21 May, 2025 Clare Dowdy finds out about the excitement, and challenges, that come with working with an early-stage company. “Culturally, working with founders is intense, in the best possible way,” says Kelly Mackenzie, founder and creative director of White Bear. The London and Dublin based branding agency has form working with founder-led companies, including Tom Parker Creamery and chocolate brand Luvli. “The business isn’t just what they do, it’s often wrapped up in their identity, sense of self-worth and purpose,” Mackenzie explains. And because of this intensity, the agency becomes almost as invested as the client. “When we’re asked to evolve or build their brand, we often tell them that it’s like being asked to mind their child,” Mackenzie says. “Naming that emotional connection early builds trust. It helps them feel safe in a process they’ve often never experienced before.” Many designers talk of going on a journey with these clients. “You have a very close relationship with the founders, and get to know them very deeply,” says Hijinks co-founder Marc Allenby. “Their idea is usually based on passion, and you – as a designer – are fuelled by that passion. That energy is self-motivating, you really care about what you’re creating.” The WeRepresent logo and wordmark designed by Hijinks When Hijinks presented the founder of talent agency WeRepresent with their logo, she burst into tears, which isn’t standard practice when presenting to bigger clients. But Hijinks had created an animated version that “breathed” – a nod to the founder’s traumatic experience of being in a coma on a ventilator with Covid. A moving approach, in more ways than one. The entrepreneurial spirit found in start-ups can be infectious. “Rather than being jaded, they have a youthful energy, and that attracts us,” says Russell Potter, the co-founder of architecture and design firm SODA, whose many hospitality start-up clients include the Instagram-beloved crumble shop, Humble Crumble. Then there’s the potential for creative freedom. “It’s a blank canvas. We’re creating something from nothing,” says Allenby at Hijinks, in contrast to a more mature brand that will come with its own baggage. But these clients may not have worked with a design studio before process, and inevitably there’s a lot of hand-holding. Dundee-based Agency of None branded the start-up QuickBlock “Start-ups by their nature, are often a very small group of people, all trying to cover many roles. So the role of the designer is often as an educator, as much as a designer,” says Lyall Bruce, director of Dundee-based Agency of None, whose start-up clients include QuickBlock, a sustainable building block made from recycled food packaging, and coffee roaster Bryte. As a consequence of this inexperience, the brief is rarely formal. It might be a loose deck, a stream-of-consciousness call, or a rough vision, according to Mackenzie. “And throughout, there will be extra calls to talk through thinking, being available on WhatsApp or Slack, and giving reassurance at each step.” That naivety is both beautiful and brilliant, says Potter at SODA. But if you’re not careful, you can get dragged into a lot of business decisions. “We’re often asked to comment above our pay grade – we can’t always have the answers,” he says. “Someone client side has to have a leap of faith and make a decision.” Inevitably budgets are tight, and agencies often need to explain the value of effective design, “Once they see the link between strong branding and commercial outcomes, budget conversations become much easier,” says Mackenzie at White Bear. Although, as several designers pointed out, this challenge is not unique to start-up clients.But for start-ups, agencies often break down payment into smaller chunks, as a way of protecting themselves. The interior of the new Crunch sandwich shop in London’s Soho designed by KIDZ KIDZ, which has offices in Amsterdam, Belgrade, Dubai and Paris, designed the new Crunch sandwich shop in London’s Soho. “Working with early-stage companies inevitably involves risk — timelines can shift, priorities may change, or funding may fall through,” says KIDZ co-founder Dmitrii Mironov. “To protect our team and ensure a smooth process, we break the work into smaller, clearly defined stages. We require prepayment for each stage; keep written records of all agreements, even when communication is fast and informal; limit the number of revisions and fix the scope of work for each stage; and withhold certain deliverables until full payment is received.” They’ve had a few cases where a project wasn’t completed because the client pivoted or changed direction unexpectedly. “While that’s never ideal, it’s part of the reality of working with start-ups,” he adds. And sometimes it makes sense to rethink payment completely. In lieu of fees from a business consultancy, Hijinks did a skills swap. Meanwhile, when Run for the Hills designed a third site for restaurant chain Cricket in London’s White City, they threw in a £5,000 bar bill to make up for the smaller fee. That allowed the agency to take the team out, thereby boosting morale, and host clients, thereby showcasing their work. The interior of one of the Humble Crumble shops, designed by SODA studio In 2015, SODA had a start-up client in the hospitality sector who offered to pay part of the fee in Bitcoin. “We ummed and ahhed, but decided to take the £19,000 in money,” Potter says. Some years later, it would have been worth over £1 million, though Potter points out that they would have sold it before then. Then there’s the gamble of a profit share, where you’re investing in their business in lieu of partial payment. At a former agency, product designer Jake Weir occasionally ended up doing sweat equity to help out, “so you’re basically partners.” When a hairdresser with limited funding came to him wanting to develop a new hair curler, the agency was given shares in the company for their design input. “We were incentivised to make it work,” Weir says. The product was a success – ultimately sold to BaByliss for “millions.” But even when budgets are low, these jobs are still worth doing sometimes. “We’ll do them as a passion project as they’re quick turnaround and they give younger guys in the studio more on-site experience,” Potter says. What happens when the client’s dream is never going to make it? MAP Project Office was once asked to design a very specific backpack. “We wondered if there was a market for this,” says MAP’s creative director, Weir. When people are pouring their life savings into a project, there’s a responsibility to warn them of the risks. Regardless, founders often have their mind set on these things. In these circumstances, MAP will look for a way to “dial the founders’ single-mindedness down,” Weirs says. “If you relax the concept a little bit, you can make it less niche and more accessible, especially for a first product.” White Bear’s work with the Tom Parker Creamery brand And experienced designers in this sector get good at spotting the jobs to avoid. Start-ups have a reputation for being short-lived. It’s commonly said that 90% of them fail, although the source for this stat is not at all clear. Harvard Business Review puts it more modestly, claiming that more than two-thirds of them never deliver a positive return to investors. The food and beverage sector, in particular, is full of such tragedies, according to The Grocer. But these potential risks shouldn’t be a reason not to take on a start-up. “The reason the project fails is not because of the design,” says Trotman at Run for the Hills, “unless the client has shittified it.” A fish restaurant that Run for the Hills worked on in London had great interiors and a cool brand, Trotman says. “But it failed on the food, and we can’t do anything about the food.” Conversely, when they do well, the agency is part of that success story. In 2005, Big Fish named and branded start-up chocolate puddings company Gü, cleverly persuading its founder to ditch his name, The Belgian Chocolate Company. Just seven later, it was sold for £32.5m. And because the agency is so embedded – it’s personal, remember – the work takes on real significance. “You really get the chance to make a lasting impact and build a long-term working relationship,” says Bruce at Agency of None. And better still for the broader industry. “The experience they have here will set up the relationship with design forever.” Design disciplines in this article Brands in this article What to read next More human resources – designers brand new workplace apps Brand Identity 15 Apr, 2025

Salesforce is willing to give the US government access to Slack for a 90% discount, with a near-70% discount on the AI add-on.

When you purchase through links on our site, we may earn an affiliate commission. Here’s how it works. Microsoft adding ability to use multiple emoji reactions in Microsoft Teams Aman Kumar Neowin @kraman19999 · May 20, 2025 11:16 EDT After Microsoft retired Skype earlier this month, Microsoft Teams has emerged as its go-to alternative, offering many exciting features such as real-time collaboration, communication, file sharing, and more. Interestingly, Microsoft has a feedback portal where users can provide feedback on different Microsoft products, including Teams, as well as request new features. One feature that users have been requesting for a long time in Microsoft Teams is the ability to react to a message with multiple emojis. It seems that Microsoft has finally listened to user’s requests and will soon introduce this feature to Microsoft Teams. TMicrosoft 365 roadmap page titled “Microsoft Teams: Multiple Emoji Reactions per Message.” According to the description, this feature will allow users to "express a wider range of emotions more effectively." This is certainly true, as you may have come across messages where you wished you could react with more than one emoji. The page also shows that the feature is currently in development, and Microsoft is planning to release its early preview by June 2025, followed by its general roll out in the same month. However, take this release date information with a pinch of salt, as the expected release date is not confirmed. It is possible that the feature could be delayed by a couple of months if Microsoft encounters any issues during development. That said, Microsoft Teams won't be the first platform to introduce the ability to react to a message with multiple emojis. Slack, a popular team communication and collaboration platform, has offered this feature for years. However, it will be interesting to see the maximum number of reactions Microsoft Teams will allow per message per person, considering that the platform has over 800 emoji reactions available. As a heads-up, Slack allows a maximum of 23 emoji reactions by one person on a single message. Tags Report a problem with article Follow @NeowinFeed

A shared language for product teams to build usable, intelligent and safe GenAI experiences beyond just the modelGenerative AI introduces a new way for humans to interact with systems by focusing on intent-based outcome specification. GenAI introduces novel challenges because its outputs are probabilistic, requires understanding of variability, memory, errors, hallucinations and malicious use which brings an essential need to build principles and design patterns as described by IBM.Moreover, any AI product is a layered system where LLM is just one ingredient and memory, orchestration, tool extensions, UX and agentic user-flows builds the real magic!This article is my research and documentation of evolving GenAI design patterns that provide a shared language for product managers, data scientists, and interaction designers to create products that are human-centred, trustworthy and safe. By applying these patterns, we can bridge the gap between user needs, technical capabilities and product development process.Here are 21 GenAI UX patternsGenAI or no GenAIConvert user needs to data needsAugment or automateDefine level of automationProgressive AI adoptionLeverage mental modelsConvey product limitsDisplay chain of thought (CoT)Leverage multiple outputsProvide data sourcesConvey model confidenceDesign for memory and recallProvide contextual input parametersDesign for coPilot, co-Editing or partial automationDefine user controls for AutomationDesign for user input error statesDesign for AI system error statesDesign to capture user feedbackDesign for model evaluationDesign for AI safety guardrailsCommunicate data privacy and controls1. GenAI or no GenAIEvaluate whether GenAI improves UX or introduces complexity. Often, heuristic-based (IF/Else) solutions are easier to build and maintain.Scenarios when GenAI is beneficialTasks that are open-ended, creative and augments user.E.g., writing prompts, summarizing notes, drafting replies.Creating or transforming complex outputs (e.g., images, video, code).E.g., converting a sketch into website code.Where structured UX fails to capture user intent.Scenarios when GenAI should be avoidedOutcomes that must be precise, auditable or deterministic. E.g., Tax forms or legal contracts.Users expect clear and consistent information.E.g. Open source software documentationHow to use this patternDetermine the friction points in the customer journeyAssess technology feasibility: Determine if AI can address the friction