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In the rapidly evolving landscape of warehouse automation, the integration of Autonomous Mobile Robots (AMRs) and robotic arms is setting new standards for efficiency, precision, and scalability. This synergy is transforming traditional warehousing operations, enabling businesses to meet the increasing demands of modern logistics and manufacturing. Understanding AMR Design and Robotic Arms AMR Design refers to the engineering and development of mobile robots capable of navigating dynamic environments without the need for predefined paths. These robots utilize a combination of sensors, cameras, and advanced algorithms to move autonomously, adapt to obstacles, and optimize their routes in real-time. Robotic arms, on the other hand, are programmable mechanical devices designed to perform tasks such as picking, placing, sorting, and assembling. When integrated with AMRs, these robotic arms can execute precise movements, enhancing the overall automation process. The Role of AMRs in Warehouse Automation AMRs serve as the backbone of modern AMR warehouses, facilitating the transportation of goods across various sections of the facility. Their key advantages include: Flexibility: AMRs can navigate complex layouts and adjust to changes in the environment, making them ideal for dynamic warehouse settings. Scalability: As demand fluctuates, additional AMRs can be deployed without significant infrastructure changes. Efficiency: By automating material transport, AMRs reduce human labor and associated costs, leading to faster order fulfillment. Enhancing Precision with Robotic Arms While AMRs handle the mobility aspect, robotic arms provide the precision required for tasks such as: Picking and Placing: Accurately selecting items from shelves and placing them into bins or packaging. Sorting: Categorizing products based on predefined criteria, such as size, weight, or destination. Assembly: Combining components to create finished products. The integration of robotic arms with AMRs allows for seamless coordination between mobility and precision, streamlining operations and reducing errors. Integration of AMRs and Robotic Arms The combination of AMRs and robotic arms creates a cohesive automation system where: AMRs transport items to designated locations. Robotic arms perform specific tasks on these items, such as picking, sorting, or assembly. Feedback loops ensure continuous optimization of the process, with real-time adjustments based on data analytics. This integrated approach enhances throughput, reduces cycle times, and improves overall warehouse efficiency. Software Solutions for Seamless Integration To maximize the potential of AMRs and robotic arms, advanced software solutions are essential. Blue Sky Robotics offers a suite of tools designed to facilitate this integration: Apollo Spacebar: A no-code control center that allows operators to plan, simulate, and execute automation tasks without programming expertise. It provides a unified interface to manage robots, sensors, and tools, ensuring smooth operations. Leonardough: A synthetic data generation tool that accelerates the training of machine learning models for computer vision, enabling robots to recognize and adapt to new SKUs quickly. Real-Time Analytics Dashboard: Provides live monitoring of robot performance, identifying bottlenecks and areas for improvement, leading to data-driven decision-making. These software solutions ensure that the integration of AMRs and robotic arms is not only seamless but also adaptable to changing operational needs. Case Studies and Real-World Applications Several industries have successfully implemented AMR and robotic arm integrations: E-commerce Fulfillment Centers: By automating picking and sorting processes, these centers have reduced order fulfillment times and increased accuracy. Manufacturing Plants: The combination of AMRs and robotic arms has streamlined assembly lines, improving production rates and product quality. Retail Distribution Hubs: Enhanced material handling capabilities have led to better inventory management and faster restocking processes. These case studies demonstrate the tangible benefits of integrating AMRs and robotic arms in warehouse automation. The fusion of AMR design and robotic arms is ushering in a new era of warehouse automation. By leveraging the strengths of both technologies, businesses can achieve greater efficiency, precision, and scalability in their operations. As the demand for faster and more accurate order fulfillment continues to rise, embracing this integrated approach will be crucial for staying competitive in the modern logistics landscape. Schedule a demo today for more information on how Blue Sky Robotics can assist in implementing these solutions, visit Blue Sky Robotics.

As manufacturing facilities face increasing pressure to automate while maintaining strict safety standards, the demand for robots capable of operating in hazardous environments continues to grow. One example of this specialized technology is the Fairino FR20 , a collaborative robot capable of withstanding hazardous environments where traditional automation falls short. Fairino markets their arms for welding, palletizing, and more. However, we have added one more special skill: painting. Designed to handle volatile solvents and flammable paint materials safely, the Fairino FR20 represents a significant leap forward in automatic paint robot technology . Where conventional robots often require extensive safety modifications or simply can't operate in classified hazardous locations, this purpose-built system brings both precision and compliance to high-risk coating operations. The Challenge of Automation in Hazardous Environments Traditional robotic painting systems may deliver excellent results in controlled settings, but they often fail when safety becomes paramount. Paint booths filled with solvent vapors, automotive finishing lines with flammable coatings, and aerospace manufacturing facilities all present explosion risks that standard industrial robots weren't designed to handle. The challenge is particularly acute in applications involving: High-volume solvent-based paint application Powder coating with combustible dust Aerospace and defense coating operations Chemical processing equipment finishing These environments demand more than just painting accuracy ; they require certified explosion-proof construction that prevents any possibility of ignition. That's precisely where the Fairino FR20 excels, offering intrinsically safe operation without compromising performance. What Makes a Robot "Explosion-Proof"? Explosion-proof robots are fundamentally different from standard industrial robots. They incorporate specialized design principles including: Sealed Enclosures : Electrical components are housed in sealed enclosures that prevent flammable gases or vapors from entering, even under fault conditions. Intrinsically Safe Electronics : Electrical systems are designed to limit energy levels below what could cause ignition, even if components fail. Temperature Control : All surfaces are engineered to remain below ignition temperatures of the surrounding atmosphere. Non-Sparking Materials : External components use materials that won't generate sparks through friction or impact. These features allow robots to operate legally and safely in classified hazardous locations where standard automation would be prohibited or require prohibitively expensive modifications. The Fairino FR20: Specifications and Capabilities The Fairino FR20 is a 6-axis collaborative robot designed for industrial applications. Key specifications include: Payload Capacity : 20 kg Reach : 1854 mm Repeatability : ±0.1 mm The FR20 is primarily marketed for applications including palletizing operations, pick-and-place tasks, and dispensing applications. Notably, Fairino offers an explosion-proof variant of the FR20 for use in hazardous environments where standard robots cannot operate. Applications for Explosion-Proof Robotics in Painting The explosion proof painting robot capabilities of the Fairino FR20 have opened doors to automation in sectors previously dependent on manual labor. Current deployments span multiple industries: Automotive Manufacturing : High-volume vehicle painting operations benefit from the FR20's ability to apply primers, basecoats, and clearcoats with consistent film thickness while operating safely in solvent-rich environments. The robot's repeatability ensures uniform coverage across complex vehicle geometries , eliminating the quality variations inherent in manual spraying. Aerospace and Defense : Aircraft component finishing requires both precision and compliance with strict safety protocols. The Fairino FR20 handles specialized aerospace coatings, including primers and topcoats with hazardous volatile organic compounds, while maintaining the explosion-proof certification required in these facilities. Industrial Equipment Coating : Manufacturers of heavy machinery, agricultural equipment, and industrial components use the FR20 for applying protective coatings that involve flammable solvents. The robot's reach and flexibility allow it to coat complex geometries that would challenge human painters while maintaining a safe working environment. Chemical and Petrochemical Facilities : Equipment maintenance and coating operations in refineries and chemical plants demand explosion-proof automation. The FR20 enables these facilities to modernize their coating processes without compromising safety standards. Investment Considerations and Total Cost of Ownership When evaluating robotic painting systems for hazardous environments, the Fairino FR20 price reflects its specialized engineering and safety certifications. While exact pricing varies based on configuration, end effector selection, and integration requirements, explosion-proof painting robots typically represent a significant capital investment compared to standard industrial robots. However, the total cost equation extends well beyond initial purchase price: Labor Cost Reduction : A single FR20 can replace multiple shift workers while eliminating overtime costs and reducing worker exposure to hazardous materials. Material Savings : Precise application reduces paint waste, and for expensive specialty coatings, this can represent substantial ongoing savings. Quality Improvement : Consistent coating thickness and coverage reduce rework and warranty claims, particularly valuable in industries like automotive and aerospace where quality standards are stringent. Compliance and Safety : The explosion-proof certification eliminates the need for extensive booth modifications or additional safety equipment that would be required to safely use standard robots in hazardous locations. Reduced Insurance Costs : Automating hazardous painting operations can lower workplace safety insurance premiums while reducing the risk of catastrophic incidents. Facilities evaluating the Fairino FR20 price should conduct a comprehensive ROI analysis that includes these factors alongside the initial capital outlay.  Conclusion The Fairino FR20 stands out in the landscape of robotic painting systems by addressing a critical need: safe, reliable automation in hazardous coating environments. Its explosion-proof certification allows facilities to modernize painting operations without compromising safety standards, while its performance characteristics deliver the quality and efficiency gains that justify the investment. For manufacturers evaluating automatic paint robot solutions, particularly those working with flammable coatings or operating in classified hazardous locations, the FR20 offers a path forward. It bridges the gap between safety requirements and automation benefits, proving that even the most challenging industrial environments can benefit from robotic precision and reliability.

