of large Internet platforms, a single line of code can lead to catastrophic consequences. However, Cursor has exploded and become a darling of the developer community as it cleverly designs an experience with confidence close to maximum.
Let’s apply the CAIR equation to Cursor in detail with simple high/medium/low grades. Look at the risk first: Cursor’s risk is actually low because the code is generated in a secure local development environment and never touches the production system directly. Users can view, test, and validate code in a controlled environment before deciding whether to deploy it to production. This design fundamentally eliminates the greatest source of fear – the possibility of direct damage to critical systems. Even if the AI generates the code in question, it will only exist on the developer’s local machine and will not affect any actual users or business operations.
Look at the cost of correction: Cursor’s correction cost is also low because fixing AI suggestions is extremely simple – you just need to delete the suggestions and write the code yourself, or simply ignore the suggestions and continue your original work. There are no complex rollback processes, no complex state changes to understand, and no cascading effects to track. If the code suggestion is bad, developers can immediately identify and discard it, as simple as ignoring a bad Google search result. This low-friction correction mechanism allows users to feel in full control of the entire process.
Finally, the value: The value provided by Cursor is high as it saves developers a lot of coding time and mental effort. It not only generates boilerplate code but also understands context and provides intelligent suggestions, helping developers realize their ideas faster. For those programming tasks that are highly repetitive and uncreative, Cursor can significantly improve efficiency. What’s more, it helps developers learn new programming patterns and best practices, providing educational value.
So Cursor’s CAIR calculation is: CAIR = high ÷ (low × low) = very high. That’s where the genius of Cursor product design comes in. Instead of trying to create a fully autonomous programming AI, they created an intelligent assistant that enhances human capabilities while maintaining ultimate human control.
Now imagine if Cursor’s design team had made a different choice. Let’s say they design a product and have the generated code automatically submitted to the production system or automatically pushed to the code repository. The risk immediately spikes to high levels because the error code can directly affect the production environment. But if they offer both strong version control and simple rollback capabilities, the cost of correction can still be kept low to moderate. In this case, while the risk is high, the CAIR can still be maintained at a reasonable level due to the relative ease of recovery.
However, if the system is both automated and lacks simple rollback capabilities, both the risk and the cost of correction become high, completely changing the equation: CAIR for alternative designs = high ÷ (high × high) = low. This sharp decline in confidence is likely to stifle user adoption, even if the underlying AI models are of exactly the same quality. This thought experiment clearly illustrates why CAIR is fundamentally about product experience design, not just technical capabilities, and why the separation of risk and corrective efforts allows for more nuanced and strategic product design decisions.
This design philosophy doesn’t just apply to code generation tools. Creative writing tools like Jasper also follow the same CAIR optimization model. They position the AI as a collaborative partner rather than an autonomous creator, maintaining a high level of CAIR by ensuring that users maintain editorial control over the final output. Users can accept, modify, or ignore the AI’s suggestions altogether, maintaining control over the creative process at all times.
90/10 UX Design Principles: Build a two-tier architecture for user confidence
In delving into CAIR optimization strategies, I discovered a design principle known in the industry as the “90/10 UX Rule,” which perfectly encapsulates how CAIR thinking can be applied in practice. This rule argues that we are still in the stone age of AI product design, where everyone is focused on improving model accuracy, but few teams have truly cracked the secrets of how to build products around imperfect AI. Even 95% accurate models still fail 1 in 20 times – and this failure is the key to breaking user trust.
The right design strategy is not to build one, but two. The first is the basic user experience for 0→90% of scenarios. The first goal is to help users get satisfactory output quickly. This part should feel like magic – predictable, fast, and designed around instant interactions. It’s like a static user experience with intelligent automation. The user inputs something, the AI runs, and it hits accurately 90% of the time—or close enough. This is your “default process”, clean, fast, and invisible.
The second is a contextual refinement interface for the last 10% of scenarios. Now comes the hard part: the inevitable mistakes. This is where most products fail – either hide complexity or throw all complexity at the user. Instead, the fix feature must be present at the exact moment and place the user needs it. Refinement tools must be contextual, lightweight, and feel like a native extension of user intent. For example, inline suggestions, a regenerate button that pops up at the right time, a local preview, and an undo function as a first-class citizen.