point. Evaluate scale, dataset availability, error risk assessment and economic ROI.Validate user expectations: - Determine if the AI solution erodes user expectations by evaluating whether the system augments human effort or replaces it entirely, as outlined in pattern 3, Augment vs. automate. - Determine if AI solution erodes pattern 6, Mental models2. Convert user needs to data needsThis pattern ensures GenAI development begins with user intent and data model required to achieve that. GenAI systems are only as good as the data they’re trained on. But real users don’t speak in rows and columns, they express goals, frustrations, and behaviours. If teams fail to translate user needs into structured, model-ready inputs, the resulting system or product may optimise for the wrong outcomes and thus user churn.How to use this patternCollaborate as a cross-functional team of PMs, Product designers and Data Scientists and align on user problems worth solving.Define user needs by using triangulated research: Qualitative (Market Reports, Surveys or Questionnaires) + Quantitative (User Interviews, Observational studies) + Emergent (Product reviews, Social listening etc.) and synthesising user insights using JTBD framework, Empathy Map to visualise user emotions and perspectives. Value Proposition Canvas to align user gains and pains with featuresDefine data needs and documentation by selecting a suitable data model, perform gap analysis and iteratively refine data model as needed. Once you understand the why, translate it into the what for the model. What features, labels, examples, and contexts will your AI model need to learn this behaviour? Use structured collaboration to figure out.3. Augment vs automateOne of the critical decisions in GenAI apps is whether to fully automate a task or to augment human capability. Use this pattern to to align with user intent and control preferences with the technology.Automation is best for tasks users prefer to delegate especially when they are tedious, time-consuming or unsafe. E.g., Intercom FinAI automatically summarizes long email threads into internal notes, saving time on repetitive, low-value tasks.Augmentation enhances tasks users want to remain involved in by increasing efficiency, increase creativity and control. E.g., Magenta Studio in Abelton support creative controls to manipulate and create new music.How to use this patternTo select the best approach, evaluate user needs and expectations using research synthesis tools like empathy map (visualise user emotions and perspectives) and value proposition canvas (to understand user gains and pains)Test and validate if the approach erodes user experience or enhances it.4. Define level of automationIn AI systems, automation refers to how much control is delegated to the AI vs user. This is a strategic UX pattern to decide degree of automation based upon user pain-point, context scenarios and expectation from the product.Levels of automationNo automation (AI assists but user decides)The AI system provides assistance and suggestions to the user but requires the user to make all the decisions. E.g., Grammarly highlights grammar issues but the user accepts or rejects corrections.Partial automation/ co-pilot/ co-editor (AI acts with user oversight)The AI initiates actions or generates content, but the user reviews or intervenes as needed. E.g., GitHub Copilot suggest code that developers can accept, modify, or ignore.Full automation (AI acts independently)The AI system performs tasks without user intervention, often based on predefined rules, tools and triggers. Full automation in GenAI are often referred to as Agentic systems. E.g., Ema can autonomously plan and execute multi-step tasks like researching competitors, generating a report and emailing it without user prompts or intervention at each step.How to use this patternEvaluate user pain point to be automated and risk involved: Automating tasks is most effective when the associated risk is low without severe consequences in case of failure. Low-risk tasks such as sending automated reminders, promotional emails, filtering spam emails or processing routine customer queries can be automated with minimal downside while saving time and resources. High-risk tasks such as making medical diagnoses, sending business-critical emails, or executing financial trades requires careful oversight due to the potential for significant harm if errors occur.Evaluate and design for particular automation level: Evaluate if user pain point should fall under — No Automation, Partial Automation or Full Automation based upon user expectations and goals.Define user controls for automation (refer pattern 15)5. Progressive GenAI adoptionWhen users first encounter a product built on new technology, they often wonder what the system can and can’t do, how it works and how they should interact with it.This pattern offers multi-dimensional strategy to help user onboard an AI product or feature, mitigate errors, aligns with user readiness to deliver an informed and human-centered UX.How to use this patternThis pattern is a culmination of many other patternsFocus on communicating benefits from the start: Avoid diving into details about the technology and highlight how the AI brings new value.Simplify the onboarding experience Let users experience the system’s value before asking data-sharing preferences, give instant access to basic AI features first. Encourage users to sign up later to unlock advanced AI features or share more details. E.g., Adobe FireFly progressively onboards user with basic to advance AI featuresDefine level of automation (refer pattern 4) and gradually increase autonomy or complexity.Provide explainability and trust by designing for errors (refer pattern 16 and 17).Communicate data privacy and controls (refer pattern 21) to clearly convey how user data is collected, stored, processed and protected.6. Leverage mental modelsMental models help user predict how a system (web, application or other kind of product) will work and, therefore, influence how they interact with an interface. When a product aligns with a user’s existing mental models, it feels intuitive and easy to adopt. When it clashes, it can cause frustration, confusion, or abandonment​.E.g. Github Copilot builds upon developers’ mental models from traditional code autocomplete, easing the transition to AI-powered code suggestionsE.g. Adobe Photoshop builds upon the familiar approach of extending an image using rectangular controls by integrating its Generative Fill feature, which intelligently fills the newly created space.How to use this patternIdentifying and build upon existing mental models by questioningWhat is the user journey and what is user trying to do?What mental models might already be in place?Does this product break any intuitive patterns of cause and effect?Are you breaking an existing mental model? If yes, clearly explain how and why. Good onboarding, microcopy, and visual cues can help bridge the gap.7. Convey product limitsThis pattern involves clearly conveying what an AI model can and cannot do, including its knowledge boundaries, capabilities and limitations.It is helpful to builds user trust, sets appropriate expectations, prevents misuse, and reduces frustration when the model fails or behaves unexpectedly.How to use this patternExplicitly state model limitations: Show contextual cues for outdated knowledge or lack of real-time data. E.g., Claude states its knowledge cutoff when the question falls outside its knowledge domainProvide fallbacks or escalation options when the model cannot provide a suitable output. E.g., Amazon Rufus when asked about something unrelated to shopping, says “it doesn’t have access to factual information and, I can only assists with shopping related questions and requests”Make limitations visible in product marketing, onboarding, tooltips or response disclaimers.8. Display chain of thought (CoT)In AI systems, chain-of-thought (CoT) prompting technique enhances the model’s ability to solve complex problems by mimicking a more structured, step-by-step thought process like that of a human.CoT display is a UX pattern that improves transparency by revealing how the AI arrived at its conclusions. This fosters user trust, supports interpretability, and opens up space for user feedback especially in high-stakes or ambiguous scenarios.E.g., Perplexity enhances transparency by displaying its processing steps helping users understand the thoughtful process behind the answers.E.g., Khanmigo an AI Tutoring system guides students step-by-step through problems, mimicking human reasoning to enhance understanding and learning.How to use this patternShow status like “researching” and “reasoning to communicate progress, reduce user uncertainty and wait times feel shorter.Use progressive disclosure: Start with a high-level summary, and allow users to expand details as needed.Provide AI tooling transparency: Clearly display external tools and data sources the AI uses to generate recommendations.Show confidence & uncertainty: Indicate AI confidence levels and highlight uncertainties when relevant.9. Leverage multiple outputsGenAI can produce varied responses to the same input due to its probabilistic nature. This pattern exploits variability by presenting multiple outputs side by side. Showing diverse options helps users creatively explore, compare, refine or make better decisions that best aligns with their intent. E.g., Google Gemini provides multiple options to help user explore, refine and make better decisions.How to use this patternExplain the purpose of variation: Help users understand that differences across outputs are intentional and meant to offer choice.Enable edits: Let users rate, select, remix, or edit outputs seamlessly to shape outcomes and provide feedback. E.g., Midjourney helps user adjust prompt and guide your variations and edits using remix10. Provide data sourcesArticulating data sources in a GenAI application is essential for transparency, credibility and user trust. Clearly indicating where the AI derives its knowledge helps users assess the reliability of responses and avoid misinformation.This is especially important in high stakes factual domains like healthcare, finance or legal guidance where decisions must be based on verified data.How to use this patternCite credible sources inline: Display sources as footnotes, tooltips, or collapsible links. E.g., NoteBookLM adds citations to its answers and links each answer directly to the part of user’s uploaded documents.Disclose training data scope clearly: For generative tools (text, images, code), offer a simple explanation of what data the model was trained on and what wasn’t included. E.g., Adobe Firefly discloses that its Generative Fill feature is trained on stock imagery, openly licensed work and public domain content where the copyright has expired.Provide source-level confidence:In cases where multiple sources contribute, visually differentiate higher-confidence or more authoritative sources.11. Convey model confidenceAI-generated outputs are probabilistic and can vary in accuracy. Showing confidence scores communicates how certain the model is about its output. This helps users assess reliability and make better-informed decisions.How to use this patternAssess context and decision stakes: Showing model confidence depends on the context and its impact on user decision-making. In high-stakes scenarios like healthcare, finance or legal advice, displaying confidence scores are crucial. However, in low stake scenarios like AI-generated art or storytelling confidence may not add much value and could even introduce unnecessary confusion.Choose the right visualization: If design research shows that displaying model confidence aids decision-making, the next step is to select the right visualization method. Percentages, progress bars or verbal qualifiers (“likely,” “uncertain”) can communicate confidence effectively. The apt visualisation method depends on the application’s use-case and user familiarity. E.g., Grammarly uses verbal qualifiers like “likely” to the content it generated along with the userGuide user action during low confidence scenarios: Offer paths forward such as asking clarifying questions or offering alternative options.12. Design for memory and recallMemory and recall is an important concept and design pattern that enables the AI product to store and reuse information from past interactions such as user preferences, feedback, goals or task history to improve continuity and context awareness.Enhances personalization by remembering past choices or preferencesReduces user burden by avoiding repeated input requests especially in multi-step or long-form tasksSupports complex tasks like longitudinal workflows like in project planning, learning journeys by referencing or building on past progress.Memory used to access information can be ephemeral (short-term within a session) or persistent (long-term across sessions) and may include conversational context, behavioural signals, or explicit inputs.How to use this patternDefine the user context and choose memory typeChoose memory type like ephemeral or persistent or both based upon use case. A shopping assistant might track interactions in real time without needing to persist data for future sessions whereas personal assistants need long-term memory for