In modern automation, vacuum grippers have become a cornerstone of precision handling for a wide range of industries, from electronics assembly to logistics fulfillment. These tools rely on sophisticated feedback systems to pick, place, and manipulate components without damage. However, the reliability of these operations hinges on one often-overlooked component: robot vacuum sensors. When these sensors fail, the consequences can ripple across production lines, causing costly downtime, damaged products, and eroded customer trust. Understanding Vacuum Gripper Sensors Robot vacuum grippers operate using suction to securely hold objects, but the act of gripping is only half the story. Sensors integrated into these grippers provide critical real-time feedback, detecting whether a part has been successfully picked or if a vacuum has been compromised. Common sensor types include pressure sensors, flow sensors, vacuum sensors, and proximity detectors. Each plays a distinct role, whether monitoring air pressure to confirm a seal or detecting the presence of an object on the gripper. By capturing precise data about part handling, these devices ensure consistent performance and allow robotic systems to adapt dynamically to variations in part shape, weight, or surface texture. In high-throughput operations, even minor lapses in sensor accuracy can quickly escalate into major operational bottlenecks. Common Failure Modes Despite their critical role, robotic sensors are prone to failure if not properly managed. Some of the most frequent issues include: Misalignment or contamination: Dust, debris, or slight misalignments can prevent sensors from detecting parts accurately. Pressure drops and leaks: Even small leaks in the vacuum system can compromise the gripper’s ability to hold items securely. Inconsistent detection: Variations in object material or texture may confuse sensors, leading to dropped parts or missed picks. Environmental factors: Extreme temperatures, humidity, or exposure to chemicals can degrade sensor performance over time. Understanding these failure modes is crucial for designing systems that are robust, resilient, and capable of maintaining high uptime. Operational Pain Points Failures in robot vacuum sensors often translate directly into operational headaches. Misplaced or dropped items can disrupt the flow of an assembly line, reducing throughput and increasing labor costs. In industries where quality and precision are paramount, such as electronics or medical device manufacturing, even a single mispick can result in scrapped components or delayed shipments. Maintenance challenges further compound the problem. Reactive approaches, waiting for a sensor to fail before addressing the issue, often result in unplanned downtime, emergency service calls, and an unpredictable maintenance budget. For executives and operations managers, these challenges highlight the hidden cost of insufficient sensor monitoring in robotic end effectors. Best Practices to Avoid Failure Mitigating these risks requires a proactive approach that blends proper technology selection, operational diligence, and forward-looking system design. Key strategies include: Select the right sensors for the application: Not all vacuum sensors are created equal. Evaluate factors such as sensitivity, environmental resistance, and compatibility with the specific robot EOAT (end-of-arm tooling) in use. Regular calibration and maintenance: Scheduled inspections and calibrations help ensure sensors continue to provide accurate feedback, reducing the risk of unnoticed degradation. Predictive maintenance integration: Leveraging robotic sensors to monitor system health can alert operators to potential failures before they impact production. Redundancy in critical applications: Using multiple sensors or cross-referencing data from different types of detectors can provide an extra layer of reliability, particularly in high-mix, high-precision environments. These practices not only extend the lifespan of the vacuum gripper system but also safeguard the efficiency and consistency of the entire automated process. Emerging Trends and Technology Enhancements The field of robot end effectors is evolving rapidly, with innovations designed to further reduce sensor failure and improve operational intelligence. Smart sensors equipped with AI and machine learning algorithms can adjust vacuum strength in real-time based on the object’s properties. IoT-enabled sensors allow for remote monitoring, providing actionable insights on system performance across multiple production lines. Additionally, advanced materials and coatings enhance sensor durability, making them less susceptible to environmental degradation. These technological enhancements reinforce the importance of considering sensor selection and integration as part of a holistic automation strategy rather than an afterthought. Conclusion Robot vacuum sensors are not just technical components, they are critical enablers of operational excellence in automated systems. Failures in these sensors can lead to costly production downtime, quality issues, and maintenance headaches. By understanding common failure modes, implementing rigorous maintenance routines, and embracing emerging sensor technologies, businesses can dramatically reduce risk while maximizing efficiency and ROI. For executives and engineers alike, the message is clear: investing in robust robotic sensors and ensuring their optimal integration into vacuum grippers is essential for sustaining high-performance automation in today’s competitive manufacturing and fulfillment environments. 👉 Want to learn more? Reach out to our team today.

High-mix manufacturing presents one of the most persistent challenges for automation engineers and operations leaders: balancing flexibility with consistent quality. In environments where product variants, part geometries, and packaging formats change daily, conventional automation strategies often fail to deliver the promised efficiency gains. Success no longer hinges solely on adding robotic arms or conveyor belts; it depends on integrating adaptive software, vision-driven inspection systems, and intelligent orchestration to build an automated assembly line capable of adjusting on the fly. High-Mix Assembly: Why Traditional Automation Struggles Traditional assembly line automation has long been optimized for repetitive, high-volume production. In a high-mix scenario, this rigidity creates multiple failure points: Frequent Changeovers: Each new SKU requires reprogramming robots, recalibrating vision systems, or even redesigning fixtures, leading to costly downtime. Vision Limitations: Static computer vision models struggle with variations in surface finish, lighting, or orientation, increasing the risk of defective products passing through inspection. Data Silos: Disconnected robotic, inspection, and MES data prevent real-time decision-making, leaving inefficiencies unidentified until it’s too late. Reactive Quality Measures: Many operations rely on end-of-line inspection rather than continuous monitoring, which can result in cascading defects across production batches. In short, high-mix production exposes the gaps in traditional automation, where fixed systems simply cannot adapt quickly enough to changing conditions. Reimagining Vision Inspection for High-Mix Environments At the core of resilient high-mix automation is vision inspection that adapts. Rather than relying on pre-trained, static algorithms, leading manufacturers now employ AI-driven vision systems capable of: Learning new part geometries or packaging types without extensive manual retraining. Adjusting to variations in lighting, surface reflectivity, and position automatically. Detecting subtle defects and deviations that traditional threshold-based systems often miss. A prime example comes from Blue Sky Robotics, whose Leonardough synthetic data pipeline accelerates vision model training. By generating realistic, labeled datasets for new SKUs, operators can deploy vision-guided inspection across diverse products without halting production. This approach addresses one of the biggest pain points in high-mix assembly: time-intensive vision calibration and retraining. Adaptive Robotics & Orchestration: Beyond Task-Level Automation Modern automated assembly lines no longer operate as isolated task machines. Instead, they function as coordinated, software-driven systems: No-Code Orchestration Platforms: Tools like Blue Sky Robotics’ Apollo Spacebar enable engineers to design, simulate, and adjust workflows without manual coding. This dramatically reduces downtime during product changeovers and eliminates the need for specialized robotics engineers for routine adjustments. Integrated Data Streams: By unifying robotic motions, vision feedback, and MES data, the system gains holistic situational awareness, allowing it to dynamically reroute tasks, adjust robot speed, or reprioritize inspection sequences in real time. Scalable Flexibility: Modular workflow design ensures that introducing a new product or SKU doesn’t require a full system overhaul. Instead, operators modify digital recipes, preconfigured instructions that robots and vision systems execute seamlessly. By moving from task-level automation to intelligent orchestration, manufacturers can mitigate the primary risks in high-mix environments: downtime, errors, and variability. Predictive Quality: Shifting From Reactive to Proactive High-mix production magnifies the consequences of minor deviations. To maintain consistent quality, the automated assembly line must operate with predictive awareness: Sensor Fusion: Combining vision with torque, force, and positional sensors allows real-time detection of assembly drift before defective parts accumulate. Continuous Monitoring: Instead of post-process inspection, the system evaluates quality during every step, flagging anomalies immediately. Data-Driven Insights: Advanced dashboards aggregate operational and quality data, providing actionable insights for process optimization and preventive maintenance. These predictive capabilities reduce scrap, rework, and line stoppages, critical for high-mix manufacturers seeking operational reliability. Strategic Advantages of Assembly Line Automation in High-Mix Operations When executed correctly, assembly line automation in high-mix environments offers more than efficiency gains. It delivers strategic value: Rapid Changeovers: No-code orchestration and adaptive vision systems reduce setup times between product variants. Higher First-Pass Yield: Continuous inspection and real-time feedback prevent defects from propagating. Operational Agility: Integrated automation platforms allow manufacturers to respond to sudden changes in demand or design without interrupting production. Reduced Engineering Overhead: Modular workflows and synthetic vision data eliminate many manual programming tasks. Competitive Differentiation: Manufacturers capable of reliably producing diverse product lines faster and with lower error rates gain a clear edge in client retention and market responsiveness. Implementation Considerations Transitioning to a high-mix automated assembly line requires a strategic approach: Assess Bottlenecks: Identify where manual intervention or error-prone steps exist in the current line. Select Adaptive Software: Prioritize platforms that support no-code workflow orchestration and synthetic vision training. Integrate Systems: Ensure robots, vision inspection units, and MES are fully interoperable. Train Operators: Equip staff to manage adaptive workflows, analyze system dashboards, and intervene strategically rather than manually perform routine tasks. Monitor ROI Beyond Labor Savings: Evaluate efficiency in terms of throughput elasticity, first-pass yield, and speed of product changeover, metrics that truly matter in high-mix contexts. Building Resilient, High-Mix Automated Assembly Lines In high-mix manufacturing, success hinges on flexible, intelligent automation rather than brute-force robotics. By combining adaptive vision inspection, modular workflow orchestration, and predictive quality monitoring, manufacturers can minimize downtime, maintain first-pass yield, and respond quickly to evolving product demands. Blue Sky Robotics’ automation platforms, including Apollo Spacebar and Leonardough, exemplify this next generation of assembly line automation, enabling manufacturers to scale without compromising precision. The bottom line: in high-mix environments, a rigid, static automated assembly line is a liability. To avoid failure and maintain competitive advantage, manufacturers must invest in systems that learn, adapt, and orchestrate themselves, turning automation from a cost-saving tool into a strategic enabler of operational intelligence. 👉 Want to learn more? Reach out to our engineering team today.