When you know that AI will fail in certain situations (because it always will), the goal is not to avoid errors – but to absorb them with a great user experience. Cursor is a master in this regard: fast output in 90% of cases, instant fallback in 10% of cases. This approach works because it aligns with the way humans build trust in automation. It’s not about perfection, it’s about making imperfections feel safe and recoverable. In other words: maximize value, minimize perceived risk and corrective costs.
This is exactly what the CAIR framework describes. It’s not just about accuracy—it’s about confidence—and confidence is a matter of user experience. The best AI products are not about avoiding failure – it’s about designing around failure. The key to this two-tier design strategy is that it recognizes that user expectations for AI are dynamic. In the initial interaction, users expect fast, high-quality results. But when things go wrong, what they need is a sense of control and the ability to recover quickly. By separating these two requirements into different interface layers, products can meet both efficiency and safety requirements.
Moderate CAIR Opportunities: An In-Depth Analysis of Monday AI Cases
Monday.com provides us with an extremely valuable case study on the moderate confidence level, showing how the CAIR framework identifies specific product improvement opportunities. Monday.com’s AI Blocks feature creates automated rules and deploys changes directly, but these modifications take effect immediately on the user’s Monday task panel. The problem is that these panels often contain critical workflow data connected to real business operations, making every AI decision have real-world impact.
Let me break down each CAIR variable for Monday AI in detail. The risk level is medium because the Monday panel is the “production data” that teams rely on for their day-to-day operations, often interconnected with other workflows, external systems, and business processes across the organization. A bad automation rule can trigger a cascading effect: it can spread misinformation between departments, send inaccurate project updates to clients, automatically assign the wrong tasks to team members, or disrupt a well-designed project timeline. The impact of these errors extends beyond the Monday platform itself but can extend to customer relationships, project delivery, and team collaboration.
The cost of correction is also moderate, as the interconnected nature of the Monday panel complicates bug fixes. When an error occurs in AI automation, users cannot simply press the “undo” button to resolve the issue. They need to do detective work to identify everything that has been modified, understand the impact of those changes on downstream processes, and then manually reverse the automated changes across multiple interconnected workflows. This process can take several hours, especially if the error affects multiple projects or teams. Users also need to notify affected colleagues, reset broken processes, and verify that all relevant systems have returned to normal.
However, the value component is high, which makes Monday AI still attractive. AI can automate tedious manual workflow management tasks that often consume a significant amount of team time. It can automatically update project status, trigger follow-ups based on project phases, assign tasks to appropriate team members, generate progress reports, and maintain project timelines. For teams managing complex projects, these automation features can save dozens of hours of routine time each week, freeing up team members to focus on more valuable creative and strategic work.
So Monday AI’s CAIR calculation is: CAIR = High ÷ (Medium × Medium) = Medium. This moderate level of CAIR creates interesting patterns of user behavior. We’ve seen users show adoption hesitancy, especially those managing critical workflows. The psychological barrier is not doubt about the AI’s capabilities, but rather the user must submit changes without being completely sure of the outcome. They know that AI may help them, but they also know that if something goes wrong, the repair process can be cumbersome.
The beauty of this case is that the CAIR framework provides a clear path for product improvement. The most obvious solution is to add a preview interface that allows users to evaluate AI-suggested modifications before they take effect. This single design change will significantly reduce risk from medium to low by separating the mental model of “testing” and “deploying”. Users can safely explore AI suggestions, understand their impact, and even test them in a sandbox environment before deciding whether to apply them in real-world workflows.
Further improvements may include the ability to roll out in stages, allowing users to test automations in a single project or team before scaling up to a larger scale. You can also add detailed changelogs and one-click rollback capabilities, significantly reducing corrective costs. These improvements elevate Monday AI from a medium CAIR to a high CAIR, potentially seeing a significant increase in adoption.
High-risk areas: When AI encounters math and money
The financial services and healthcare sectors provide us with a profound case study on the fundamental limitations of AI, particularly in mathematical and numerical reasoning. The challenges in these areas are not only the consequences of mistakes, but also the fundamental problem is the ability gap of AI in these areas. Large language models are inherently unreliable in mathematical calculations, numerical analysis, and precise reasoning, making fields such as accounting, tax preparation, investment advice, and medical diagnostics extremely challenging and difficult to fully overcome, no matter how cleverly you design the user experience.