personalization.Use memory intelligently in user interactionsBuild base prompts for LLM to recall and communicate information contextually (E.g., “Last time you preferred a lighter tone. Should I continue with that?”).Communicate transparency and provide controlsClearly communicate what’s being saved and let users view, edit or delete stored memory. Make “delete memories” an accessible action. E.g. ChatGPT offers extensive controls across it’s platform to view, update, or delete memories anytime.13. Provide contextual input parametersContextual Input parameters enhance the user experience by streamlining user interactions and gets to user goal faster. By leveraging user-specific data, user preferences or past interactions or even data from other users who have similar preferences, GenAI system can tailor inputs and functionalities to better meet user intent and decision making.How to use this patternLeverage prior interactions: Pre-fill inputs based on what the user has previously entered. Refer pattern 12, Memory and recall.Use auto complete or smart defaults: As users type, offer intelligent, real-time suggestions derived from personal and global usage patterns. E.g., Perplexity offers smart next query suggestions based on your current query thread.Suggest interactive UI widgets: Based upon system prediction, provide tailored input widgets like toasts, sliders, checkboxes to enhance user input. E.g., ElevenLabs allows users to fine-tune voice generation settings by surfacing presets or defaults.14. Design for co-pilot / co-editing / partial automationCo-pilot is an augmentation pattern where AI acts as a collaborative assistant, offering contextual and data-driven insights while the user remains in control. This design pattern is essential in domains like strategy, ideating, writing, designing or coding where outcomes are subjective, users have unique preferences or creative input from the user is critical.Co-pilot speed up workflows, enhance creativity and reduce cognitive load but the human retains authorship and final decision-making.How to use this patternEmbed inline assistance: Place AI suggestions contextually so users can easily accept, reject or modify them. E.g., Notion AI helps you draft, summarise and edit content while you control the final version.Save user intent and creative direction: Let users guide the AI with input like goals, tone, or examples, maintaining authorship and creative direction. E.g., Jasper AI allows users to set brand voice and tone guidelines, helping structure AI output to better match the user’s intent.15. Design user controls for automationBuild UI-level mechanisms that let users manage or override automation based upon user goals, context scenarios or system failure states.No system can anticipate all user contexts. Controls give users agency and keep trust intact even when the AI gets it wrong.How to use this patternUse progressive disclosure: Start with minimal automation and allow users to opt into more complex or autonomous features over time. E.g., Canva Magic Studio starts with simple AI suggestions like text or image generation then gradually reveals advanced tools like Magic Write, AI video scenes and brand voice customisation.Give users automation controls: UI controls like toggles, sliders, or rule-based settings to let users choose when and how automation can be controlled. E.g., Gmail lets users disable Smart Compose.Design for automation error recovery: Give users correction when AI fails (false positives/negatives). Add manual override, undo, or escalate options to human support. E.g., GitHub Copilot suggests code inline, but developers can easily reject, modify or undo suggestions when output is off.16. Design for user input error statesGenAI systems often rely on interpreting human input. When users provide ambiguous, incomplete or erroneous information, the AI may misunderstand their intent or produce low-quality outputs.Input errors often reflect a mismatch between user expectations and system understanding. Addressing these gracefully is essential to maintain trust and ensure smooth interaction.How to use this patternHandle typos with grace: Use spell-checking or fuzzy matching to auto-correct common input errors when confidence is high (e.g., >80%), and subtly surface corrections (“Showing results for…”).Ask clarifying questions: When input is too vague or has multiple interpretations, prompt the user to provide missing context. In Conversation Design, these types of errors occur when the intent is defined but the entity is not clear. Know more about entity and intent. E.g., ChatGPT when given low-context prompts like “What’s the capital?”, it asks follow-up questions rather than guessing.Support quick correction: Make it easy for users to edit or override your interpretation. E.g., ChatGPT displays an edit button beside submitted prompts, enabling users to revise their input17. Design for AI system error statesGenAI outputs are inherently probabilistic and subject to errors ranging from hallucinations and bias to contextual misalignments.Unlike traditional systems, GenAI error states are hard to predict. Designing for these states requires transparency, recovery mechanisms and user agency. A well-designed error state can help users understand AI system boundaries and regain control.A Confusion matrix helps analyse AI system errors and provides insight into how well the model is performing by showing the counts of - True positives (correctly identifying a positive case) - False positives (incorrectly identifying a positive case) - True negatives (correctly identifying a negative case)- False negatives (failing to identify a negative case)Scenarios of AI errors and failure statesSystem failure (wrong output)False positives or false negatives occur due to poor data, biases or model hallucinations. E.g., Citibank financial fraud system displays a message “Unusual transaction. Your card is blocked. If it was you, please verify your identity”System limitation errors (no output)True negatives occur due to untrained use cases or gaps in knowledge. E.g., when an ODQA system is given a user input outside the trained dataset, throws the following error “Sorry, we don’t have enough information. Please try a different query!”Contextual errors (misunderstood output)True positives that confuse users due to poor explanations or conflicts with user expectations comes under contextual errors. E.g., when user logs in from a new device, gets locked out. AI responds: “Your login attempt was flagged for suspicious activity”How to use this patternCommunicate AI errors for various scenarios: Use phrases like “This may not be accurate”, “This seems like…” or surface confidence levels to help calibrate trust.Use pattern convey model confidence for low confidence outputs.Offer error recovery: Incase of System failure or Contextual errors, provide clear paths to override, retry or escalate the issue. E.g., Use way forwards like “Try a different query,” or “Let me refine that.” or “Contact Support”.Enable user feedback: Make it easy to report hallucinations or incorrect outputs. Read more about pattern 19. Design to capture user feedback.18. Design to capture user feedbackReal-world alignment needs direct user feedback to improve the model and thus the product. As people interact with AI systems, their behaviours shape and influence the outputs they receive in the future. Thus, creating a continuous feedback loop where both the system and user behaviour adapt over time. E.g., ChatGPT uses Reaction buttons and Comment boxes to collect user feedback.How to use this patternAccount for implicit feedback: Capture user actions such as skips, dismissals, edits, or interaction frequency. These passive signals provide valuable behavioral cues that can tune recommendations or surface patterns of disinterest.Ask for explicit feedback: Collect direct user input through thumbs-up/down, NPS rating widgets or quick surveys after actions. Use this to improve both model behavior and product fit.Communicate how feedback is used: Let users know how their feedback shapes future experiences. This increases trust and encourages ongoing contribution.19. Design for model evaluationRobust GenAI models require continuous evaluation during training as well as post-deployment. Evaluation ensures the model performs as intended, identify errors and hallucinations and aligns with user goals especially in high-stakes domains.How to use this patternThere are three key evaluation methods to improve ML systems.LLM based evaluations (LLM-as-a-judge) A separate language model acts as an automated judge. It can grade responses, explain its reasoning and assign labels like helpful/harmful or correct/incorrect.E.g., Amazon Bedrock uses the LLM-as-a-Judge approach to evaluate AI model outputs.A separate trusted LLM, like Claude 3 or Amazon Titan, automatically reviews and rates responses based on helpfulness, accuracy, relevance, and safety. For instance, two AI-generated replies to the same prompt are compared, and the judge model selects the better one.This automation reduces evaluation costs by up to 98% and speeds up model selection without relying on slow, expensive human reviews.Enable code-based evaluations: For structured tasks, use test suites or known outputs to validate model performance, especially for data processing, generation, or retrieval.Capture human evaluation: Integrate real-time UI mechanisms for users to label outputs as helpful, harmful, incorrect, or unclear. Read more about it in pattern 19. Design to capture user feedbackA hybrid approach of LLM-as-a-judge and human evaluation drastically boost accuracy to 99%.20. Design for AI guardrailsDesign for AI guardrails means building practises and principles in GenAI models to minimise harm, misinformation, toxic behaviour and biases. It is a critical consideration toProtect users and children from harmful language, made-up facts, biases or false information.Build trust and adoption: When users know the system avoids hate speech and misinformation, they feel safer and show willingness to use it often.Ethical compliance: New rules like the EU AI act demand safe AI design. Teams must meet these standards to stay legal and socially responsible.How to use this patternAnalyse and guide user inputs: If a prompt could lead to unsafe or sensitive content, guide users towards safer interactions. E.g., when Miko robot comes across profanity, it answers“I am not allowed to entertain such language”Filter outputs and moderate content: Use real-time moderation to detect and filter potentially harmful AI outputs, blocking or reframing them before they’re shown to the user. E.g., show a note like: “This response was modified to follow our safety guidelines.Use pro-active warnings: Subtly notify users when they approach sensitive or high stakes information. E.g., “This is informational advice and not a substitute for medical guidance.”Create strong user feedback: Make it easy for users to report unsafe, biased or hallucinated outputs to directly improve the AI over time through active learning loops. E.g., Instagram provides in-app option for users to report harm, bias or misinformation.Cross-validate critical information: For high-stakes domains (like healthcare, law, finance), back up AI-generated outputs with trusted databases to catch hallucinations. Refer pattern 10, Provide data sources.21. Communicate data privacy and controlsThis pattern ensures GenAI applications clearly convey how user data is collected, stored, processed and protected.GenAI systems often rely on sensitive, contextual, or behavioral data. Mishandling this data can lead to user distrust, legal risk or unintended misuse. Clear communication around privacy safeguards helps users feel safe, respected and in control. E.g., Slack AI clearly communicates that customer data remains owned and controlled by the customer and is not used to train Slack’s or any third-party AI modelsHow to use this patternShow transparency: When a GenAI feature accesses user data, display explanation of what’s being accessed and why.Design opt-in and opt-out flows: Allow users to easily toggle data sharing preferences.Enable data review and deletion: Allow users to view, download or delete their data history giving them ongoing control.ConclusionThese GenAI UX patterns are a starting point and represent the outcome of months of research, shaped directly and indirectly with insights from notable designers, researchers, and technologists across leading tech companies and the broader AI communites across Medium and Linkedin. I have done my best to cite and acknowledge contributors along the way but I’m sure I’ve missed many. If you see something that should be credited or expanded, please reach out.Moreover, these patterns are meant to grow and evolve as we learn more about creating AI that’s trustworthy and puts people first. If you’re a designer, researcher, or builder working with AI, take these patterns, challenge them, remix them and contribute your own. Also, please let me know in comments about your suggestions. If you would like to collaborate with me to further refine this, please reach out to me.20+ GenAI UX patterns, examples and implementation tactics was originally published in UX Collective on Medium, where people are continuing the conversation by highlighting and responding to this story.