The smart warehouse conversation has evolved. Five years ago, automation meant adding a few robots to fill labor gaps or handle repetitive work. Today, the leaders in logistics know that true warehouse intelligence isn’t about isolated hardware, it’s about integrating robotics, perception systems, and automation software into a unified, adaptive operation. The challenge isn’t whether to automate, it’s how to align automation with fluctuating demand, SKU diversity, and complex fulfillment models. Why Traditional Automation Is No Longer Enough Conventional warehouse automation, fixed conveyors, static picking robots, and rule-based systems, worked when product mixes were stable. But in today’s fulfillment cycle, demand variability has rendered rigid systems inefficient. Warehouse owners face issues like: High-mix, low-volume operations: Traditional robots struggle with irregular items or packaging variations. Seasonal labor spikes: Even semi-automated sites can’t ramp up fast enough when workflows depend on reprogramming. Data silos: Robotics data, WMS data, and ERP data often sit in disconnected layers, preventing meaningful optimization. Engineering bottlenecks: Deploying or adjusting robotic workflows requires specialists, slowing change management. The modern smart warehouse addresses these barriers not just with machines, but with data orchestration, adaptive software, and vision-driven intelligence. Smart Warehouse Systems: From Task Automation to Flow Orchestration A truly smart warehouse isn’t defined by how many robots it uses, but by how those robots make decisions. The most advanced systems today leverage AI-enabled orchestration layers that dynamically synchronize every process, from inbound pallet receiving to outbound labeling. For example, Blue Sky Robotics’ Apollo Spacebar platform acts as a no-code “control layer” that sits between robotics hardware and human operators. It translates complex workflows into modular digital “recipes.” The system can be updated in minutes, not weeks, without interrupting production. That orchestration layer becomes the foundation for operational agility. When a new SKU, packaging format, or client account is introduced, the system automatically adjusts robotic logic, camera models, and routing behavior in real time. AI Vision and Synthetic Data: The Key to Handling High-Mix Workloads Warehouse owners who have experimented with robotic picking know the Achilles’ heel of most systems: computer vision training. Conventional models require thousands of labeled images per SKU, a bottleneck that delays deployment and limits flexibility. Blue Sky Robotics’ Leonardough tool sidesteps this by generating synthetic datasets that mimic real-world packaging and lighting conditions. The result is a continuously learning system where new SKUs can be onboarded within hours instead of days. This synthetic vision pipeline represents a turning point for 3PL automation, where product variation is constant and human reprogramming simply can’t keep up. By abstracting vision training into a software layer, smart warehouses can finally achieve the SKU-agnostic automation that was once theoretical. The Role of Software-Defined Automation In the era of software-defined warehousing, hardware is commoditized, the differentiation now comes from the intelligence coordinating it. Automation platforms equipped with digital twins and real-time analytics dashboards now allow operators to: Simulate the effect of new layouts before physically moving anything. Test robot path planning and throughput scenarios in a virtual replica of the warehouse. Visualize congestion points and workflow imbalance across shifts using heat maps. This data-centric approach transforms decision-making. Instead of reactive troubleshooting, managers can predict where inefficiencies will emerge and adjust proactively, rerouting robots, modifying task assignments, or reprioritizing orders algorithmically. Strategic Value of 3PL Automation For 3PLs, automation isn’t just an operational upgrade, it’s a business model shift. Smart 3PLs now use automation as a service differentiator, offering clients guaranteed SLAs, real-time order visibility, and traceability reporting through shared dashboards. Robotics gives them elastic capacity, but it’s the orchestration software that provides commercial flexibility, allowing them to onboard new customers without custom engineering. In practice, this means a 3PL can adjust layout logic or order profiles dynamically across clients, something impossible with fixed legacy systems. The combination of adaptive robotics and AI-based orchestration creates a scalable, customer-responsive fulfillment model that drives both efficiency and retention. Top Warehouse Automation Robots, and Why “Top” Means Different Things Now When people talk about the top warehouse automation robots, they often focus on specs, payload, speed, navigation method. But for advanced operators, the “best” robot is the one that can integrate seamlessly into a software-defined ecosystem. Today’s elite systems include: Collaborative arms with AI vision that adapt to irregular items in mixed bins. AMRs (Autonomous Mobile Robots) that re-map their routes on the fly when congestion patterns change. Dynamic palletizing systems that learn stacking sequences through reinforcement learning. Vision-guided inspection units that perform automated quality checks before packaging. What sets the leaders apart is interoperability, the ability for each unit to communicate through the same orchestration framework, using shared APIs and live data feedback loops. Measuring ROI: From Efficiency Metrics to Flow Intelligence Warehouse owners evaluating ROI on automation should move beyond labor reduction metrics. A mature smart warehouse delivers compounding returns through: Throughput elasticity: Scaling output without physical expansion. Data fidelity: Real-time visibility that prevents costly overstock or mispicks. Cycle time compression: The ability to adapt daily to SKU volatility. Engineering efficiency: Reducing dependency on external integrators by empowering operations teams with no-code control. In essence, the ROI is operational sovereignty, owning your ability to adapt. Looking Ahead: The Warehouse as a Living System The next generation of automation will treat the warehouse less like a fixed facility and more like a living, learning organism. Systems will anticipate rather than respond, adapting workflows in real time based on sensor data, demand forecasts, and even worker behavior. The pioneers in this space are already merging machine learning, synthetic vision, and no-code orchestration into cohesive ecosystems that learn, optimize, and self-correct. For warehouse leaders, the question isn’t whether the technology is ready, it’s whether your operations are ready to operate as an intelligent system, not a static process. From Automation to Intelligence A smart warehouse doesn’t just automate labor; it automates decision-making. By moving beyond task-level robotics to software-defined orchestration, warehouse operators gain a level of adaptability and insight that manual systems simply can’t deliver. Whether through 3PL automation or in-house modernization, the smartest move is investing in platforms that connect, not just perform. Those who do will lead the next phase of intelligent, data-driven logistics. 👉 Want to learn more? Reach out to our engineering team today.

In today’s manufacturing landscape, companies face constant pressure to boost productivity, cut costs, and maintain high quality. Fairino robots provide a powerful solution by integrating seamlessly into production lines to enhance speed, precision, safety, and intelligent decision-making through advanced AI. This article explores the benefits, applications, and real-world case studies of Fairino robots in modernizing manufacturing across industries such as automotive, aerospace, electronics, and food and beverage production . With collaborative capabilities, precision control, and full Industry 4.0 connectivity, Fairino robots are redefining manufacturing efficiency and transforming production operations. What Are the Key Benefits of Using Fairino Robots in Manufacturing? Fairino robots deliver transformative benefits by boosting productivity, reducing costs, and ensuring high product quality. How Do Fairino Robots Increase Production Speed and Reduce Downtime? Fairino robots operate continuously with minimal disruptions. Their rapid cycle times and predictive maintenance—enabled by advanced sensors and machine learning—reduce cycle times to allowing manufacturers to produce more within the same timeframe. The integration of simulation tools and real-time analytics optimizes their utilization, while quick adaptation and reprogramming allow production lines to switch between products efficiently, reducing changeover times and downtime. In What Ways Do Fairino Robots Help Reduce Manufacturing Costs? These robots lower labor costs by automating repetitive tasks, allow better resource management, and improve energy use efficiency. Their precise operations reduce material waste, scrap, and rework expenses. Predictive maintenance prevents unexpected breakdowns, while enhanced supply chain integration and ERP connectivity optimize material handling and planning—ultimately decreasing direct production costs and energy consumption. How Do Fairino Robots Improve Product Quality and Consistency? Fairino robots use high-resolution sensors, vision systems, and feedback loops to maintain consistent accuracy. In welding, for example, robotic arms deliver precise, repeatable welds that improve structural integrity and minimize variability. Similarly, in automated painting, these robots ensure uniform coating and prevent overspray. Real-time quality data allows manufacturers to promptly address issues, resulting in products that consistently meet high quality standards. How Do Fairino Robots Enhance Worker Safety in Manufacturing Environments? Automating dangerous processes—such as heavy lifting, high-temperature operations, and handling hazardous materials—minimizes workplace injuries. Collaborative robots ( cobot-capable ) work safely alongside human operators thanks to sensors, force and torque limiters, emergency stops, and real-time monitoring. These safety measures reduce workplace accidents, liability risks, and regulatory non-compliance, creating a safer and more efficient production environment. What Types of Fairino Robots Are Used to Boost Manufacturing Efficiency? Manufacturers benefit from various Fairino robots including collaborative robots, robotic arms, and automated guided vehicles (AGVs)/autonomous mobile robots (AMRs), each designed for specific operational needs. What Are Fairino Cobots and How Do They Collaborate With Human Workers? Fairino cobot-capable are engineered to operate in shared workspaces alongside human operators. They perform repetitive tasks with precision while their intuitive programming and safety features, powered by advanced sensors and computer vision, enable them to detect human presence and adjust operations accordingly. These cobots help create flexible work environments where human workers can focus on problem-solving and quality assurance, leading to productivity increases. How Do Fairino Robotic Arms Improve Precision in Manufacturing Tasks? Designed for tasks such as welding, assembly, and component placement, Fairino robotic arms offer multi-axis control and fine manipulation capabilities. Their real-time feedback systems and high-speed processors ensure movements are executed within strict tolerances, reducing rework and material waste. In sectors like automotive manufacturing, these arms provide the repeatability and accuracy necessary to maintain high quality standards. How Do Fairino AGVs and AMRs Streamline Material Handling Processes? Fairino’s AGVs and AMRs are built for efficient material transport across the factory floor. They automate the movement of materials and finished goods, reducing human error and delays. Advanced mapping, navigation, real-time tracking, and routing technologies enable these vehicles to navigate dynamic environments, thereby cutting material transport time and improving overall operational efficiency while also enhancing worker safety. How Are Fairino Robots Applied in Specific Manufacturing Processes? Fairino robots