Let me explain this in detail with AI tax preparation software. The core challenge is not only that tax errors can lead to serious consequences (IRS audits, fines, legal issues), but also that large language models simply cannot reliably perform the complex mathematical calculations and application of rules required for tax preparation. Tax calculations involve precise numerical manipulation, complex conditional logic, and an accurate understanding of changing tax laws. When AI is unreliable in these basic capabilities, even the best product design cannot create enough user confidence.
If such a system were designed to automatically file tax returns, CAIR would be catastrophically low: CAIR for automatic tax filing AI = high ÷ (high× high) = very low. The high risk comes from the serious consequences of tax errors, and the high cost of correction comes from the complexity and time cost of fixing submission errors. Even if the value is high (saving a lot of time and professional expenses), a CAIR of this design will still put off most users.
TurboTax’s successful strategy provides a great case for how CAIR thinking can be applied in high-risk areas. Instead of trying to get AI to do tax preparation independently, they recognized the fundamental limitations of AI and designed the product experience around these limitations. TurboTax doesn’t just provide the ultimate expert review, but establishes multiple layers of human oversight throughout the process: guided workflows ensure all critical data is entered by humans, AI provides optimization suggestions and error detection, but humans need to review and approve every important step. In this approach, AI enhances human tax preparation rather than trying to replace human judgment in numerical calculations.
The world of investment trading faces similar challenges. Automated investment traders, who need to deal with both mathematical complexity and significant financial risk, need to deal with both mathematical complexity and significant financial risk. If designed as a fully autonomous system, AI executing real trades creates paralyzing low confidence: autonomous trading CAIR = high ÷ (high × high) = very low. Even small mistakes can lead to significant financial losses, and fixing erroneous trades can be costly, especially in fast-moving markets.
Successful platforms such as Wealthfront achieve more reasonable CAIR levels through clever product positioning. They limit AI’s role to pattern recognition and trend analysis – areas where AI really excels, while keeping humans in control of all numerical decisions and actual trade execution. AI can identify market patterns, analyze historical trends, and provide investment advice, but final trading decisions and execution still require human confirmation. This division of labor leverages AI’s strengths while avoiding its weaknesses in numerical accuracy.
The healthcare sector shows the same patterns and challenges. AI diagnostic tools exhibit significant limitations in handling tasks that require numerical precision, such as drug dosage calculations, risk assessment scores, and interpretation of laboratory results. These tasks require not only pattern recognition but also precise mathematical calculations and an accurate understanding of numerical relationships. Successful medical AI tools, such as ECG analysis systems, achieve reasonable levels of CAIR by positioning AI as a pattern detection assistant. These tools excel in identifying abnormal patterns in ECG waveforms, but leave numerical analysis and final diagnosis to medical professionals.
The lessons learned from these high-risk areas are clear: the strategy for success is not to wait for AI to become more accurate, but to design around its current limitations while making the most of what it really excels at. This approach creates sustainable competitive advantages that are more difficult to replicate than pure technological improvements because they are based on a deep understanding of human psychology, risk perception, and trust-building processes.
Five strategic principles for CAIR optimization
After analyzing hundreds of successful and failed AI products, I identified five reliable and proven CAIR optimization strategies. These are not theoretical frameworks, but practical principles that have consistently shown results across different industries and use cases. Each principle directly influences one or more components of the CAIR equation, providing product teams with specific directions for improvement.
The first principle is that strategic people design in a loop, which optimizes all three variables of the CAIR equation at the same time. Many teams mistakenly believe that “autonomy” equates to “better,” but this mindset significantly increases risk in most real-world use cases. The key is to add human oversight, but it must be strategic. Adding human intervention at every decision point seriously harms value because it interrupts workflows and reduces efficiency. Instead, placing human supervision at critical decision points maximizes CAIR. For example, asking users to approve each AI suggestion undermines productivity, but requiring confirmation before irreversibly important actions maintains both security and utility. The art of design lies in identifying where human supervision can provide the greatest CAIR optimization at the cost of minimal value dilution.
The second principle is reversibility design, specifically designed to reduce the cost of correction. When users know they can easily undo any action performed by AI, the mental and practical effort required to fix the error drops dramatically. Clarifying the psychological security of the “escape channel” can transform users’ anxiety into confidence. In our analysis, we consistently saw the effect of doubling product adoption simply by adding prominent and easy-to-use revocation features. The key is to make the undo feature not only technically feasible but also psychologically significant. Users need to know exactly how to exit when they start using AI features, and this visible safety net significantly reduces their resistance to use.