Posted on : May 19, 2025 By Tech World Times General  Rate this post Choosing the right trivia team name is important. It can set the mood and bring instant laughs. A good name makes your team stand out. It can also break the ice and boost the fun. In 2025, people want creative and funny team trivia names. Names that make others smile or even giggle out loud. Whether it’s for a pub quiz, office event, or game night, the right name adds charm. Here is a big list of over 100 funny team trivia names you can use this year. Classic and Timeless Funny Team Trivia Names These names are simple, funny, and never go out of style. Cristopher Walken Quiz On My Face Quizzy Rascals Let’s Get Quizzical Smarty Pints Trivianators The Quizzard of Oz Sherlock Homies Tequila Mockingbird I Am Smarticus Clever Wordplay Trivia Team Names These names are punny and smart. Great for teams who love word games. Quiz Khalifa Quiz Me Baby One More Time The Know It Ales Victorious Secret Risky Quizness The Fact Checkers Trivia Newton John Agatha Quiztie Menace to Sobriety Simple Minds Bar and Booze-Themed Trivia Names Perfect for pub quizzes or trivia nights with drinks. Beer Pressure The Booze Clues Whiskey Business Full of Trivia Alcohol You Later Rum Forrest Rum The Drinking Thinkers Mind Over Miller Brews and Brains Nacho Average Team Office and Work-Themed Names Ideal for workplace trivia events or corporate quiz nights. Ctrl Alt Elite Cubicle Crew Slackers United Excel-lent Minds Spreadsheet Superstars The Late Submitters Team No Deadlines Keyboard Warriors Meeting Survivors The PTO Squad Movie & TV Show-Inspired Names Fans of movies and TV shows will love these funny team trivia names. Breaking Badasses The Quizzengers Game of Phones The Office Jokers Stranger Quizzes Marvelous Minds Quizflix and Chill The Walking Answer How I Met Your Answer Friends with Answers Music-Inspired Trivia Names Fun for teams who love music and trivia combined. Bohemian Rhapsodorks The Rolling Clones The Bee Quizz Quizzical Harmony Red Hot Trivia Peppers The Quiztles Stairway to Trivia Quizzin’ In the Wind Answer Me Maybe Quizard and the Lizard Nerdy and Geeky Team Names For trivia lovers who also love tech, comics, or science. The Brainiacs Nerd Immunity Hogwarts Dropouts Trivia Trekkies Live Long and Answer Lord of the Answers May the Facts Be With You Quizzards United The Answer Benders Dumbledorks Pop Culture-Inspired Trivia Names These names are trendy and reflect popular culture in 2025. TikTok Scholars Meme Team Supreme Reels and Deals The Snapple Facts Streaming Geniuses Hashtag Smart AI Know Everything The Viral Minds ChatGPT Champs Influencer Intellects Random and Ridiculous Names These are just silly and super fun. E = MC Hammer The Googling Gurus Siri-ously Smart Low Expectations We Thought This Was Bingo Brain Farts Smells Like Team Spirit 404 Brain Not Found Definitely Maybe Zero Clue Crew Competitive and Confident Names If your team plans to win, these names send the message. Champions of Trivia The Answer Squad Born to Win Full Marks Only The Perfect Score Brainstorm Troopers The Real MVPs First Place or Bust Quiz Masters The Grand Finalists One-Word Funny Team Trivia Names Sometimes, short and punchy is better. Quizzly Smartheads Guessers Witsquad Thinktank Final Thoughts Trivia nights are more fun with a good team name. It builds identity and starts the game with laughter. Whether you love wordplay, pop culture, or just jokes, this list has something for you. Funny team trivia names show off your humor. They also make your team easy to remember. In 2025, don’t settle for boring names. Pick something fun, bold, or just plain weird. If you’re stuck, just combine your favorite movie, food, or quote with a trivia word. Be creative and have fun. FAQs 1. Why is a funny trivia team name important? A funny name sets the mood. It helps your team stand out and adds fun to the game. 2. Can we change our trivia team name for each game? Yes, you can! Many teams use new names every week to keep things fresh. 3. How do I choose the best name? Think of what your team likes. Use jokes, puns, or pop culture for ideas. 4. Do trivia hosts allow funny or weird names? Most hosts love creative names. Just keep it respectful and friendly. 5. Are these trivia names good for virtual games? Yes, all names listed work for online and in-person trivia nights. Tech World TimesTech World Times (TWT), a global collective focusing on the latest tech news and trends in blockchain, Fintech, Development & Testing, AI and Startups. If you are looking for the guest post then contact at techworldtimes@gmail.com

iSpeedToLead is a fast-growing, innovative marketplace selling motivated seller leads to real estate investors and wholesalers. We’re transforming real estate lead generation through cutting-edge AI-driven solutions, enhancing marketplace liquidity, and optimizing user experiences.Role Mission: Lead the practical deployment and integration of AI technologies across the organization, ensuring all AI initiatives translate into tangible business results and seamless user experiences.Responsibilities:Coordinate the implementation of AI projects across different business functions, including operations, sales, marketing, and customer support.Act as a bridge between AI/ML engineering teams and non-technical stakeholders to facilitate clear communication and effective collaboration.Manage project timelines, ensuring AI initiatives are delivered on schedule and within budget.Troubleshoot and resolve issues during AI project execution, ensuring smooth operational integration.Train internal teams on how to effectively use and leverage AI-driven tools and systems.Design and optimize automation workflows using tools like Make.Develop and refine AI prompts for maximum efficiency and performance in AI-driven features.Requirements:3+ years experience in technical project management or AI implementation roles.Familiarity with machine learning concepts, AI-driven software solutions, and data management practices.Proficiency in project management and collaboration tools such as Jira, Slack, Notion, Monday.com, Trello, and similar platforms.Strong hands-on experience with automation tools (Make.com, Zapier, or similar).Understanding of prompt engineering and optimizing AI model interactions for enhanced outcomes.Excellent organizational and communication skills, able to clearly articulate technical details to non-technical audiences.Strong problem-solving abilities and practical experience in project management methodologies (Agile/Scrum).Prior experience implementing AI solutions in marketplace, SaaS, or real estate tech environments is highly desirable.What Success Looks Like:Timely and successful delivery of AI projects with measurable business impacts.Strong internal adoption and user satisfaction with implemented AI solutions.Effective and efficient communication between technical and business teams.Why Join Us?Work in a dynamic, growth-oriented environment.Directly impact strategic business outcomes through AI innovation.Collaborate closely with a driven, innovative team.Competitive salary, equity opportunities, and career advancement.Next Steps: Excited about driving impactful AI implementations? Apply now and shape the future of AI-driven real estate technology at iSpeedToLead!Apply NowLet's start your dream job Apply now Meet JobCopilot: Your Personal AI Job HunterAutomatically Apply to Remote Product JobsJust set your preferences and Job Copilot will do the rest-finding, filtering, and applying while you focus on what matters. Activate JobCopilot

In 2019, Karen Hao, a senior reporter with MIT Technology Review, pitched me on writing a story about a then little-known company, OpenAI. It was her biggest assignment to date. Hao’s feat of reporting took a series of twists and turns over the coming months, eventually revealing how OpenAI’s ambition had taken it far afield from its original mission. The finished story was a prescient look at a company at a tipping point—or already past it. And OpenAI was not happy with the result. Hao’s new book, Empire of AI: Dreams and Nightmares in Sam Altman’s OpenAI, is an in-depth exploration of the company that kick-started the AI arms race, and what that race means for all of us. This excerpt is the origin story of that reporting. — Niall Firth, executive editor, MIT Technology Review I arrived at OpenAI’s offices on August 7, 2019. Greg Brockman, then thirty‑one, OpenAI’s chief technology officer and soon‑to‑be company president, came down the staircase to greet me. He shook my hand with a tentative smile. “We’ve never given someone so much access before,” he said. At the time, few people beyond the insular world of AI research knew about OpenAI. But as a reporter at MIT Technology Review covering the ever‑expanding boundaries of artificial intelligence, I had been following its movements closely. Until that year, OpenAI had been something of a stepchild in AI research. It had an outlandish premise that AGI could be attained within a decade, when most non‑OpenAI experts doubted it could be attained at all. To much of the field, it had an obscene amount of funding despite little direction and spent too much of the money on marketing what other researchers frequently snubbed as unoriginal research. It was, for some, also an object of envy. As a nonprofit, it had said that it had no intention to chase commercialization. It was a rare intellectual playground without strings attached, a haven for fringe ideas. But in the six months leading up to my visit, the rapid slew of changes at OpenAI signaled a major shift in its trajectory. First was its confusing decision to withhold GPT‑2 and brag about it. Then its announcement that Sam Altman, who had mysteriously departed his influential perch at YC, would step in as OpenAI’s CEO with the creation of its new “capped‑profit” structure. I had already made my arrangements to visit the office when it subsequently revealed its deal with Microsoft, which gave the tech giant priority for commercializing OpenAI’s technologies and locked it into exclusively using Azure, Microsoft’s cloud‑computing platform. Each new announcement garnered fresh controversy, intense speculation, and growing attention, beginning to reach beyond the confines of the tech industry. As my colleagues and I covered the company’s progression, it was hard to grasp the full weight of what was happening. What was clear was that OpenAI was beginning to exert meaningful sway over AI research and the way policymakers were learning to understand the technology. The lab’s decision to revamp itself into a partially for‑profit business would have ripple effects across its spheres of influence in industry and government.  So late one night, with the urging of my editor, I dashed off an email to Jack Clark, OpenAI’s policy director, whom I had spoken with before: I would be in town for two weeks, and it felt like the right moment in OpenAI’s history. Could I interest them in a profile? Clark passed me on to the communications head, who came back with an answer. OpenAI was indeed ready to reintroduce itself to the public. I would have three days to interview leadership and embed inside the company. Brockman and I settled into a glass meeting room with the company’s chief scientist, Ilya Sutskever. Sitting side by side at a long conference table, they each played their part. Brockman, the coder and doer, leaned forward, a little on edge, ready to make a good impression; Sutskever, the researcher and philosopher, settled back into his chair, relaxed and aloof. I opened my laptop and scrolled through my questions. OpenAI’s mission is to ensure beneficial AGI, I began. Why spend billions of dollars on this problem and not something else? Brockman nodded vigorously. He was used to defending OpenAI’s position. “The reason that we care so much about AGI and that we think it’s important to build is because we think it can help solve complex problems that are just out of reach of humans,” he said. He offered two examples that had become dogma among AGI believers. Climate change. “It’s a super‑complex problem. How are you even supposed to solve it?” And medicine. “Look at how important health care is in the US as a political issue these days. How do we actually get better treatment for people at lower cost?” On the latter, he began to recount the story of a friend who had a rare disorder and had recently gone through the exhausting rigmarole of bouncing between different specialists to figure out his problem. AGI would bring together all of these specialties. People like his friend would no longer spend so much energy and frustration on getting an answer. Why did we need AGI to do that instead of AI? I asked. This was an important distinction. The term AGI, once relegated to an unpopular section of the technology dictionary, had only recently begun to gain more mainstream usage—in large part because of OpenAI. And as OpenAI defined it, AGI referred to a theoretical pinnacle of AI research: a piece of software that had just as much sophistication, agility, and creativity as the human mind to match or exceed its performance on most (economically valuable) tasks. The operative word was theoretical. Since the beginning of earnest research into AI several decades earlier, debates had raged about whether silicon chips encoding everything in their binary ones and zeros could ever simulate brains and the other biological processes that give rise to what we consider intelligence. There had yet to be definitive evidence that this was possible, which didn’t even touch on the normative discussion of whether people should develop it. AI, on the other hand, was the term du jour for both the version of the technology currently available and the version that researchers could reasonably attain in the near future through refining existing capabilities. Those capabilities—rooted in powerful pattern matching known as machine learning—had already demonstrated exciting applications in climate change mitigation and health care. Sutskever chimed in. When it comes to solving complex global challenges, “fundamentally the bottleneck is that you have a large number of humans and they don’t communicate as fast, they don’t work as fast, they have a lot of incentive problems.” AGI would be different, he said. “Imagine it’s a large computer network of intelligent computers—they’re all doing their medical diagnostics; they all communicate results between them extremely fast.” This seemed to me like another way of saying that the goal of AGI was to replace humans. Is that what Sutskever meant? I asked Brockman a few hours later, once it was just the two of us. “No,” Brockman replied quickly. “This is one thing that’s really important. What is the purpose of technology? Why is it here? Why do we build it? We’ve been building technologies for thousands of years now, right? We do it because they serve people. AGI is not going