are customized for various manufacturing processes such as welding, painting, assembly, and logistics—each benefitting from their speed, precision, and reliability. How Do Fairino Robots Automate Welding for Increased Efficiency and Quality? In welding applications, Fairino Robots deliver precise and repeatable welds by using advanced sensors and pre-programmed trajectories to maintain optimal welding parameters. This results in reduced rework rates and increased production speeds. Laser-guided calibration enhances seam accuracy while reducing operator exposure to harsh conditions, thereby ensuring quality and safety in compliance with regulatory standards. What Advantages Do Fairino Robots Offer in Automated Painting? In automated painting, Fairino Robots control spray patterns and application pressure to produce an even finish. Their advanced vision systems and real-time feedback adjust to varying surface conditions, minimizing overspray and material waste. This control results in consistent, high-quality finishes and improved production efficiency, which directly translates into lower costs and environmental benefits. How Do Fairino Robots Accelerate Assembly Lines and Reduce Errors? By automating assembly tasks—such as inserting, fastening, and aligning components—Fairino robots reduce human error and operator fatigue. Their precise programming ensures uniform and accurate assemblies, leading to faster throughput, fewer defects, and improved customer satisfaction. Their modular design also allows for quick reconfiguration to adapt to new product designs, further enhancing operational flexibility. How Do Fairino Robots Optimize Material Handling and Logistics? Integrated AGVs and AMRs streamline material handling by autonomously transporting items from storage to production lines. They reduce reliance on manual labor, cut down on transportation delays, and minimize workplace injuries. Smart routing and real-time tracking improve inventory management, reducing production cycle times and supporting seamless enterprise system integration. Fairino Cobots: Flexible Automation for High-Mix Environments Fairino robotic arms are designed for flexible automation—the ability to adapt quickly to changing production needs. Unlike traditional industrial robots that require extensive reprogramming and fixed setups, Fairino Cobots thrive in high-mix, low-volume manufacturing, where products and tasks change frequently. On modern production lines, especially those serving industries like electronics, aerospace, and consumer goods, manufacturers often face smaller batch sizes, rapid product cycles, and unexpected demand shifts. Fairino Cobots address these challenges by offering: Simple programming interfaces  – Operators can reprogram tasks in minutes with intuitive drag-and-drop controls, no coding required. Adaptive safety features  – Force and torque sensors , real-time monitoring, and built-in collision detection allow safe operation beside human workers, even in tight workspaces. Rapid task switching  – A single cobot-capable arm can move from assembly in the morning to packaging in the afternoon, eliminating downtime between jobs. Productivity boosts  – By automating repetitive work and reducing changeover times, cobots keep production lines running at peak efficiency. This flexibility makes Fairino robotic arms especially valuable in high-mix environments, where customization, speed, and agility are essential. Businesses can scale their automation strategy without committing to rigid, single-purpose systems—making cobots a practical entry point into Industry 4.0 smart manufacturing. What Role Do Fairino Robots Play in Industry 4.0 Smart Factories? In Industry 4.0 environments, Fairino robots are integral nodes of a fully connected manufacturing ecosystem. They communicate via standardized protocols with ERP systems, data analytics platforms, and other machines. This connectivity enables real-time performance monitoring, adaptive control, and predictive maintenance, substantially reducing downtime and optimizing production workflows. Additionally, they support the digital twin concept by feeding data into simulation models for performance optimization. How Does AI and Machine Learning Enhance Fairino Robot Performance? Fairino Robots incorporate AI and machine learning to continuously improve their operational performance. These technologies enable the robots to learn from previous tasks, adjust operating speeds, and predict potential mechanical issues proactively. Advanced computer vision aids in defect detection, ensuring precision and reliability. Over time, this self-improving capability leads to enhanced productivity and better manufacturing outcomes. What Are the Benefits of Robotics-as-a-Service (RaaS) Models With Fairino? The RaaS model offered by Fairino allows manufacturers to adopt automation without large upfront investments by shifting costs to a subscription or usage basis. This model provides flexibility for technology upgrades, improves budgeting accuracy, and simplifies maintenance as service providers handle updates and technical support. RaaS accelerates technology adoption and aligns closely with dynamic business growth strategies. AGVs and AMRs: Smarter Material Handling Fairino’s AGVs and AMRs streamline material flows across factories and warehouses by: Automating transport of raw materials and finished goods Cutting transport time Navigating dynamic environments with advanced mapping and routing Reducing injuries linked to manual handling These tools keep production lines and logistics chains running smoothly. Which Industries Benefit Most From Fairino Robots Improving Manufacturing Efficiency? Fairino Robots are adaptable across a broad range of industries, each benefiting from tailored automation solutions. How Do Fairino Robots Enhance Efficiency in the Automotive Industry? In automotive manufacturing, Fairino robots automate assembly, painting, and welding, delivering tighter tolerances and higher quality. They help scale production quickly while reducing human error and bottlenecks, thus shortening time-to-market for new models. Enhanced quality control features further reduce rework, scrap, and warranty claims, providing a substantial competitive edge. What Are the Uses of Fairino Robots in Aerospace Manufacturing? Aerospace manufacturing demands precision and reliability. Fairino robots perform drilling, riveting, and assembly tasks with high repeatability, essential for meeting stringent aerospace standards. Their ability to work in cleanroom environments and handle delicate materials ensures compliance and drives cost efficiencies through optimized robotic performance. How Are Fairino Robots Applied in Electronics Manufacturing? In electronics, Fairino robots automate soldering, assembly, and testing, ensuring the careful handling of sensitive components. Their precision enhances yield, reduces errors, and supports high-speed production cycles. They also perform quality inspections effectively, helping manufacturers avoid costly recalls and maintain consumer trust. How Do Fairino Robots Improve Food & Beverage Production Processes? Manufacturers in the food and beverage industry use Fairino robots for packaging, sorting, and palletizing, ensuring swift, hygienic, and consistent processing. By automating routine tasks, these robots reduce contamination risks, minimize waste, and improve inventory management—all critical for meeting stringent food safety standards and boosting overall efficiency. Frequently Asked Questions Q: How quickly can Fairino robots be integrated into existing manufacturing lines? A: Fairino robots are designed for seamless integration, with most installations completed within a few weeks depending on system complexity. Q: Are Fairino robots adaptable for both small and large-scale manufacturing operations? A: Yes, they are scalable and can be customized to suit any production size, from small workshops to large factories. Q: What safety measures are integrated into Fairino’s collaborative robots? A: Their advanced safety features include collision detection, force and torque limits, and real-time monitoring to ensure safe human-robot collaboration. Q: How do Fairino robots support Industry 4.0 initiatives? A: By integrating with IoT devices, ERP systems, and advanced analytics platforms, they enable real-time data sharing and predictive maintenance. Q: Can Fairino robots be upgraded with new technology over time? A: Absolutely. They are designed for easy software and hardware upgrades to incorporate enhancements such as improved AI and sensors. Q: What industries have reported the greatest benefits from using Fairino Robots? A: Automotive, aerospace, electronics, and food & beverage sectors report significant improvements in efficiency, quality, and cost reduction. Final Thoughts Fairino robots are redefining manufacturing by automating complex processes while enhancing speed, consistency, and safety. Their integration with advanced robotics, AI, and Industry 4.0 connectivity creates a competitive edge that not only lowers production costs but also improves product quality. Businesses adopting these transformative technologies position themselves for sustainable growth and innovation in modern manufacturing. As technology evolves, incorporating Fairino Robots will be essential for achieving operational excellence and a robust return on investment.

When you think of a “robot,” you might imagine huge machines locked behind cages on factory floors. But a new generation is changing that picture. Collaborative robots (cobots) are designed to work side by side with people—safely, flexibly, and efficiently. What Makes Cobot-Capable Robots Different? Safe to work with  – No cages required, designed for human collaboration Perfect for repetitive tasks  – High-volume, low-complexity work Lower cost to deploy  – Fewer safety barriers and infrastructure changes Cobots are also flexible automation tools. They can be reprogrammed in minutes without coding. A single robot might assemble parts in the morning and sort packages in the afternoon. Intelligence Meets Accessibility Modern cobots use AI and computer vision to: Detect and identify objects with cameras and sensors Adapt to changes in position in real time Learn new tasks without advanced programming With drag-and-drop style programming, even non-technical staff can train and operate them. This makes automation accessible to businesses of any size. Why Cobot-Capable Robots Make Business Sense Cost-efficient:  Lower setup costs and reduced repetitive labor Precise:  Sub-millimeter accuracy for delicate work Scalable:  One robot can handle multiple roles across shifts Cobots deliver precision—down to 0.1 millimeters—for tasks like circuit board placement or consistent packaging, without fatigue or errors. Real-World Applications Warehouses:  Sorting and packaging Labs:  Handling delicate samples Manufacturing:  Repetitive assembly and finishing tasks With the right end-effectors—grippers, suction cups, or claws—cobots can adapt to almost any job. Some can even pick up fragile items like eggs without breaking them. Human Oversight Remains Essential While cobots are becoming smarter, experts—including OpenAI researchers—have found that automation always needs some level of human oversight. Here’s why: Error detection:  AI systems may miss rare or unexpected conditions that humans can catch. Safety checks:  Even when cobots operate safely, people must confirm that new tasks won’t create hazards. Quality control:  Human inspectors ensure outputs meet exacting standards before products reach customers. Ethical judgment:  Certain decisions—like how automation impacts jobs or sensitive environments—require human responsibility. In practice, this oversight often looks like: Operators reviewing AI-generated paths before robots run them Spot checks on finished products for quality assurance Human intervention when sensors provide conflicting data Rather than replacing humans, cobots shift the role of workers—from repetitive labor to supervisors, trainers, and problem-solvers. The Future of Flexible Automation Cobot-capable robots aren’t replacing people. They’re enhancing human work by improving safety, productivity, and adaptability. The future of work isn’t humans versus  robots—it’s humans and robots working together. And that future is already here.