The third principle is consequence isolation, which is mainly used to reduce risk perception. Creating a safe space for AI experimentation with sandbox environments, preview mode, and draft capabilities, this design effectively separates the mental model of “exploration” and “execution,” essentially eliminating users’ fear of unintended consequences when experimenting with AI features. Our data shows that products that offer sandbox environments consistently show 3-4x higher feature adoption. Users are more willing to explore the boundaries of AI’s capabilities when they know they can safely “play” and experiment, which ultimately leads to deeper product usage and higher user satisfaction.
The fourth principle is transparency design, which reduces perceived risk and remediation costs at the same time. When users understand why AI makes specific decisions, they can better assess the reliability of AI outputs (reducing perceived risk) and more effectively identify specific issues that need to be fixed (reduced corrective efforts). The explanatory feature significantly increases reuse, as users can correct specific flaws in AI reasoning instead of abandoning the AI output entirely and starting over. Transparency also helps build long-term trust, with users gradually establishing more accurate expectations about their capabilities by understanding how AI works.
The fifth principle is to control gradient design, which adds value while managing risk. Allows users to calibrate CAIR based on individual comfort levels, starting with low-risk features and gradually unlocking higher-value, but also higher-risk, abilities as confidence builds. This approach acknowledges that different users have varying risk tolerances and creates a natural path to skill and confidence building. Users can learn and adapt to AI features at a pace they feel comfortable with, rather than being forced to face all the complexities at once.
The true power of these five principles lies in their synergy. When multiple principles are applied simultaneously, the effects are not just additive, but multiplied. For example, combining transparency with reversibility not only reduces users’ perception of risk, but also gives them more confidence to try AI suggestions, as they understand the AI’s reasoning process and know that they can easily undo if the results are not satisfactory. Similarly, the combination of consequence isolation and control gradients creates a learning environment where users can progressively build understanding and trust in AI capabilities in a safe space.
Reframe the mindset of AI product development
The CAIR framework fundamentally changes the way we assess AI product readiness, representing a shift in mindset from technology-driven to user-driven. Traditional AI product development focuses on one question: “Is AI accurate enough?” But the CAIR framework requires us to ask another equally important question at the same time: “Is user confidence in AI high enough to achieve widespread adoption?” This dual perspective shifts the conversation from purely technical metrics to a comprehensive perspective that balances technical capabilities and user experience.
Under the CAIR framework, the most important product question becomes: How easy is it for users to correct AI errors? How serious are the possible consequences of AI errors? How much real value does a successful AI output provide to users? How much control do humans retain at critical decision-making moments? Are the limitations and uncertainties of AI clearly communicated to users? The answers to these questions directly determine the CAIR score of the product and, in turn, the likelihood of user adoption.
For product leaders, this insight is liberating. You don’t need to wait for the perfect AI technology to create a successful product. An 85% accuracy AI running in a high CAIR design will consistently outperform a 95% accuracy AI running in a low CAIR design in terms of user adoption and satisfaction. This understanding shifts control of product success from the AI R&D team to the product design team. It means that even with the same underlying AI technology, you can still create a user experience that is significantly better than your competitors with better product design decisions.
For organizations, the CAIR framework requires that AI projects must be led by both product and AI technology teams, and product design decisions should be considered as important success factors as model tuning. Traditional AI project evaluation mainly focuses on technical indicators: model accuracy, inference speed, training cost, etc. However, the CAIR framework requires AI readiness assessments to include user confidence calculations, not just technical performance assessments. This means that UX design needs to be considered during the project planning phase, rather than how to package the product after technology development is complete.
I firmly believe that companies that achieve long-term success in AI are not necessarily those with state-of-the-art AI models, but those that have a deep understanding of how to engineer user confidence and maximize CAIR through thoughtful product design. This capability is more difficult to replicate than purely technical advantages because it requires a deep understanding of user psychology, risk perception, trust-building processes, and product design. Technical advantages may disappear with the release of new models, but the competitive advantage created by good product design is more durable.
This shift in thinking has also changed our approach to AI product iteration. Traditional approaches may focus on improving model accuracy to improve products, but the CAIR framework shows that the same or even better improvements may come from design changes that reduce risk perception or reduce the cost of correction. This provides product teams with more avenues to improve their products and no longer rely solely on AI advancements.