to be different—not the way that we envision it, not the way we want to build it, not the way we think it should play out.” That said, he acknowledged a few minutes later, technology had always destroyed some jobs and created others. OpenAI’s challenge would be to build AGI that gave everyone “economic freedom” while allowing them to continue to “live meaningful lives” in that new reality. If it succeeded, it would decouple the need to work from survival. “I actually think that’s a very beautiful thing,” he said. In our meeting with Sutskever, Brockman reminded me of the bigger picture. “What we view our role as is not actually being a determiner of whether AGI gets built,” he said. This was a favorite argument in Silicon Valley—the inevitability card. If we don’t do it, somebody else will. “The trajectory is already there,” he emphasized, “but the thing we can influence is the initial conditions under which it’s born. “What is OpenAI?” he continued. “What is our purpose? What are we really trying to do? Our mission is to ensure that AGI benefits all of humanity. And the way we want to do that is: Build AGI and distribute its economic benefits.” His tone was matter‑of‑fact and final, as if he’d put my questions to rest. And yet we had somehow just arrived back to exactly where we’d started. Our conversation continued on in circles until we ran out the clock after forty‑five minutes. I tried with little success to get more concrete details on what exactly they were trying to build—which by nature, they explained, they couldn’t know—and why, then, if they couldn’t know, they were so confident it would be beneficial. At one point, I tried a different approach, asking them instead to give examples of the downsides of the technology. This was a pillar of OpenAI’s founding mythology: The lab had to build good AGI before someone else built a bad one. Brockman attempted an answer: deepfakes. “It’s not clear the world is better through its applications,” he said. I offered my own example: Speaking of climate change, what about the environmental impact of AI itself? A recent study from the University of Massachusetts Amherst had placed alarming numbers on the huge and growing carbon emissions of training larger and larger AI models. That was “undeniable,” Sutskever said, but the payoff was worth it because AGI would, “among other things, counteract the environmental cost specifically.” He stopped short of offering examples. “It is unquestioningly very highly desirable that data centers be as green as possible,” he added. “No question,” Brockman quipped. “Data centers are the biggest consumer of energy, of electricity,” Sutskever continued, seeming intent now on proving that he was aware of and cared about this issue. “It’s 2 percent globally,” I offered. “Isn’t Bitcoin like 1 percent?” Brockman said. “Wow!” Sutskever said, in a sudden burst of emotion that felt, at this point, forty minutes into the conversation, somewhat performative. Sutskever would later sit down with New York Times reporter Cade Metz for his book Genius Makers, which recounts a narrative history of AI development, and say without a hint of satire, “I think that it’s fairly likely that it will not take too long of a time for the entire surface of the Earth to become covered with data centers and power stations.” There would be “a tsunami of computing . . . almost like a natural phenomenon.” AGI—and thus the data centers needed to support them—would be “too useful to not exist.” I tried again to press for more details. “What you’re saying is OpenAI is making a huge gamble that you will successfully reach beneficial AGI to counteract global warming before the act of doing so might exacerbate it.” “I wouldn’t go too far down that rabbit hole,” Brockman hastily cut in. “The way we think about it is the following: We’re on a ramp of AI progress. This is bigger than OpenAI, right? It’s the field. And I think society is actually getting benefit from it.” “The day we announced the deal,” he said, referring to Microsoft’s new $1 billion investment, “Microsoft’s market cap went up by $10 billion. People believe there is a positive ROI even just on short‑term technology.” OpenAI’s strategy was thus quite simple, he explained: to keep up with that progress. “That’s the standard we should really hold ourselves to. We should continue to make that progress. That’s how we know we’re on track.” Later that day, Brockman reiterated that the central challenge of working at OpenAI was that no one really knew what AGI would look like. But as researchers and engineers, their task was to keep pushing forward, to unearth the shape of the technology step by step. He spoke like Michelangelo, as though AGI already existed within the marble he was carving. All he had to do was chip away until it revealed itself. There had been a change of plans. I had been scheduled to eat lunch with employees in the cafeteria, but something now required me to be outside the office. Brockman would be my chaperone. We headed two dozen steps across the street to an open‑air café that had become a favorite haunt for employees. This would become a recurring theme throughout my visit: floors I couldn’t see, meetings I couldn’t attend, researchers stealing furtive glances at the communications head every few sentences to check that they hadn’t violated some disclosure policy. I would later learn that after my visit, Jack Clark would issue an unusually stern warning to employees on Slack not to speak with me beyond sanctioned conversations. The security guard would receive a photo of me with instructions to be on the lookout if I appeared unapproved on the premises. It was odd behavior in general, made odder by OpenAI’s commitment to transparency. What, I began to wonder, were they hiding, if everything was supposed to be beneficial research eventually made available to the public? At lunch and through the following days, I probed deeper into why Brockman had cofounded OpenAI. He was a teen when he first grew obsessed with the idea that it could be possible to re‑create human intelligence. It was a famous paper from British mathematician Alan Turing that sparked his fascination. The name of its first section, “The Imitation Game,” which inspired the title of the 2014 Hollywood dramatization of Turing’s life, begins with the opening provocation, “Can machines think?” The paper goes on to define what would become known as the Turing test: a measure of the progression of machine intelligence based on whether a machine can talk to a human without giving away that it is a machine. It was a classic origin story among people working in AI. Enchanted, Brockman coded up a Turing test game and put it online, garnering some 1,500 hits. It made him feel amazing. “I just realized that was the kind of thing I wanted to pursue,” he said. In 2015, as AI saw great leaps of advancement, Brockman says that he realized it was time to return to his original ambition and joined OpenAI as a cofounder. He wrote down in his notes that he would do anything to bring AGI to fruition, even if it meant being a janitor. When he got married four years later, he held a civil ceremony at OpenAI’s office in front of a custom flower wall emblazoned with the shape of the lab’s hexagonal logo. Sutskever officiated. The robotic hand they used for research stood in the aisle bearing the rings, like a sentinel from a post-apocalyptic future. “Fundamentally, I want to work on AGI for the rest of my life,” Brockman told me. What motivated him? I asked Brockman. What are the chances that a transformative technology could arrive in your lifetime? he countered. He was confident that he—and the team he assembled—was uniquely positioned to usher in that transformation. “What I’m really drawn to are problems that will not play out in the same way if I don’t participate,” he said. Brockman did not in fact just want to be a janitor. He wanted to lead AGI. And he bristled with the anxious energy of someone who wanted history‑defining recognition. He wanted people to one day tell his story with the same mixture of awe and admiration that he used to recount the ones of the great innovators who came before him. A year before we spoke, he had told a group of young tech entrepreneurs at an exclusive retreat in Lake Tahoe with a twinge of self‑pity that chief technology officers were never known. Name a famous CTO, he challenged the crowd. They struggled to do so. He had proved his point. In 2022, he became OpenAI’s president. During our conversations, Brockman insisted to me that none of OpenAI’s structural changes signaled a shift in its core mission. In fact, the capped profit and the new crop of funders enhanced it. “We managed to get these mission‑aligned investors who are willing to prioritize mission over returns. That’s a crazy thing,” he said. OpenAI now had the long‑term resources it needed to scale its models and stay ahead of the competition. This was imperative, Brockman stressed. Failing to do so was the real threat that could undermine OpenAI’s mission. If the lab fell behind, it had no hope of bending the arc of history toward its vision of beneficial AGI. Only later would I realize the full implications of this assertion. It was this fundamental assumption—the need to be first or perish—that set in motion all of OpenAI’s actions and their far‑reaching consequences. It put a ticking clock on each of OpenAI’s research advancements, based not on the timescale of careful deliberation but on the relentless pace required to cross the finish line before anyone else. It justified OpenAI’s consumption of an unfathomable amount of resources: both compute, regardless of its impact on the environment; and data, the amassing of which couldn’t be slowed by getting consent or abiding by regulations. Brockman pointed once again to the $10 billion jump in Microsoft’s market cap. “What that really reflects is AI is delivering real value to the real world today,” he said. That value was currently being concentrated in an already wealthy corporation, he acknowledged, which was why OpenAI had the second part of its mission: to redistribute the benefits of AGI to everyone. Was there a historical example of a technology’s benefits that had been successfully distributed? I asked. “Well, I actually think that—it’s actually interesting to look even at the internet as an example,” he said, fumbling a bit before settling on his answer. “There’s problems, too, right?” he said as a caveat. “Anytime you have something super transformative, it’s not going to be easy to figure out how to maximize positive, minimize negative. “Fire is another example,” he added. “It’s also got some real drawbacks to it. So we have to figure out how to keep it under control and have shared standards. “Cars are a good example,” he followed. “Lots of people have cars, benefit a lot of people. They have some drawbacks to them as well. They have some externalities that are not necessarily good for the world,” he finished hesitantly. “I guess I just view—the thing we want for AGI is not that different from the positive sides of the internet, positive sides of cars, positive sides of fire. The implementation is very different, though, because it’s a very different type of technology.” His eyes lit up with a new idea. “Just look at utilities. Power companies, electric companies are very centralized entities that provide low‑cost, high‑quality things that meaningfully improve people’s lives.” It was a nice analogy. But Brockman seemed once again unclear about how OpenAI would turn itself into a utility. Perhaps through distributing universal basic income, he wondered aloud, perhaps through something else. He returned to the one thing he knew for certain. OpenAI was committed to redistributing AGI’s benefits and giving everyone economic freedom. “We actually really mean that,” he said. “The way that we think about it is: Technology so far has been something that does rise all the boats, but it has this real concentrating effect,” he said. “AGI could be more extreme. What if all value gets locked up in one place? That is the trajectory we’re on as a society. And we’ve never seen that extreme of it. I don’t think that’s a good world. That’s not a world that I want to sign up for. That’s not a world that I want to help build.” In February 2020, I published my profile for MIT Technology Review, drawing on my observations from my time in the office, nearly three dozen interviews, and a handful of internal documents. “There is a misalignment between what the company publicly espouses and how it operates behind closed doors,” I wrote. “Over time, it has allowed a fierce competitiveness and mounting pressure for ever more funding to erode its founding ideals of transparency, openness, and collaboration.” Hours later, Elon Musk replied to the story with three tweets in rapid succession: “OpenAI should be more open imo” “I have no control & only very limited insight into OpenAI. Confidence in Dario for safety is not high,” he said, referring to Dario Amodei, the director of research. “All orgs developing advanced AI should be regulated, including Tesla” Afterward, Altman sent OpenAI employees an email. “I wanted to share some thoughts about the Tech Review article,” he wrote. “While definitely not catastrophic, it was clearly bad.” It was “a fair criticism,” he said that the piece had identified a disconnect between the perception of OpenAI and its reality. This could be smoothed over not with changes to its internal practices but some tuning of OpenAI’s public messaging. “It’s good, not bad, that we have figured out how to be flexible and adapt,” he said, including restructuring the organization and heightening confidentiality, “in order to achieve our mission as we learn more.” OpenAI should ignore my article for now and, in a few weeks’ time, start underscoring its continued commitment to its original principles under the new transformation. “This may also be a good opportunity to talk about the API as a strategy for openness and benefit sharing,” he added, referring to an application programming interface for delivering OpenAI’s models. “The most serious issue of all, to me,” he continued, “is that someone leaked our internal documents.” They had already opened an investigation and would keep the company updated. He would also suggest that Amodei and Musk meet to work out Musk’s criticism, which was “mild relative to other things he’s said” but still “a bad thing to do.” For the avoidance of any doubt, Amodei’s work and AI safety were critical to the mission, he wrote. “I think we should at some point in the future find a way to publicly defend our team (but not give the press the public fight they’d love right now).” OpenAI wouldn’t speak to me again for three years. From the book , to be published on May 20, 2025, by Penguin Press, an imprint of Penguin Publishing Group, a division of Penguin Random House LLC. Copyright © 2025 by Karen Hao.