Tony, Steven, and Bhargav for Beyond the Bot Episode 2 In this episode of Beyond the Bot , Tony DeHart and Steven King sit down with Bhargav Bompalli, the senior Computer Vision and Robotics Engineer at Blue Sky Robotics, to analyze the recent unveiling of Figure's humanoid robot powered by the Helix AI model. The discussion dives deep into the technical and societal implications of this advancement in robotics, covering everything from vision-language-action (VLA) models to human-robot interaction , and the training infrastructure required to bring such a machine to life. This conversation provides a practical and forward-thinking lens on how humanoid robots, though not always the most efficient solution, offer a flexible, scalable toolset for future automation. The team explores how Helix’s twin-model architecture, its massive training corpus, and intuitive task reasoning set a new benchmark in robotics. If you're curious about where human-like robots are headed and how businesses might apply these technologies, this is an essential listen. Transcript: Tony DeHart:  Welcome to the latest episode of Beyond the Bot , where we break down the latest and emerging technology—and how to put it to work in your business. I'm Tony DeHart. Steven King:  And I'm Steven King. Tony:   Today we're going to be breaking down some exciting news: we got a first look at Figure 01’s Helix this week, which is the latest in humanoid robotics. To break this down from a technical perspective, we had to bring in Bhargav Bompalli, our Senior Computer Vision and Robotics Engineer at Blue Sky Robotics. Bhargav, thank you so much for joining us. Bhargav Bompalli:  Thank you for having me. Tony:  In some ways I feel a little bit of deja vu because we’ve seen advancements in humanoid robotics before—particularly with Tesla’s Optimus bot. We broke down that demo in a previous video. Some folks were a little skeptical of that technology. Steven, how is this release different? Steven:  Well, for one thing, when we were talking about the Tesla one, everything was presented as if it was AI-driven. There was a lot of great AI stuff there, but some of it was done by human remote controllers. In this case, we have a video from another company—and they also released a lot of information that Bhargav was able to dig into and actually understand the mechanics behind. That gives us a higher level of trust in what we saw. Tony:  So Bhargav, when you were researching this and looking into the technical details, were you able to follow how they pulled this off? Bhargav:  Yes, it’s a very exciting technology. And the fact that they published all these findings on their website and put out a press release makes it more trustworthy and repeatable in the public’s eyes. Tony:  That transparency is really important to build trust. Steven, before we dive into the technical breakdown, is this what we’re looking at as the future of robotics? Steven:  There’s definitely a lot of excitement around humanoid robots. We live in a human world built around our form factor—stairs, tools, environments designed for humans. So there's inherent value in robots that can navigate that. Plus, there's the dream of a Rosie-from-the-Jetsons style assistant that can multitask. But it’s not the only path forward. Task-oriented robots that are specialized for efficiency in a narrow area also offer huge value. That said, Figure just raised a $1.5 billion investment, making them a $40 billion company. They have the resources to solve hard problems. Tony:  Bhargav, this is exciting from a technical perspective because it’s a first look at Helix—the vision-language-action model they developed in-house. It’s what allowed them to break from their long-term partnership with OpenAI. Can you tell us what a vision-language-action model is, and how that enabled this shift? Bhargav:  Absolutely. This demo was a flawless execution of a VLA—a vision-language-action model. It combines a vision-language model (VLM), which understands visual inputs and language, with action outputs. So the robot can interpret what it sees and hears, and then perform a corresponding physical task. Tony:  Now, interacting with robots via voice isn’t entirely new. How is this different from something like an Alexa? Bhargav:  Think of Alexa as the first step. You ask it for a recipe, it tells you one. A VLA model can take that a step further. You say “make me breakfast,” and the robot identifies ingredients, reasons through the task, and actually makes it—combining vision with physical action. Steven:  From a human-robot interaction perspective, this is a big leap. Historically we used teach pendants or coded instructions. Now, people expect to interact with humanoid robots the same way we interact with other humans—using natural language and gestures. This video demonstrates how those expectations are being met. Tony:  So it’s like having another tool in the toolkit. Steven:  Exactly. Think of humanoid robots as the Swiss Army knife—multi-purpose, adaptable, and great for general use. But sometimes you want a specialized tool—like a chef’s knife instead of a multi-tool blade. Each has its place. Tony:  Bhargav, this robot was interacting with objects it had never seen before. How was it able to do that? Bhargav:  It's trained on a 7-billion parameter vision-language model. While that’s not the largest out there, it’s efficient. It’s seen millions of images online, so it can generalize. Even if it’s never seen a ketchup bottle, it's seen enough similar objects to know how to interact with it. It maps visual cues to actions. Steven:  I noticed that in the fridge scene, it placed the ketchup bottle next to another condiment already laying on its side—suggesting it understood spatial logic and context. Tony:  Let’s talk about the training process. How did they train Helix compared to how we traditionally annotate datasets? Bhargav:  Figure used over 500 hours of teleoperated robot data—robots performing actions like picking up boxes, placing them on shelves, etc. Each action was mapped to a text prompt like, “Pick up the cardboard box and place it on the top shelf.” And interestingly, those prompts were AI-generated. So another AI helped annotate the training data. Tony:  We also saw two robots working together in the video. Were they communicating? Bhargav:  Surprisingly, no. Figure said there's no direct communication protocol between the robots. They’re both running the same neural network—so they act with the same knowledge. It's like having one brain across two bodies. They collaborate by awareness, not communication. Steven:  It’s kind of like how your brain controls both arms. They’re not talking to each other—they’re both just acting based on shared knowledge. Tony:  Now, I can’t get past this moment where the robots make eye contact. I mean, if they aren’t actually communicating, what are they doing? Steven:  So, we don't have any insight specifically as to what the engineers were thinking here, but I think this is part of that human-computer interaction and psychology. If they’re going to have a human form, they need to have some of the behaviors of humans. But I don't know if they hardcoded this for the video or if this is just part of the programming. Like, “hey, we completed a task together!” and they look at each other. Maybe there's going to be a high five one day, right? But the way humans work with robots is going to be really important for adoption. If people are going to adopt it, they have to feel comfortable with it, and this may be one of those steps that helps that. And we’ve seen that in our own deployments, too. We sometimes name the robots, and when we make these baby steps towards personification, people tend to work with them, not against them. When we're deploying robots, they’re typically being used alongside workers, and so we want them to be a team, right? So one thing is we even personify them to the point of when you see one of your robotic arms laying on its side because it just hasn't been installed or something yet, it feels weird! It feels like something's wrong and it's sick or something. So I think this is something that we naturally do as humans, and I think as we interact more and more with robots, we're gonna see this type of interaction with them. Tony:  So, Bhargav, if they aren’t actually communicating yet, can you tell us about the AI that’s actually running under the hood? Is it just one giant VLA model? What’s the story there? Bhargav:  So this is actually another big breakthrough for Figure. So I think this is how they got the name Helix, but basically it’s two AI models both running asynchronously in order to perform their functions as smoothly as they did in the demo. The first one they called S2, which is kind of like the brains of the robot. So S2 is what is running the VLM, which is the vision-language model. So it is constantly analyzing what is sees (so visual data) and it’s also analyzing its robot pose. So it knows what’s in front of it and also what position the arms are in and where the torso is, and it feeds that information to the “brain.” The second model is S1, and this is like the muscles of the robot. Basically, it uses the same visual input as S2, but this time it outputs fine motor function. So depending on what it sees, like a ketchup bottle for example, it’s using this transformer model to convert that into how to pick it up. Tony:  Wow, that’s really interesting. You know, in some cases it’s a lot of the same underlying principles that we use in our solutions. Steven, are humanoid robots something we might see in Blue Sky’s own solutions in the future? Steven:  I wouldn’t say no. But for now, most of our work benefits more from single-arm robots focused on specific tasks. Humanoids are great generalists, but if the job is repeatable and well-scoped, a simpler robot is usually better. That said, it’s a space we’ll keep watching. Tony:  Bhargav, thank you so much for joining us. Bhargav:  Thanks for having me. Tony:  And thank you for tuning in. We’ll see you next week for another episode of Beyond the Bot , where we break down the latest emerging tech—and how to put it to work for your business.

Steven for Beyond the Bot Episode 4 In this compelling episode of Beyond the Bot , Tony DeHart and Steven King take us inside the Restaurant Franchising and Innovation Summit in Myrtle Beach, South Carolina. As restaurants face labor shortages, rising operational costs, and a rapidly changing customer experience landscape, artificial intelligence and robotics are stepping in as key enablers of change. This episode delivers insights from industry leaders about where AI is having the most impact—and where the human touch still matters most. From back-of-house automation to customer experience management, Steven talks with technology vendors and restaurant operators exploring how emerging solutions are shaping the future of food service. Conversations with Ryan Black of Sambazon Açaí and Dave Lehman of Birdeye reveal how AI tools are already helping brands gain efficiency, improve feedback loops, and personalize the consumer journey. Tune in for practical, on-the-ground perspectives on the future of automation in dining. Transcript: Tony DeHart:  Hello and welcome to another exciting episode of Beyond the Bot , where we bring you the latest in emerging technologies like artificial intelligence and robotics, and talk about how to put them to work in your business. I'm Tony, and I'm here in the Blue Sky Lab. Today, we're focusing on the restaurant industry, where our own Steven King is in Myrtle Beach, South Carolina at the Restaurant Franchising and Innovation Summit. He'll be talking to owners, operators, and vendors in the food service space about how they're using AI, and where robotics might make a real difference. Tony DeHart:  Steven, we're excited to hear about your conversations with folks on the ground. But before we jump into those, I'm curious—what are some of the insights you've picked up? What are you hearing are the biggest opportunities for robotic solutions in the restaurant space? Steven King:  After three days of talking with different restaurant operators and people working at the local level, one thing that's clear is that they are all looking for operational efficiency. There's already a fair amount of AI being used in kiosk and point-of-sale systems, but the real pain point seems to be staffing—getting people to work and keeping that consistent. So anything we can do to help back-of-house operations is getting a lot of interest. Robotics could be a real solution there, especially in helping automate repetitive kitchen tasks or improving the speed and accuracy of food preparation. It's not just about reducing labor costs—it's also about ensuring consistency, minimizing errors, and enabling staff to focus more on the customer experience. Tony DeHart:  That’s interesting. It's certainly no secret that staffing challenges are a big issue across the board. A lot of folks have experimented with robotic technology in the past. How is the technology different now? Steven King:  I spoke with the Chief Operations Officer of a major brand—a group with a lot of locations. They were early adopters of Miso Robotics for kitchen work. Initially, they were excited about integrating robots into the kitchen for tasks like flipping burgers or frying items. But it didn’t go as planned. They found that the robots were inconsistent, and when a unit went down, it could take too long to get it back online. In a fast-paced kitchen, downtime just isn’t an option. That experience taught them the importance of reliability and recovery in automation. One of the key takeaways from our conversation was that new developments in computer vision are changing the game. With vision-based systems, robots don't need exact placement or precision—they can adapt to the environment in ways traditional robotics couldn’t. This makes them more suited to work alongside humans in unpredictable, fast-moving settings like restaurant kitchens. Especially when paired with collaborative robots, or "cobots," they can bring flexibility and resilience into operations. Tony DeHart:  That’s exciting—so technologies like computer vision are helping make robots more autonomous, reliable, and robust. What are some of the other big challenges these operators are facing? Steven King:  Besides staffing, they're also trying to grow their markets and boost sales. AI helps with that too, especially in reaching the right people through targeted advertising and optimizing the customer journey from the moment someone walks in or orders online. AI is playing a big role in digital engagement and customer experience, helping brands personalize interactions and refine their service offerings. But again, the recurring theme I heard over and over was staffing. Local managers are constantly under pressure to maintain adequate staffing levels, and the turnover is intense. AI and robotics offer a path to reduce that pressure by handling repetitive or time-consuming tasks. Tony DeHart:  Makes sense. As we apply automation, we want to do it thoughtfully. Where should we place it for the most impact, and where does the human touch still matter most? Steven King:  That’s a key question. If your brand is centered around personal customer experience, like Jersey Mike’s, you probably don’t want to automate those one-on-one interactions. Their brand is built on that personal greeting and interaction as you customize your sandwich. Automation would disrupt that. But there’s a lot you can do in the back-of-house. Food prep, dishwashing, supply inventory—these are all ripe for automation. The idea is to enhance the front-line customer experience by streamlining everything behind the scenes. That way, your employees can focus on hospitality and engagement, while AI and robotics handle the operational load. Tony DeHart:  You also had the chance to catch up with some service providers using AI to drive real business value. Let's hear what they had to say. Ryan Black of Sambazon Açaí Steven King:  I'm here with Ryan Black. Ryan, tell us a bit about what you do and your brand. Ryan Black:  Sure. At Sambazon Açaí, we make the world’s best açaí—truly. We’re present in about 55 countries and supply açaí from the palm of the tree to the palm of your hand. You can find us in major supermarkets around the world, and we also supply a lot of big food service brands. On top of that, we’ve launched our own stores, mostly located in airports and universities for now, but we’re expanding into more neighborhoods soon. Steven King:  What are some of the operational challenges you face, and where do you see AI helping? Ryan Black:  AI is essentially smart software. Anywhere you have repetitive tasks, like scanning inventory or lifting and sorting heavy materials in a warehouse, AI can step in. It can also help with logistics and efficiency in getting products to the right place at the right time. Beyond that, there’s a lot of promise in using generative AI for customer engagement. Think about recommender systems—like Netflix suggesting a show based on your viewing history. In our case, we could suggest products based on individual customer preferences or purchasing patterns. That opens the door to a more personalized customer experience, especially in digital retail. Steven King:  And on the robotics side? Ryan Black:  Robotics is always something we think about, especially in our manufacturing facilities. Imagine robots sorting incoming fruit, performing quality control, releasing materials for production—those are real use cases. The challenge is integrating these systems in a way that complements our human workforce. Robots are great at handling mundane, repetitive, and potentially hazardous tasks. But humans are better at making decisions, applying judgment, and adapting to nuance. The future is in blending those strengths—not replacing people, but enabling them to work more effectively by letting the robots handle the rest. Steven King:  I often ask people what’s something they do a thousand times a day that they wish a robot could do instead. Ryan Black:  Exactly. That’s where the magic happens. You identify those pain points and let automation relieve them so your team can focus on what really matters. Steven King:  Thanks, Ryan. Really appreciate your time. Dave Lehman of Birdeye Steven King:  I'm now here with Dave Lehman, President and COO of Birdeye. Dave, tell us about your platform. Dave Lehman:  Great to be here. Birdeye is a customer experience and reputation management platform. We work with businesses of all sizes to help them connect with their customers, monitor and improve their reputation, manage social media, and handle listings across multiple platforms. AI is deeply integrated across our product suite, especially in our Insights AI and Competitor AI features. These tools give companies a real-time look at how customers perceive their brand and how they stack up against competitors. Steven King:  Can you give an example of how a restaurant might use this? Dave Lehman:  Absolutely. One of our clients, Black Bear Diner, launched a new menu item—chicken fried steak—in Texas. They started getting negative feedback from one location, and their instinct was to assume the product was a flop and needed to be pulled. But when they looked at the data through our Insights AI, they realized that feedback from other regions was overwhelmingly positive. The issue was isolated to a single area. By identifying that nuance, they avoided a costly overhaul and instead made a localized adjustment. That’s the kind of clarity AI can provide—it cuts through the noise and shows the real story. Steven King:  What do you see coming next in terms of AI's role in your platform? Dave Lehman:  The future is all about AI agents. These are automated tools that can help with tasks like responding to reviews, updating business listings, or managing social media. What’s exciting is how these agents will begin to interact with each other to streamline even more workflows. Instead of having to manage each channel separately, you’ll have smart systems doing it for you, faster and more accurately. This frees up people to focus on the creative and strategic aspects of running a business. For restaurants, that means more time spent on delivering great food and hospitality—less time buried in administrative tasks. Steven King:  Thanks so much, Dave. Tony DeHart:  Thanks everyone for tuning in to this episode of Beyond the Bot . We’ll be back soon with more insights into how emerging technologies like AI and robotics can help you transform your business.