From a practical perspective, I recommend that the product team start here: start by calculating the CAIR score for your current AI capabilities. Using a simple rating system of high/medium/low, evaluate the value, risk, and cost of correction for each feature. Identify touchpoints with the lowest CAIR scores, which are often the biggest bottlenecks for user adoption. Choose a specific low CAIR feature and apply one of the five optimization principles we discussed. Measure changes in adoption rates after implementing improvements. You’ll most likely see significant differences right away, and more importantly, you’ll build a repeatable framework that will guide every subsequent AI product decision.
The ultimate question is not whether your AI technology is good enough, but whether your users are confident enough in it to discover its value. This shift in mindset from technology-centric to user-centric is the key factor that separates successful AI products from failed ones.
From theory to practice: CAIR application patterns in different industries
Through an in-depth analysis of successful AI products across various industries, I discovered an interesting map of CAIR application patterns. Different industries and product types exhibit significantly different CAIR profiles, which provide a valuable reference framework for product designers. Understanding these patterns can help teams better anticipate user reactions and choose the optimization strategies that best suit their specific scenarios.
In the world of code and creative tools, we see a consistent pattern of high CAIR. Both programming tools like Cursor and writing assistants like Jasper exhibit high value, low risk, and low cost of correction. The secret to the success of these tools is that they create an environment where “trial and error costs are extremely low.” Users can easily generate, evaluate, modify, or discard AI outputs, and the entire process feels more like collaborating with a very fast assistant rather than delegating important decisions to an uncontrollable system.
Workflow and project management tools, such as Monday AI, often exhibit a moderate CAIR pattern. These tools offer high value, but since they operate with important business data shared by teams, the risk and cost of correction are at a moderate level. The opportunity for improvement in this area is particularly high, as relatively simple design changes, such as adding preview features or improving undo mechanisms, can significantly boost CAIR and drive adoption.
The financial and medical sectors represent CAIR’s biggest challenges, which are inherently subject to high risks and high costs of correction. But with clever product positioning and feature limitations, some products still achieve acceptable CAIR levels. Successful strategies often involve limiting AI to tasks it is truly good at (like pattern recognition) while letting humans handle high-risk numerical decisions and eventual execution.
Interestingly, I found a strong correlation between CAIR patterns and user adoption velocity. High-CAIR products typically experience rapid organic growth and strong user referrals, while low-CAIR products often face slow adoption and high churn rates even with excellent marketing and strong technology. This pattern is consistent across cultures and geographic regions, suggesting that CAIR reflects a universal human psychological need for risk and control.
Of particular note is that CAIR optimization is often easier to achieve quick wins than technical improvements. We see many cases where product teams increase adoption by 2-3x in a matter of weeks with simple design changes, while the same level of technical improvement can take months of R&D work. This makes CAIR a powerful lever that product teams can directly control and optimize.
The Future of CAIR: Redefining the Standards for AI Product Success
I believe CAIR will gradually become a fundamental metric for AI product development, just as conversion rate is as important to e-commerce websites or Net Promoter Score to customer satisfaction. As more companies recognize that user confidence is more important than technical accuracy, we will see a fundamental shift in evaluation standards across the industry.
This shift is already beginning to be felt in some forward-thinking companies. Instead of just pursuing marginal improvements in model performance, they reallocate resources to CAIR optimization. This includes investing in better user experience design, more robust undo and preview capabilities, clearer communication of AI capability boundaries, and smarter human-robot collaboration workflows.
From an investment and business perspective, I predict that companies that adopt CAIR thinking early will gain a significant advantage in the upcoming competition for AI products. They will build AI products that users truly trust and use frequently, not just tech demos or marketing gimmicks. This difference will be clearly reflected in user retention, referral rates, and long-term business value.
For AI practitioners, mastering the CAIR framework will become a core skill. Future product managers need to be able to quickly assess the CAIR potential of AI capabilities, designers need to know how to create high-confidence user experiences, and engineers need to understand how technical decisions affect user confidence. This interdisciplinary collaboration will be a hallmark of a successful AI product team.
Ultimately, the CAIR framework reminds us that the best technology does not necessarily win the market, but the most trustworthy technology. In the era of rapid AI development, products that can balance innovation and trust, capability and controllability will be the real winners. It’s not just about business success, but also about whether AI technology can truly deliver on its promise of improving human lives.
As we stand at the starting point of the era of AI products, CAIR provides us with a powerful compass pointing towards a future where not only is technology advanced but also users are truly willing to embrace. This is the real difference between the success and failure of AI products, and it is also the key to building a better AI world.