In 2019, Karen Hao, a senior reporter with MIT Technology Review, pitched me on writing a story about a then little-known company, OpenAI. It was her biggest assignment to date. Hao’s feat of reporting took a series of twists and turns over the coming months, eventually revealing how OpenAI’s ambition had taken it far afield from its original mission. The finished story was a prescient look at a company at a tipping point—or already past it. And OpenAI was not happy with the result. Hao’s new book, Empire of AI: Dreams and Nightmares in Sam Altman’s OpenAI, is an in-depth exploration of the company that kick-started the AI arms race, and what that race means for all of us. This excerpt is the origin story of that reporting. — Niall Firth, executive editor, MIT Technology Review I arrived at OpenAI’s offices on August 7, 2019. Greg Brockman, then thirty‑one, OpenAI’s chief technology officer and soon‑to‑be company president, came down the staircase to greet me. He shook my hand with a tentative smile. “We’ve never given someone so much access before,” he said. At the time, few people beyond the insular world of AI research knew about OpenAI. But as a reporter at MIT Technology Review covering the ever‑expanding boundaries of artificial intelligence, I had been following its movements closely. Until that year, OpenAI had been something of a stepchild in AI research. It had an outlandish premise that AGI could be attained within a decade, when most non‑OpenAI experts doubted it could be attained at all. To much of the field, it had an obscene amount of funding despite little direction and spent too much of the money on marketing what other researchers frequently snubbed as unoriginal research. It was, for some, also an object of envy. As a nonprofit, it had said that it had no intention to chase commercialization. It was a rare intellectual playground without strings attached, a haven for fringe ideas. But in the six months leading up to my visit, the rapid slew of changes at OpenAI signaled a major shift in its trajectory. First was its confusing decision to withhold GPT‑2 and brag about it. Then its announcement that Sam Altman, who had mysteriously departed his influential perch at YC, would step in as OpenAI’s CEO with the creation of its new “capped‑profit” structure. I had already made my arrangements to visit the office when it subsequently revealed its deal with Microsoft, which gave the tech giant priority for commercializing OpenAI’s technologies and locked it into exclusively using Azure, Microsoft’s cloud‑computing platform. Each new announcement garnered fresh controversy, intense speculation, and growing attention, beginning to reach beyond the confines of the tech industry. As my colleagues and I covered the company’s progression, it was hard to grasp the full weight of what was happening. What was clear was that OpenAI was beginning to exert meaningful sway over AI research and the way policymakers were learning to understand the technology. The lab’s decision to revamp itself into a partially for‑profit business would have ripple effects across its spheres of influence in industry and government.  So late one night, with the urging of my editor, I dashed off an email to Jack Clark, OpenAI’s policy director, whom I had spoken with before: I would be in town for two weeks, and it felt like the right moment in OpenAI’s history. Could I interest them in a profile? Clark passed me on to the communications head, who came back with an answer. OpenAI was indeed ready to reintroduce itself to the public. I would have three days to interview leadership and embed inside the company. Brockman and I settled into a glass meeting room with the company’s chief scientist, Ilya Sutskever. Sitting side by side at a long conference table, they each played their part. Brockman, the coder and doer, leaned forward, a little on edge, ready to make a good impression; Sutskever, the researcher and philosopher, settled back into his chair, relaxed and aloof. I opened my laptop and scrolled through my questions. OpenAI’s mission is to ensure beneficial AGI, I began. Why spend billions of dollars on this problem and not something else? Brockman nodded vigorously. He was used to defending OpenAI’s position. “The reason that we care so much about AGI and that we think it’s important to build is because we think it can help solve complex problems that are just out of reach of humans,” he said. He offered two examples that had become dogma among AGI believers. Climate change. “It’s a super‑complex problem. How are you even supposed to solve it?” And medicine. “Look at how important health care is in the US as a political issue these days. How do we actually get better treatment for people at lower cost?” On the latter, he began to recount the story of a friend who had a rare disorder and had recently gone through the exhausting rigmarole of bouncing between different specialists to figure out his problem. AGI would bring together all of these specialties. People like his friend would no longer spend so much energy and frustration on getting an answer. Why did we need AGI to do that instead of AI? I asked. This was an important distinction. The term AGI, once relegated to an unpopular section of the technology dictionary, had only recently begun to gain more mainstream usage—in large part because of OpenAI. And as OpenAI defined it, AGI referred to a theoretical pinnacle of AI research: a piece of software that had just as much sophistication, agility, and creativity as the human mind to match or exceed its performance on most (economically valuable) tasks. The operative word was theoretical. Since the beginning of earnest research into AI several decades earlier, debates had raged about whether silicon chips encoding everything in their binary ones and zeros could ever simulate brains and the other biological processes that give rise to what we consider intelligence. There had yet to be definitive evidence that this was possible, which didn’t even touch on the normative discussion of whether people should develop it. AI, on the other hand, was the term du jour for both the version of the technology currently available and the version that researchers could reasonably attain in the near future through refining existing capabilities. Those capabilities—rooted in powerful pattern matching known as machine learning—had already demonstrated exciting applications in climate change mitigation and health care. Sutskever chimed in. When it comes to solving complex global challenges, “fundamentally the bottleneck is that you have a large number of humans and they don’t communicate as fast, they don’t work as fast, they have a lot of incentive problems.” AGI would be different, he said. “Imagine it’s a large computer network of intelligent computers—they’re all doing their medical diagnostics; they all communicate results between them extremely fast.” This seemed to me like another way of saying that the goal of AGI was to replace humans. Is that what Sutskever meant? I asked Brockman a few hours later, once it was just the two of us. “No,” Brockman replied quickly. “This is one thing that’s really important. What is the purpose of technology? Why is it here? Why do we build it? We’ve been building technologies for thousands of years now, right? We do it because they serve people. AGI is not going to be different—not the way that we envision it, not the way we want to build it, not the way we think it should play out.” That said, he acknowledged a few minutes later, technology had always destroyed some jobs and created others. OpenAI’s challenge would be to build AGI that gave everyone “economic freedom” while allowing them to continue to “live meaningful lives” in that new reality. If it succeeded, it would decouple the need to work from survival. “I actually think that’s a very beautiful thing,” he said. In our meeting with Sutskever, Brockman reminded me of the bigger picture. “What we view our role as is not actually being a determiner of whether AGI gets built,” he said. This was a favorite argument in Silicon Valley—the inevitability card. If we don’t do it, somebody else will. “The trajectory is already there,” he emphasized, “but the thing we can influence is the initial conditions under which it’s born. “What is OpenAI?” he continued. “What is our purpose? What are we really trying to do? Our mission is to ensure that AGI benefits all of humanity. And the way we want to do that is: Build AGI and distribute its economic benefits.” His tone was matter‑of‑fact and final, as if he’d put my questions to rest. And yet we had somehow just arrived back to exactly where we’d started. Our conversation continued on in circles until we ran out the clock after forty‑five minutes. I tried with little success to get more concrete details on what exactly they were trying to build—which by nature, they explained, they couldn’t know—and why, then, if they couldn’t know, they were so confident it would be beneficial. At one point, I tried a different approach, asking them instead to give examples of the downsides of the technology. This was a pillar of OpenAI’s founding mythology: The lab had to build good AGI before someone else built a bad one. Brockman attempted an answer: deepfakes. “It’s not clear the world is better through its applications,” he said. I offered my own example: Speaking of climate change, what about the environmental impact of AI itself? A recent study from the University of Massachusetts Amherst had placed alarming numbers on the huge and growing carbon emissions of training larger and larger AI models. That was “undeniable,” Sutskever said, but the payoff was worth it because AGI would, “among other things, counteract the environmental cost specifically.” He stopped short of offering examples. “It is unquestioningly very highly desirable that data centers be as green as possible,” he added. “No question,” Brockman quipped. “Data centers are the biggest consumer of energy, of electricity,” Sutskever continued, seeming intent now on proving that he was aware of and cared about this issue. “It’s 2 percent globally,” I offered. “Isn’t Bitcoin like 1 percent?” Brockman said. “Wow!” Sutskever said, in a sudden burst of emotion that felt, at this point, forty minutes into the conversation, somewhat performative. Sutskever would later sit down with New York Times reporter Cade Metz for his book Genius Makers, which recounts a narrative history of AI development, and say without a hint of satire, “I think that it’s fairly likely that it will not take too long of a time for the entire surface of the Earth to become covered with data centers and power stations.” There would be “a tsunami of computing . . . almost like a natural phenomenon.” AGI—and thus the data centers needed to support them—would be “too useful to not exist.” I tried again to press for more details. “What you’re saying is OpenAI is making a huge gamble that you will successfully reach beneficial AGI to counteract global warming before the act of doing so might exacerbate it.” “I wouldn’t go too far down that rabbit hole,” Brockman hastily cut in. “The way we think