Tony and Justin Lorenzo for Beyond the Bot Episode 5 In this episode of Beyond the Bot , Tony DeHart from Blue Sky Robotics sits down with Justin Lorenzo, President of L&M Architectural Signs, to explore how a multi-generational, family-run signage business is transforming with automation. Justin shares how his team embraced robotic painting technology not only to boost efficiency but to elevate quality—meeting the intense demands of modern manufacturing without compromising their craftsmanship. From early challenges to impressive returns on investment, the conversation explores what it really means to merge tradition with cutting-edge innovation. Transcript: Tony DeHart:  Hello and welcome back to the Blue Sky Robotics lab. I'm Tony DeHart, and on today’s episode of Beyond the Bot , we're excited to bring you a conversation with Justin Lorenzo from L&M Signs. Justin is an innovator in manufacturing and the signage industry more generally, and we're excited to catch up with him about how he's using automation to benefit his business. Justin, thank you so much for joining us. I’m super excited to catch up with you and hear a little bit more about your story and the results that you’ve seen. Can you kick us off by telling us just a little bit about who you are, what your company is, and what it does? Justin Lorenzo:  Sure! I’m Justin Lorenzo, nice to meet everybody. I’m the President of L&M Architectural Signs. We’re a full-service signage and environmental graphics company. I’ve pretty much grown up in the industry—my dad and granddad started this in 1982. My mom and dad worked it for years, and now my brother and I run it. It’s kind of the way you grow up in the Lorenzo household—in the sign industry. So it's been a lifelong pursuit. What do we do? Oh boy, lots of different things—dimensional logos and letters, environmental graphics like wall coverings and glass films, custom projects like millwork and metal fabrication. There’s not much we don’t do. We manufacture about 95% in-house and manage everything internally. Tony:  So you really are that classic American small business, family-owned and going back generations. That’s incredible. How did you make the decision—or what motivated you—to look towards automation? Justin:  At large, we've always been updating as fast as we can. As soon as water jet became viable, we moved from manual cutting on band saws to water jets. CNC routers came even sooner. We try to stay ahead of the technology curve. With robotics specifically, we were looking for an opportunity to get our feet wet. Same with additive manufacturing, AI—all these new technologies. If you don’t stay ahead, you become irrelevant quickly. The opportunity came up to jump into robotics, and we contacted you guys. I guess we can talk more about the specific use case—the painting robot. Tony:  Yeah, I’d love to hear how you targeted painting as one of your first robotic projects. Justin:  We had a very specific large project—can’t talk about the client—but consistency of the paint finish was hyper-critical. We’ve been painting and finishing for decades, and we knew this application needed a higher tier, a different standard. It was a matte black paint. It needed consistent color, Sheen, and mill thickness—not just part-to-part, but batch-to-batch and over time. We tried with human application and couldn’t hit the consistency. Robotics seemed like our best shot. Tony:  So in contrast to people looking at automation as a labor-savings tool, you were really looking at it for quality and consistency. Justin:  Correct, absolutely. Tony:  You came to us with the challenge—getting a consistent finish. What happened next from your perspective? Justin:  We knew robotic painting systems exist, but most are geared towards scale—like automotive or industrial painting. Those systems cost upwards of $250–$300k and are less flexible. We needed something small-footprint, easy to run, easy to start and stop. We figured a custom solution was the way to go. We found you guys, and you said, “Sure, we’ve done similar things. We can help.” Tony:  Not just custom but flexible, right? Because you’re a project-based shop. One day it's squares, the next it's circles. Justin:  Exactly. You might be doing squares all day today, but tomorrow it's circles or triangles. It changes constantly. Tony:  What kind of results have you seen since putting this system to work? Justin:  The consistency far exceeded our expectations. Batch after batch, week after week—it’s flawless. You can’t tell the difference between what was sprayed today and what was sprayed a month ago. Matte black is the gold standard—very unforgiving. And the robot nails it. Tony:  How have you integrated it with your current painters? Justin:  Right now, we’re still only using it for that one big project. But we're planning to use it for our product line too—these frames painted white or black. Currently, our operator manually primes the parts, loads multiple trays into the robot, hits the Go button, and walks away. During the cycle time, he’s doing other tasks—priming, mixing paint, organizing. It’s an augment, not a replacement. He’s more efficient overall—even though the robot isn’t faster than an experienced painter, the combined output is higher. Tony:  So not man vs. machine—it’s man plus machine. Justin:  Exactly. It helps the person. It doesn’t replace them. Tony:  Any unexpected benefits? Justin:  Definitely—paint usage was a huge surprise. We're saving anywhere from 50% to 70% on paint, which we did not anticipate at all. When we first watched the robot spray, we actually assumed it was using more  paint because the spray fan stays on longer. But once we ran the numbers, it turned out the robot is able to lay down such a precise and consistent pattern that we’re pushing way less volume through the gun. The overlap is perfect every time, which means we don’t have to overcompensate like you sometimes do with a manual sprayer. And that really adds up—some of the paint we’re using on this job is over $120 a gallon, even after reducers. In phase one of the project alone, we estimated we’d need about 40 gallons. But because of the robot's efficiency, we only used about 20. That’s a direct savings of around $2,400 just on materials for that one phase. When you factor in multiple batches over the course of the year, those numbers really compound. It’s not just a minor improvement—it’s a fundamental shift in how we think about material usage and waste. Tony:  So how did that impact your ROI period? Justin:  Honestly, the project wouldn’t have been feasible without the robot to begin with—that was our return on investment right out of the gate. But even beyond that, if you’re looking strictly at dollars and cents—between the increase in quality, the efficiency gains, and the reduction in paint usage—we’re on track to fully recover the system cost within this calendar year. That’s way faster than we initially projected. The robot has already paid for itself in terms of value delivered. Tony:  Incredible. And you took a risk on us—what was that experience like? Justin:  Hindsight being 20/20, it was absolutely the right call. You guys really pulled it off. After our first Google Meet, I could tell you were a motivated team. I did some digging, looked through the resumes—you had the technical background, and I knew that we had enough in-house capability to collaborate if there were gaps. But that wasn’t even necessary. You handled the whole thing end to end. From a risk-reward standpoint, it’s one of the best bets we’ve made. I’d do it again in a heartbeat. Tony:  And uptime? Has the system stayed online and productive? Justin:  No issues at all. The only time we ran into anything was when we had to unplug the system to move some things around in the shop. There was a brief hiccup during the reboot, but your team jumped on a call with ours and logged in remotely. We were back up the same day. Other than that, it’s been smooth sailing—zero downtime, and you’ve been totally supportive throughout. Tony:  You're clearly at the cutting edge. How do you see automation impacting your industry and small/mid-sized manufacturing in the next 5–10 years? Justin:  If you’re not doing it now, you’ll become irrelevant. It’s not just about using ChatGPT in the office. It’s additive manufacturing, robotics in the shop. We might add robotic welding, conveyors, minor assembly—maybe another robot in five years. Ten years? Who knows. Fully integrated systems, AI vision, additive manufacturing. I see industries converging—good builders building lots of different things, not just signage. Tony:  Yeah, and what we’re seeing as materials and labor are getting more expensive is that being able to create this synergy between people and machines really creates a key competitive advantage. Has that been your experience? Justin:  Always. Tony:  And how has the operator experience been? Justin:  No issues whatsoever. He’s happy with it. He was a little bit intimidated by it— there were jokes running around the shop of “oh you’re gonna get replaced by a robot.” They named it after him! He loves it now. Tony:  Super glad to hear that, Justin. Well with this question of automation more generally in your industry, what are the stakes? What does this mean to you as a family-run business in terms of being able to stay competitive? Justin:  Well we touched on this a little before, too, but if you’re not exploring this now, you’re going to become irrelevant, and pretty quick. You need to be thinking automation, you need to be thinking robotics, you need to be thinking AI technology in every part of what you do. If you’re not doing it you won’t exist in 10–15 years. Tony:  That’s certainly a wakeup call. As we wrap up, is there anything else you'd like to share? Justin:  Just looking forward to talking more. We’ve got other projects we want to explore. Let’s see what’s possible. Tony:  Justin, thanks so much for joining us. Always a pleasure, and we look forward to watching you grow and be successful and having that be a collaborative effort. Looking forward to the future! Justin:  Yes, sir. Absolutely.

Steven and Tony for Beyond the Bot episode 6 In this episode of Beyond the Bot, hosts Tony DeHart and Steven King dive into the fascinating and fast-evolving world of collaborative robots, or cobot capable robots (cobots). From differences between cobots and traditional industrial robots to the latest advancements in AI, machine vision, and usability, Tony and Steven explore how cobots are reshaping industries. They also unpack how small and medium-sized businesses can adopt these technologies efficiently and affordably—while retaining and repurposing human talent. With insights from real-world use cases, this conversation is a must-listen for anyone curious about the future of automation, workplace collaboration, and robotic integration. Transcript Tony DeHart:  Hello, and welcome to another exciting episode of Beyond the Bot , where we bring you the latest in AI and robotics—and how to put it to work in your business today. I'm Tony. Steven King:  And I'm Steven. Tony:  We're here in the Blue Sky Lab, and today we're going to be talking to you about cobots. Now Steven, just for our listeners before we jump into it—can you give us a little bit of insight into what a cobot actually is, for folks who might not be familiar with the term? Steven:  Well, historically we've had industrial robots, which worked behind a fenced-in, protected area. They were very strong and required strict safety protocols. Cobots, on the other hand, are designed to work alongside people. The idea is that a human and a robot can collaborate, which allows for much more flexibility in the tasks we can tackle. Tony:  So why might some folks be interested in using a cobot instead of an industrial robot? What are some of the benefits and tradeoffs? Steven:  For one, they're less expensive. You can deploy them in more environments—offices, labs, small manufacturing lines—places where industrial robots typically aren't feasible. Cobots are safer and more accessible, which opens up a lot of opportunities for repetitive tasks in tabletop environments, for example. Plus, because they’re built with safety in mind, you don’t have to invest as heavily in safety cages or large protective infrastructure. Tony:  And with less safety equipment required, I imagine the deployment cost is also significantly lower? Steven:  Exactly. Even if the robot itself isn't cheaper, the total cost of the project usually is, since we don’t need as much hard automation infrastructure. We're not putting in as many fences or interlocks, which saves both time and money. Tony:  Without those hard automation pieces, do you get any ancillary benefits—like added flexibility? Steven:  Definitely. You can program a cobot to do one task today, switch modes, and have it do something else tomorrow. That ability to pivot makes cobots highly adaptable to shifting business needs. We call them missions—customized sequences the robot performs. It’s easy to switch between missions as needs change. Tony:  Cobots are clearly having a moment right now. Their capabilities are expanding rapidly. What's driving that? Steven:  We get to solve a wide range of problems across different industries. We're seeing cobots being used not just on assembly lines and in warehouses, but also in labs and offices. These robots now offer 0.1 millimeter repeatability—very precise work, which is ideal for tasks that are hard for human hands to do consistently. And that precision opens doors for things like small electronics assembly or lab automation. Plus, the cost has come down, which is making them accessible to more people. Tony:  And how does AI, along with sensor technology and cameras, fit into all of this? Steven:  Robots have been around since the 1960s. Traditionally, they moved from point A to point B, doing the same thing over and over. With AI—especially computer vision and machine learning—we now teach robots to identify and interact with objects. That adaptability reduces errors and simplifies programming. Instead of saying “go to coordinate X,” we now say “find the object”—and the robot figures out where it is, even if it’s moved slightly. Tony:  So if you're not locked into precise positioning, you can work more easily alongside humans. And humans, like me, don't always put things back in the exact same place. Steven:  Exactly. Think about a kitchen—no chef puts the spatula in the exact same place every time. Vision-enabled cobots can handle that variability. That’s what makes them so ideal for collaborative environments—they tolerate real-world messiness. Tony:  How does all this affect how we program and operate these robots? Steven:  At Blue Sky, we design interfaces that make cobots as easy to use as a power tool. We leverage AI and solid UI design to allow operators with basic training to run them. With just a bit more training, they can create new missions and customize tasks without needing a roboticist on staff. You don’t need to write code—we use drag-and-drop and intuitive workflows. Tony:  We've got industrial robots, cobots, and now humanoids entering the scene. How do you choose the right one? Steven:  It depends on your end goal. If you're doing highly variable tasks, a humanoid might be best. But if it's repetitive—like moving boxes from A to B—a cobot or industrial robot is more efficient. Humanoids are often overkill and less energy-efficient for those tasks. They’re built to do everything, but most jobs don’t need that. Instead, you want the right tool for the job. Tony:  Within the cobot space, there are tons of options. How do you decide which ones to focus on—and how much to spend? Steven:  We’ve tested many, and the landscape has shifted a lot in just the last few years. Universal Robots (UR) has long been a leader, but now there are great alternatives at a third of the cost. One we like is the UFactory xArm 6. It’s easy to work with, has a great SDK, and fits most of our needs—good payload, precision, and affordability. And it integrates easily with our existing platforms. Tony: So as a buyer, I’m looking at payload, SDK support, and maybe also service and support? Steven:  Exactly. We guide our clients through uptime requirements, number of shifts, and what kind of support they'll need. Many small businesses don’t have roboticists, so we ensure strong support options—both onsite and remote. We even offer remote diagnostics and mission updates. Tony:  It’s not just about the robot, right? What about all the other hardware and tools? Steven:  The cobot is just the base. You also need the right software and the right end-of-arm tooling. Sometimes that's suction; sometimes it’s a traditional claw or even a custom-built tool for specific tasks. We've 3D printed some that can pick up something as delicate as an egg without breaking it. Others are rigid and allow for tasks like pushing or spraying adhesives. Tony:  So who's the quarterback pulling all of this together? The robot manufacturer? The tooling provider? Steven:  Usually, it’s an integrator. Manufacturers provide the base robot, but most clients need help with customization—whether in software or end tooling. We support clients through the setup and give them tools to continue adapting on their own. And we always make sure they have the training to tweak and evolve their setups over time. Tony:  As we look to the future with more and more of these actually in the workforce being productive—we often talk about managers for people. But what’s the corollary for robots? Who oversees them? Steven: Yeah, it was interesting—we were talking to a client the other day about HR, Human Resources. And we joked that now we have RR, right? Robotic Resources. I don't know what we’re going to call it, but ultimately it’s people who have a basic level of training and can make sure the robots are doing what they’re supposed to be doing. That might mean just using a user interface—something as simple as a web browser—to edit, change, update, and make new missions. Other times, it might mean having someone with a screwdriver who can disconnect two cables, take out six screws, replace the arm, and ship it back. Tony:  Do you give them names? Put googly eyes on them? Steven:  Human-computer interaction is something that’s really important to us. We want to make sure that people feel comfortable with the robot, both in terms of safety and in how they interact with it. It’s a coworker—it’s a cobot. So yeah, we sometimes have given them names and even googly eyes. It just depends on the client and how creative they want to be. But we’re very clear that we want them to have real, specific names—not just numbers. And part of our work is making sure our clients can communicate clearly with their teams, so people understand what the cobot is actually going to do—not what they fear it might do. Tony:  So the future really is man plus  machine—not man versus  machine. Steven:  Exactly. In most cases, these cobots come in and work alongside humans. And then businesses repurpose those people to do different tasks. Almost all of our clients do that. Some have even opened new businesses or launched new opportunities because they could redeploy their people to more meaningful or growth-oriented work. Tony: Steven, it’s always exciting to catch up with you on the fast-changing world of robotics and AI. Thanks for joining us on Beyond the Bot. Steven:  Thanks for having me. Tony:  We’ll be back next week with another exciting topic!