about it is the following: We’re on a ramp of AI progress. This is bigger than OpenAI, right? It’s the field. And I think society is actually getting benefit from it.” “The day we announced the deal,” he said, referring to Microsoft’s new $1 billion investment, “Microsoft’s market cap went up by $10 billion. People believe there is a positive ROI even just on short‑term technology.” OpenAI’s strategy was thus quite simple, he explained: to keep up with that progress. “That’s the standard we should really hold ourselves to. We should continue to make that progress. That’s how we know we’re on track.” Later that day, Brockman reiterated that the central challenge of working at OpenAI was that no one really knew what AGI would look like. But as researchers and engineers, their task was to keep pushing forward, to unearth the shape of the technology step by step. He spoke like Michelangelo, as though AGI already existed within the marble he was carving. All he had to do was chip away until it revealed itself. There had been a change of plans. I had been scheduled to eat lunch with employees in the cafeteria, but something now required me to be outside the office. Brockman would be my chaperone. We headed two dozen steps across the street to an open‑air café that had become a favorite haunt for employees. This would become a recurring theme throughout my visit: floors I couldn’t see, meetings I couldn’t attend, researchers stealing furtive glances at the communications head every few sentences to check that they hadn’t violated some disclosure policy. I would later learn that after my visit, Jack Clark would issue an unusually stern warning to employees on Slack not to speak with me beyond sanctioned conversations. The security guard would receive a photo of me with instructions to be on the lookout if I appeared unapproved on the premises. It was odd behavior in general, made odder by OpenAI’s commitment to transparency. What, I began to wonder, were they hiding, if everything was supposed to be beneficial research eventually made available to the public? At lunch and through the following days, I probed deeper into why Brockman had cofounded OpenAI. He was a teen when he first grew obsessed with the idea that it could be possible to re‑create human intelligence. It was a famous paper from British mathematician Alan Turing that sparked his fascination. The name of its first section, “The Imitation Game,” which inspired the title of the 2014 Hollywood dramatization of Turing’s life, begins with the opening provocation, “Can machines think?” The paper goes on to define what would become known as the Turing test: a measure of the progression of machine intelligence based on whether a machine can talk to a human without giving away that it is a machine. It was a classic origin story among people working in AI. Enchanted, Brockman coded up a Turing test game and put it online, garnering some 1,500 hits. It made him feel amazing. “I just realized that was the kind of thing I wanted to pursue,” he said. In 2015, as AI saw great leaps of advancement, Brockman says that he realized it was time to return to his original ambition and joined OpenAI as a cofounder. He wrote down in his notes that he would do anything to bring AGI to fruition, even if it meant being a janitor. When he got married four years later, he held a civil ceremony at OpenAI’s office in front of a custom flower wall emblazoned with the shape of the lab’s hexagonal logo. Sutskever officiated. The robotic hand they used for research stood in the aisle bearing the rings, like a sentinel from a post-apocalyptic future. “Fundamentally, I want to work on AGI for the rest of my life,” Brockman told me. What motivated him? I asked Brockman. What are the chances that a transformative technology could arrive in your lifetime? he countered. He was confident that he—and the team he assembled—was uniquely positioned to usher in that transformation. “What I’m really drawn to are problems that will not play out in the same way if I don’t participate,” he said. Brockman did not in fact just want to be a janitor. He wanted to lead AGI. And he bristled with the anxious energy of someone who wanted history‑defining recognition. He wanted people to one day tell his story with the same mixture of awe and admiration that he used to recount the ones of the great innovators who came before him. A year before we spoke, he had told a group of young tech entrepreneurs at an exclusive retreat in Lake Tahoe with a twinge of self‑pity that chief technology officers were never known. Name a famous CTO, he challenged the crowd. They struggled to do so. He had proved his point. In 2022, he became OpenAI’s president. During our conversations, Brockman insisted to me that none of OpenAI’s structural changes signaled a shift in its core mission. In fact, the capped profit and the new crop of funders enhanced it. “We managed to get these mission‑aligned investors who are willing to prioritize mission over returns. That’s a crazy thing,” he said. OpenAI now had the long‑term resources it needed to scale its models and stay ahead of the competition. This was imperative, Brockman stressed. Failing to do so was the real threat that could undermine OpenAI’s mission. If the lab fell behind, it had no hope of bending the arc of history toward its vision of beneficial AGI. Only later would I realize the full implications of this assertion. It was this fundamental assumption—the need to be first or perish—that set in motion all of OpenAI’s actions and their far‑reaching consequences. It put a ticking clock on each of OpenAI’s research advancements, based not on the timescale of careful deliberation but on the relentless pace required to cross the finish line before anyone else. It justified OpenAI’s consumption of an unfathomable amount of resources: both compute, regardless of its impact on the environment; and data, the amassing of which couldn’t be slowed by getting consent or abiding by regulations. Brockman pointed once again to the $10 billion jump in Microsoft’s market cap. “What that really reflects is AI is delivering real value to the real world today,” he said. That value was currently being concentrated in an already wealthy corporation, he acknowledged, which was why OpenAI had the second part of its mission: to redistribute the benefits of AGI to everyone. Was there a historical example of a technology’s benefits that had been successfully distributed? I asked. “Well, I actually think that—it’s actually interesting to look even at the internet as an example,” he said, fumbling a bit before settling on his answer. “There’s problems, too, right?” he said as a caveat. “Anytime you have something super transformative, it’s not going to be easy to figure out how to maximize positive, minimize negative. “Fire is another example,” he added. “It’s also got some real drawbacks to it. So we have to figure out how to keep it under control and have shared standards. “Cars are a good example,” he followed. “Lots of people have cars, benefit a lot of people. They have some drawbacks to them as well. They have some externalities that are not necessarily good for the world,” he finished hesitantly. “I guess I just view—the thing we want for AGI is not that different from the positive sides of the internet, positive sides of cars, positive sides of fire. The implementation is very different, though, because it’s a very different type of technology.” His eyes lit up with a new idea. “Just look at utilities. Power companies, electric companies are very centralized entities that provide low‑cost, high‑quality things that meaningfully improve people’s lives.” It was a nice analogy. But Brockman seemed once again unclear about how OpenAI would turn itself into a utility. Perhaps through distributing universal basic income, he wondered aloud, perhaps through something else. He returned to the one thing he knew for certain. OpenAI was committed to redistributing AGI’s benefits and giving everyone economic freedom. “We actually really mean that,” he said. “The way that we think about it is: Technology so far has been something that does rise all the boats, but it has this real concentrating effect,” he said. “AGI could be more extreme. What if all value gets locked up in one place? That is the trajectory we’re on as a society. And we’ve never seen that extreme of it. I don’t think that’s a good world. That’s not a world that I want to sign up for. That’s not a world that I want to help build.” In February 2020, I published my profile for MIT Technology Review, drawing on my observations from my time in the office, nearly three dozen interviews, and a handful of internal documents. “There is a misalignment between what the company publicly espouses and how it operates behind closed doors,” I wrote. “Over time, it has allowed a fierce competitiveness and mounting pressure for ever more funding to erode its founding ideals of transparency, openness, and collaboration.” Hours later, Elon Musk replied to the story with three tweets in rapid succession: “OpenAI should be more open imo” “I have no control & only very limited insight into OpenAI. Confidence in Dario for safety is not high,” he said, referring to Dario Amodei, the director of research. “All orgs developing advanced AI should be regulated, including Tesla” Afterward, Altman sent OpenAI employees an email. “I wanted to share some thoughts about the Tech Review article,” he wrote. “While definitely not catastrophic, it was clearly bad.” It was “a fair criticism,” he said that the piece had identified a disconnect between the perception of OpenAI and its reality. This could be smoothed over not with changes to its internal practices but some tuning of OpenAI’s public messaging. “It’s good, not bad, that we have figured out how to be flexible and adapt,” he said, including restructuring the organization and heightening confidentiality, “in order to achieve our mission as we learn more.” OpenAI should ignore my article for now and, in a few weeks’ time, start underscoring its continued commitment to its original principles under the new transformation. “This may also be a good opportunity to talk about the API as a strategy for openness and benefit sharing,” he added, referring to an application programming interface for delivering OpenAI’s models. “The most serious issue of all, to me,” he continued, “is that someone leaked our internal documents.” They had already opened an investigation and would keep the company updated. He would also suggest that Amodei and Musk meet to work out Musk’s criticism, which was “mild relative to other things he’s said” but still “a bad thing to do.” For the avoidance of any doubt, Amodei’s work and AI safety were critical to the mission, he wrote. “I think we should at some point in the future find a way to publicly defend our team (but not give the press the public fight they’d love right now).” OpenAI wouldn’t speak to me again for three years. From the book , to be published on May 20, 2025, by Penguin Press, an imprint of Penguin Publishing Group, a division of Penguin Random House LLC. Copyright © 2025 by Karen Hao.