Tony and Marissa for Beyond the Bot Episode 8 In the second part of this Beyond the Bot  interview, hosts Tony and Steven are joined by Marissa Porto, the Knight Chair in Local News, to explore the rapidly evolving world of artificial intelligence. The conversation zeroes in on the intersection of AI with creativity, business ethics, environmental impact, and workforce transformation. Together, they unpack nuanced questions around generative AI (GenAI), the responsibilities of companies in retraining their workforce, and the moral and ethical implications of using AI tools for content creation. Whether you’re a tech-savvy entrepreneur, a creative professional, or just curious about the ethical trajectory of AI, this episode offers a rich and thought-provoking dive into what it means to innovate responsibly in a digital age. Transcript Tony DeHart:  Hello and welcome to another episode of Beyond the Bot, where we go beyond the headlines and explore the world of AI and robotics and what it means for you and your business. I'm Tony. Steven King:  And I'm Steven. Tony:  And we're here in the Blue Sky Lab and we're joined by Marissa, the Knight Chair for Local News and Sustainability at UNC Chapel Hill. Marissa, thank you so much for joining us. Marissa Porto:  Thank you for having me. Tony:  So before we jump into the topic here, can you tell us a little bit about who you are and what your relationship with the news and artificial intelligence is? Marissa:  Well, I've spent most of my career in newsrooms covering small communities around the country and leading newsrooms and then leading news businesses for companies in the United States. And here I've been for three years. I'm the Knight Chair in local news. I focus my time and attention on the intersection between journalism and sustainability innovation. And the last few years I've been studying AI and how it's changing the business model. Tony:  So AI is a huge topic in the realm of innovation and creating content, right? And, you know, we talk a lot internally about artificial intelligence as a driver of business value. But today we really want to focus on the creative applications of AI and what some of those might look like. So when we talk about AI art, what exactly are we talking about here? Marissa:  So we're talking about—it's a broad spectrum, right? It's everything from poetry to stories to videos to— Steven:  Music. Marissa:  Music. Great. Everything that is creative is AI art. And that is what we're looking at today and what we're using in our classroom to teach our students. Tony:  So when we look at generative AI, Steven, specifically on the business front, there are a lot of ways that we can use this, right? What are some of the applications that a business might be looking to accomplish with generative AI? Steven:  I think before I answer that question, I might want to say that there's an argument over: is generative or AI art really art? Is it the creative process? Does it make things? So how do we define art, essentially? But let's just assume we're going to call it art because it makes a visual image or it makes something that makes us think. And so there is business value to that. People can make a t-shirt, they can sell that t-shirt. And so now all of a sudden people are like, "Oh, I can make things really quickly." They don't have to have all that talent or skill that they needed before. And so now they're able to do things because they have an idea and they can use generative AI to generate that idea that they can then try to sell and make money with. Tony:  So Marissa, when we focus in on that application—if we are generating an image using artificial intelligence—we've kind of cut a creative person out of that equation in some ways. What are the ethical implications of that? Marissa:  Well, I mean, I think there are a lot of ethical implications of what we're doing with AI. Right? First, they're really twofold. The first is: what is AI using for you to be able to go in there and give it a prompt and have it spit something back at you? Is it copyrighted material? And is that copyrighted material being used with permission or not? In which case, they're undercutting the economic value of this content. Right? So that's the first issue. Then the second real issue is that, as you're developing something using AI, at what point does it become something more than generated AI—something that really has artistic value, that has human interaction in it? What's the point there at which it becomes a creative endeavor? Tony:  And so if we go back to our t-shirt example, for instance, what really is that point where it becomes a new creation and not something that anybody can just go and print that image? At what point is it actually copyrightable? Steven:  Well, I think from my perspective, it's one of those things that maybe even the moment that it's generated—now the courts can argue over this—but the moment it was generated was based on a concept I had. So I had an idea for a t-shirt, I really did, and I was basically like, "Our robots suck." Okay, that was the concept we were going with. And I kind of came up—I wanted it comic style. I wanted it to have like the big "pow" kind of icon about it. I crafted this thing till I got to the exact colors I wanted, and then I got it and I thought I had it right. And then I used it, I made it, and now someone else has copied the idea. Do I own the copyright on that? I don't know. I like to think that I do. But ultimately, I could have sold the t-shirt—I didn't, right? But if I did sell the t-shirt, then all of a sudden I'd be losing money on that. So I think the moment that I created the prompt, I created something that didn't exist before. So therefore, I should be able to have the copyright on that. But people like to argue over that. Marissa:  Right. I mean, this is a global issue. It isn't just in the United States. This conversation is happening around the world. And the issue becomes: how much creative work was put into the prompt? So the courts are still diving into this, but the Copyright Office at the Library of Congress said in January that if there's significant human creative input into the content, then it is possible it could be copyrighted. So as an example, if I prompt ChatGPT by saying that—(and you could fill in any number of those tools)—but if I used a prompt and said, "Generate an image of a dog on a skateboard," right? That prompt is just a prompt. But then if I say, maybe I want the dog to be—I like Collies, so a Collie. And I want it to have five puppies, and I want it to have a green beret, and I did some back and forth about what that dog needed to look like and what color it was. Now you're starting to get into beyond the first prompt—you're starting to use a tool with human input and expression. And that is where, with the Copyright Office decision in January, they decided that could be copyrighted. Now, who makes the decision and at what point? That's the question right now. Tony:  Well Marissa, I want to hone back in on one thing that you said a moment ago, which is that this question is twofold—not just can the output be copyrighted, but is it being influenced by inputs that might have been copyrighted? So if we go back to our t-shirt example: if I say I want a picture of a dog on a skateboard in, say, Studio Ghibli style or in the style of Salvador Dalí, does that change the copyright implications? And does it change the ethical implications of using that art? Marissa:  Yes. So The New York Times and some other news organizations are now suing Microsoft for this very reason. They're saying Microsoft used that content that is copyrighted by The New York Times and they allowed their tools to be trained by it. And therefore, anything that's being output that has a New York Times style to it or feels similar to a story really was used without permission. So what you see now is, on one side, organizations like The New York Times suing for that. And then on the other side, some organizations, news organizations and media organizations, actually finding a way to contract their content, whatever their content is, and have the AI organization, the company, give them money for the use of that for training purposes. So those are sort of the economic and legal things that are happening in the world today. Steven:  Because it's really complicated. Because I say, I want to make this in Salvador Dalí style. Then I had to have looked at a Salvador Dalí painting to do that. Now, if I were the artist and I copy his style, the courts have said that an artist doesn't own that style. But in the case of the AI part, they had to study and take in that without permission. In most cases it's happening. And so therefore it's like you made a derivative of something you probably shouldn't have had access to in the first place. And that's where it really gets complicated as to what this thing is and kind of who has access and rights to it. So if I do it in The New York Times style, does The New York Times now get a few pennies every time I want to make something in that style? I think the courts are going to have to figure that out. Marissa:  Right. And there's a term called fair use. And fair use is a legal term. And it essentially says, if I'm taking some information and I'm transforming it in some way—so let's say I read something or see something and I decide to use it and transform it—this is outside of AI—I transform it into, let's say, a column. Right? I read something in The New York Times. I thought, oh, this is really interesting. I use some of the information, not word for word, but for a column, a writing that's either pro or con. That's a transformative use. Right? So that's called fair use of that content. And companies are arguing—the AI companies are arguing—well, letting us train our AI bots on content, that's a fair use. And so that's really what the courts are going to have to figure out right now. Tony:  Well, and notable to that example, it's attributed. Right? In that case, you're saying this is information that I got from a New York Times article. But that's not always the case with AI. So for example, Steven, from a business perspective, as a person who comes up with a lot of creative solutions, how would you feel if an AI chatbot was able to parse those solutions and serve them to people without your will or knowledge? Steven:  Yeah. I mean, it's like, you know, we come up with a solution. We share that with a client. The client then took that and built it on their own. That's really frustrating. Okay. The same thing is happening in AI every day. But it's a collective and you may not be aware of it. Right. And so as a business owner and as we're trying to figure out the future of this, I think business owners are going to have to decide how much do I share publicly? Is there going to be some way of me saying, no, this content is not available to AI bots, for example? Is this something that I want to have some way of protecting? We don't have a good way to do that, but I think it'll be up to the people. Maybe the University of North Carolina, Hussman School should come up with that, right? Maybe there has to be some way that we do protections of these and give people the choice to opt in and opt out. Those types of things. Marissa:  And I mean, I would say there are businesses already that are building their own AI models. Right. We just had someone from Bloomberg, a UNC grad, speaking to my media economics class. And one of the things she said is that Bloomberg has a closed system. So it puts in its own system Bloomberg content and only allows Bloomberg content because it already knows that Bloomberg content has been vetted. And so you can't get into the system from outside, but inside the company, you can get into it. So you see those sort of closed systems developing now. Tony:  Now Marissa and Steven, there are a lot of things that we can do to protect copyrighted materials moving forward. But a lot of media companies have kind of made this argument that the toothpaste is out of the tube, so to speak. There are already massive libraries of open source materials that have been used to train these models. And so is this even a relevant question, or is there a way to go back? Or, you know, where do we go from here, given that that's, you know, sort of the bell's already been rung, so to speak? Marissa:  I think that is a... that is a challenging question. So and you have to look at it with the vantage point of the United States, and then you have to look at it from a global perspective. So here in the United States, we sort of have a little bit of the Wild West feeling about regulating business. And it- it's continued, right, this administration is very much anti-regulation for business. And so you see some of those guardrails coming down, for different types of businesses. But you also have, you know, the in the EU, there's a very significant, there's very significant guardrails around the use of AI and how the ethical uses of AI and how it will be rolled out, and when it's rolled out. All of those things the EU has, has built into its, its laws. And here, that we could... we could be affected by that based on business. So that's a whole conversation that, that I was having, a few weeks ago with some folks from a German university who were visiting here. How do you change the law? Marissa:  Is it- can you put the genie back in the bottle? And, and what would cause the states to actually consider, different legislation. And it seems like the conversation was that if business had to go into another country where there's different legislation, then that might prompt the United States to think about what that legislation should be here. Steven:  Yeah. This is, this is really a business thing, right? Like, as a human, I can't unsee something that I've seen, but as a trained model, as a piece of code that has been received and data has been collected, we can retrain things and no longer use that model. Right. So but that doesn't make it financially smart, right? So a company is going to fight all they can. It's a whole lot cheaper to pay lawyers to defend this than it will be to retrain and remove all that and just keep getting the value that they're expecting out of it. So I think if, if the courts decide it, yes, technically we can put the toothpaste back in the tube as you said. Right. Because we will just use a different tube, right. Like we'll have to do things differently. And so I think there's a way to do it, but financially it's not in the company's best interest, nor is it in the best interests of innovation. The question is innovation versus your rights, you know? Marissa:  And, and let's be honest, ethics right? I mean, how is this technology going to be used in an ethical way? I mean, right now, some of the issues that we have in AI, is it’s being used for deepfakes. And deepfakes are particularly challenging if you are, let's be frank, a female, because a lot of what's happening in that deepfake area is  sexualized content, for celebrities, particularly women. So, what are the ethics of not having AI guidelines, and the United States is right at the cusp of thinking about what to do about deepfakes. And I hope we do something useful for those sorts of guidelines. But those ethics are really important to think about. Even if the genie is out of the bottle. Tony:  And there are certainly examples like that where there is a clear, you know, wrong approach and a clear right approach. Right. But it does seem like even for businesses and creators and individuals that, you know, have the best intentions and want to do things in an ethical and legal way, you know, there maybe is some gray area where the right choice is not always as clear. Tony:  And, you know, Steven, from from your perspective as a business owner, that level of uncertainty is is famously bad for business, right? And so, you know, I guess my question to you is twofold. First of all, from a business perspective, how do you navigate that uncertain environment? And then from a regulation perspective, what can be done to remove that gray area and kind of provide some clear guidance for folks? Steven:  Yeah, I think we're going to have to see the courts decide. We're going to have to see precedent. Once we have precedent, then we can make policies and kind of move things forwards. And, and, and businesses will be able to know where they can operate. That's going to take time. So I think what you're gonna see is businesses are going to start and businesses are going to fail, businesses are going to get acquired and things are all going to happen as these things happen and technology is going to change faster than policy. And that always has happened, right? Throughout history, technology moves faster than policy. And so we have to figure this out, and hopefully we're driven by good ethical standards and we follow these things.