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More As enterprises increasingly look to build and deploy generative AI-powered applications and services for internal or external use (employees or customers), one of the toughest questions they face is understanding exactly how well these AI tools are performing out in the wild. In fact, a recent survey by consulting firm McKinsey and Company found that only 27% of 830 respondents said that their enterprises’ reviewed all of the outputs of their generative AI systems before they went out to users. Unless a user actually writes in with a complaint report, how is a company to know if its AI product is behaving as expected and planned? Raindrop, formerly known as Dawn AI, is a new startup tackling the challenge head-on, positioning itself as the first observability platform purpose-built for AI in production, catching errors as they happen and explaining to enterprises what went wrong and why. The goal? Help solve generative AI’s so-called “black box problem.” “AI products fail constantly—in ways both hilarious and terrifying,” wrote co-founder Ben Hylak on X recently, “Regular software throws exceptions. But AI products fail silently.” Raindrop seeks to offer any category-defining tool akin to what observability company Sentry does for traditional software. But while traditional exception tracking tools don’t capture the nuanced misbehaviors of large language models or AI companions, Raindrop attempts to fill the hole. “In traditional software, you have tools like Sentry and Datadog to tell you what’s going wrong in production,” he told VentureBeat in a video call interview last week. “With AI, there was nothing.” Until now — of course. How Raindrop works Raindrop offers a suite of tools that allow teams at enterprises large and small to detect, analyze, and respond to AI issues in real time. The platform sits at the intersection of user interactions and model outputs, analyzing patterns across hundreds of millions of daily events, but doing so with SOC-2 encryption enabled, protecting the data and privacy of users and the company offering the AI solution. “Raindrop sits where the user is,” Hylak explained. “We analyze their messages, plus signals like thumbs up/down, build errors, or whether they deployed the output, to infer what’s actually going wrong.” Raindrop uses a machine learning pipeline that combines LLM-powered summarization with smaller bespoke classifiers optimized for scale. Promotional screenshot of Raindrop’s dashboard. Credit: Raindrop.ai “Our ML pipeline is one of the most complex I’ve seen,” Hylak said. “We use large LLMs for early processing, then train small, efficient models to run at scale on hundreds of millions of events daily.” Customers can track indicators like user frustration, task failures, refusals, and memory lapses. Raindrop uses feedback signals such as thumbs down, user corrections, or follow-up behavior (like failed deployments) to identify issues. Fellow Raindrop co-founder and CEO Zubin Singh Koticha told VentureBeat in the same interview that while many enterprises relied on evaluations, benchmarks, and unit tests for checking the reliability of their AI solutions, there was very little designed to check AI outputs during production. “Imagine in traditional coding if you’re like, ‘Oh, my software passes ten unit tests. It’s great. It’s a robust piece of software.’ That’s obviously not how it works,” Koticha said. “It’s a similar problem we’re trying to solve here, where in production, there isn’t actually a lot that tells you: is it working extremely well? Is it broken or not? And that’s where we fit in.” For enterprises in highly regulated industries or for those seeking additional levels of privacy and control, Raindrop offers Notify, a fully on-premises, privacy-first version of the platform aimed at enterprises with strict data handling requirements. Unlike traditional LLM logging tools, Notify performs redaction both client-side via SDKs and server-side with semantic tools. It stores no persistent data and keeps all processing within the customer’s infrastructure. Raindrop Notify provides daily usage summaries and surfacing of high-signal issues directly within workplace tools like Slack and Teams—without the need for cloud logging or complex DevOps setups. Advanced error identification and precision Identifying errors, especially with AI models, is far from straightforward. “What’s hard in this space is that every AI application is different,” said Hylak. “One customer might build a spreadsheet tool, another an alien companion. What ‘broken’ looks like varies wildly between them.” That variability is why Raindrop’s system adapts to each product individually. Each AI product Raindrop monitors is treated as unique. The platform learns the shape of the data and behavior norms for each deployment, then builds a dynamic issue ontology that evolves over time. “Raindrop learns the data patterns of each product,” Hylak explained. “It starts with a high-level ontology of common AI issues—things like laziness, memory lapses, or user frustration—and then adapts those to each app.” Whether it’s a coding assistant that forgets a variable, an AI alien companion that suddenly refers to itself as a human from the U.S., or even a chatbot that starts randomly bringing up claims of “white genocide” in South Africa, Raindrop aims to surface these issues with actionable context. The notifications are designed to be lightweight and timely. Teams receive Slack or Microsoft Teams alerts when something unusual is detected, complete with suggestions on how to reproduce the problem. Over time, this allows AI developers to fix bugs, refine prompts, or even identify systemic flaws in how their applications respond to users. “We classify millions of messages a day to find issues like broken uploads or user complaints,” said Hylak. “It’s all about surfacing patterns strong and specific enough to warrant a notification.” From Sidekick to Raindrop The company’s origin story is rooted in hands-on experience. Hylak, who previously worked as a human interface designer at visionOS at Apple and avionics software engineering at SpaceX, began exploring AI after encountering GPT-3 in its early days back in 2020. “As soon as I used GPT-3—just a simple text completion—it blew my mind,” he recalled. “I instantly thought, ‘This is going to change how people interact with technology.’” Alongside fellow co-founders Koticha and Alexis Gauba, Hylak initially built Sidekick, a VS Code extension with hundreds of paying users. But building Sidekick revealed a deeper problem: debugging AI products in production was nearly impossible with the tools available. “We started by building AI products, not infrastructure,” Hylak explained. “But pretty quickly, we saw that to grow anything serious, we needed tooling to understand AI behavior—and that tooling didn’t exist.” What started as an annoyance quickly evolved into the core focus. The team pivoted, building out tools to make sense of AI product behavior in real-world settings. In the process, they discovered they weren’t alone. Many AI-native companies lacked visibility into what their users were actually experiencing and why things were breaking. With that, Raindrop was born. Raindrop’s pricing, differentiation and flexibility have attracted a wide range of initial customers Raindrop’s pricing is designed to accommodate teams of various sizes. A Starter plan is available at $65/month, with metered usage pricing. The Pro tier, which includes custom topic tracking, semantic search, and on-prem features, starts at $350/month and requires direct engagement. While observability tools are not new, most existing options were built before the rise of generative AI. Raindrop sets itself apart by being AI-native from the ground up. “Raindrop is AI-native,” Hylak said. “Most observability tools were built for traditional software. They weren’t designed to handle the unpredictability and nuance of LLM behavior in the wild.” This specificity has attracted a growing set of customers, including teams at Clay.com, Tolen, and New Computer. Raindrop’s customers span a wide range of AI verticals—from code generation tools to immersive AI storytelling companions—each requiring different lenses on what “misbehavior” looks like. Born from necessity Raindrop’s rise illustrates how the tools for building AI need to evolve alongside the models themselves. As companies ship more AI-powered features, observability becomes essential—not just to measure performance, but to detect hidden failures before users escalate them. In Hylak’s words, Raindrop is doing for AI what Sentry did for web apps—except the stakes now include hallucinations, refusals, and misaligned intent. With its rebrand and product expansion, Raindrop is betting that the next generation of software observability will be AI-first by design. Daily insights on business use cases with VB Daily If you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI. Read our Privacy Policy Thanks for subscribing. Check out more VB newsletters here. An error occured.

The structural reality behind disappearing entry-level design roles and some practical advice for finding ways inMade by me with Midjourney.I haven’t worked with a junior designer in nearly a decade.Not because I don’t value them, but because the environments I’ve worked in haven’t been set up to support them.Lean teams leave no room for juniorsFor the past eight years, I’ve worked primarily at hyper-scaling, technical, enterprise startups, especially in cybersecurity. These are companies that have found traction and are rapidly scaling to meet aggressive benchmarks in competitive markets. The pressure to deliver quickly and accurately is relentless.In these engineering-driven organizations building products for technical users, design teams typically remain lean: often just 2–3 people. Every designer is expected to lead a pod, own a problem space, and deliver independently from day one.There’s simply no extra bandwidth for mentorship. No built-in scaffolding to help junior designers safely learn and grow. No slack in the system for someone still finding their footing.Even with the best intentions, these teams just aren’t structurally built to support junior growth.My design career started differentlyI’ve seen the flip side of this, too. My first design role was at American Express, on a 20-person team.I got to contribute immediately on small, concrete tasks and learn gradually by osmosis: supporting experienced leads, prototyping their ideas, and building confidence through direct exposure and feedback.That environment was primed to support junior talent, with clear paths for growth and mentorship built into the workflow.But that kind of team is increasingly rare in today’s landscape.It’s structural, not personalIn the industry as a whole, the modern product designer role has evolved into something inherently non-junior. It requires strategic decision-making, confident navigation of business and technical trade-offs, and providing guidance to senior or lead-level stakeholders (most often, a tech lead and product lead).These abilities only develop through direct experience with product development cycles. Experience that juniors, by definition, don’t yet have.Making matters worse: The tactical tasks that juniors traditionally cut their teeth on are increasingly being delegated to AI tools. Tasks that once required a human junior designer with specialized training can now be handled by generative AI tools in a fraction of the time and cost to the organization.This fundamentally changes the entry pathway. When the low-complexity work that helped juniors develop their skills is automated away, we lose the natural onramp that allowed designers to gradually progress from tactical execution to strategic direction.Remote work has further complicated things by removing informal learning opportunities that happen naturally in an in-person work environment, like shadowing senior designers, being in the room for strategy discussions, or casual mentorship chats.What this means if you’re trying to break inIf you’re trying to break into product design today and facing constant rejection:You’re not imagining how hard it is. And it’s not just you.The traditional entry path has largely disappeared. Companies that once hired junior designers now expect more maturity from day one, creating that impossible requirement of “needing experience to get experience.”It’s not all doom and gloom, though. You just need to adjust your approach accordingly for the situation we find ourselves in.Where juniors can startInstead of waiting for the overall market to become junior-friendly again (which I don’t see happening), focus your search on environments more structurally accepting of new talent:Very early-stage startups: Pre-seed or seed companies often have tight budgets and simply need someone enthusiastic who can execute designs. It will be trial-by-fire, but you’ll gain rapid hands-on experience.Stable, established businesses outside of ‘big tech’: Businesses with predictable revenue streams often provide structured environments for junior designers (my early experience at American Express is a prime example). It might not be as glamorous as a ‘big tech’ job, but as a result they’re less competitive while still offering critical experience to get started.Design agencies: Since their business model focuses on selling design services, agencies naturally employ more designers and can support a mix of experience levels. The rapid exposure to multiple projects makes them solid launchpads even if your long-term goal is to work in-house in tech.Finding a side door into designIf direct paths seem closed, you can still find a side door. Sometimes the smartest move isn’t aiming straight for a ‘product designer’ title, but stepping into a role where you can stay close to product and grow into the craft:UI Developer or Front-End Engineer (my path)Graphic or Visual DesignerGrowth or Marketing DesignerProduction-focused design rolesThis is how I did it: I started as a UI developer focused on prototyping, built skills through collaboration, and shifted into product design once I had more experience. Another unintentional benefit of this path for me was that I started out at a higher pay scale due to my coding skills and continued to leverage that into higher pay as a product designer throughout my career.So while pursuing an adjacent role might not be your ideal scenario, I think it’s a completely reasonable stepping stone to build relevant skills while getting close to the product design process. From there, you can gradually take on more responsibilities and switch roles when you can make a stronger case for yourself based on real-world experience.Make your own opportunitiesBeyond targeting the right environments, here are three strategies that helped me break into my roles:Publish relevant work publicly: Make up projects for yourself that are relevant to the kinds of companies you might like to work for and publish them online. This is how I got into advertising without having an advertising degree or any direct experience: I wrote an advertising blog and used that as my ‘portfolio’. For product design these days there are so many ways to share work: Figma links, video recordings, social posts, etc. Use them to your advantage!Position yourself near opportunities: I took a bet on moving to Chicago for my advertising gig, then NYC for my first design role. Being in these creative hubs helped me build my foundational network that I still rely on to this day. While remote work has changed the landscape, proximity to creative communities still creates opportunity for serendipitous connection and relationship building that can transform your career. Once you’re seasoned and sought-after, go wherever you want. But you’d be surprised how often just being physically present opens doors.Become the early expert on new tools: Go deep into emerging tools and methodologies senior leaders may be curious about but not have the bandwidth to explore. I did this earlier in my career, bringing Sketch + Invision to a team when that was cutting-edge (RIP 🪦), then later introducing Figma before it became the industry standard. Now I’m exploring AI prototyping tools as the next generation. Being the team’s expert in new technology creates unique value that transcends experience levels and often positions you as the go-to resource regardless of your seniority or title.Final ThoughtsThe structural shifts outlined here aren’t your fault, and they don’t reflect your potential.If you can accept the reality of today’s landscape, then you can start to adjust your approach and find new paths, even if they look different than what you expected.Take it from me: a liberal arts grad who found my way into advertising, engineering, and design, persistently creating opportunities along the way.Ultimately, your goal isn’t just to land your first product design job; it’s to build valuable skills that compound over time, preparing you to thrive no matter how the market evolves.Good luck out there!Patrick Morgan is the creator of Unknown Arts and lead product designer at Sublime Security. If you enjoyed this post, subscribe to his newsletter and follow him on LinkedIn for weekly insights.No country for Junior Designers was originally published in UX Collective on Medium, where people are continuing the conversation by highlighting and responding to this story.