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A comprehensive showcase of Taiwan’s technological prowess in manufacturing was on full display at the recent Automate 2026 event. The Taiwan Excellence Pavilion, featuring 23 award-winning companies, drew significant interest from attendees with its cutting-edge innovations in industrial automation and robotics.

The pavilion, organized by Taiwan’s International Trade Administration (TITA) and the Taiwan External Trade Development Council (TAITRA), highlighted the island nation’s growing role as a trusted technology partner for modern U.S. manufacturing. The event provided a platform for Taiwanese companies to demonstrate their capabilities in supporting next-generation smart manufacturing.

The level of engagement from manufacturers, system integrators, and automation leaders throughout the four-day event was impressive, reflecting the increasing demand for technologies that improve agility, simplify integration, and accelerate innovation. According to TAITRA spokespersons, Taiwan Excellence companies are uniquely positioned to meet this demand through their highly integrated ecosystem.

Throughout Automate 2026, attendees participated in educational presentations and technology showcases highlighting Taiwanese innovations. The event featured a series of daily events and product tours that allowed visitors to explore the technologies driving the next generation of smart manufacturing.

The Taiwan Excellence Showcase was one of the highlights of the event, featuring six companies – TECHMAN ROBOT, Syntec, TOYO, Axiomtek, SINTRONES, and DFI – presenting technologies addressing key trends discussed throughout Automate 2026. These included robotics, AI-enabled automation, industrial computing, and smart manufacturing.

Guided tours of the pavilion provided attendees with a deeper understanding of how Taiwan’s award-winning technologies support modern manufacturing. The tours allowed visitors to hear directly from exhibitors, explore real-world applications, and gain insights into the latest innovations in industrial automation and robotics.

The strength of Taiwan’s innovation ecosystem was acknowledged by Clarissa Schwendeman, director of marketing for the Association for Advancing Automation. She noted that collaboration between manufacturers, technology providers, integrators, engineers, and global partners is driving progress in automation and that Taiwan Excellence plays an important role in advancing global business and technology.

Many trends highlighted at Automate 2026 – including AI-enabled automation and smart factory integration – are increasing the need for technologies that improve flexibility, interoperability, and operational efficiency. Taiwan’s highly integrated ecosystem of technology providers, manufacturers, and engineering specialists is well-positioned to help manufacturers address these challenges while accelerating innovation.

The Taiwan Excellence Awards were established in 1993 by Taiwan’s Ministry of Economic Affairs (MOEA) to recognize exceptional achievements in product innovation. The awards assess products based on four important factors: research and development, design, quality, and marketing – with the key criterion being that the products are made in Taiwan.

The news is out: Tesla has begun dismantling the original Model S and Model X assembly line at its Fremont Factory, marking a significant shift in the company’s production strategy. The decommissioning process, which started just 46 days ago, will make way for the production of Optimus humanoid robots, with an ambitious goal of reaching 1 million units per year once fully ramped up.

The Model S entered production at Fremont in June 2012, followed by the Model X in 2015. Together they defined Tesla’s identity as a premium electric vehicle (EV) maker for over a decade. Production of both vehicles officially wound down around May 10, 2026, after Elon Musk announced the phase-out during Tesla’s Q4 2025 earnings call in January 2026.

The speed at which this changeover is happening is remarkable. Tesla says the decommissioning of the legacy assembly line took just 46 days – a pace that reflects how aggressively the company is prioritizing the transition to robot production. According to reporting from The Robot Report and Assembly Magazine, the full conversion of the space to Optimus production lines is expected to be completed in roughly four months from start to finish.

The Fremont site will host production of the third generation of Optimus, Tesla’s humanoid robot. Musk has publicly targeted 1 million units per year as annual capacity once the line is fully ramped up – a figure that would dwarf any humanoid robot program in existence. This target is ambitious for a product that hasn’t yet shipped commercially.

Tesla plans to begin external commercial sales of Optimus later in 2026, with a long-term price target of $20,000 to $30,000 per unit. Early-access units in 2026 are expected to be priced higher – reports suggest $50,000 to $80,000 – with broader volume availability slated for 2027. Initial output will be ‘extremely slow,’ according to Musk’s own words.

Each Gen 3 Optimus contains roughly 10,000 unique parts, and Tesla is standing up entirely new production processes for actuators, hands, and structural components that have no direct analogue in EV manufacturing. Limited production on the converted Fremont line is expected to begin in late July or August 2026.

The decommissioned space was specifically the legacy Model S/X general assembly line, which had been operating at a fraction of its potential volume for years as demand for the flagship sedans and SUVs tapered off relative to mass-market cars. For existing Model S and Model X owners, this doesn’t change service or parts support – Tesla has consistently maintained legacy vehicle service commitments even as it retires older product lines.

The Bigger Strategic Picture

Musk has told investors on multiple earnings calls that Optimus could eventually be worth more than Tesla’s entire automotive business. Whether or not that materializes, physically converting the birthplace of Tesla’s car business into a robot factory is the clearest signal yet that the company is acting on that thesis, not just talking about it.

The internal deployment of Gen 3 Optimus units at Fremont gives Tesla a rare feedback loop: the robots being built will help build the next generation of themselves. Over 1,000 Gen 3 Optimus units were already operational on Tesla’s own production floor as of early 2026, handling tasks like battery module assembly and EV pack loading.

The next question is how quickly Tesla can fill the empty floor space at Fremont with robot production. The company has publicly acknowledged that ramping up a brand-new product to seven-figure annual volumes will take years, not months – but the momentum is building.

Financial technology company Revolut has integrated its standalone crypto exchange, Revolut X, with third-party artificial intelligence (AI) assistants. This move enables users to analyze markets, set alerts, and place trades using natural language prompts, marking a significant expansion of agentic trading capabilities in the cryptocurrency space.

The supported AI assistants include Claude, Gemini, OpenClaw, and Cursor. Users can link their accounts through a universal skill or command-line interface published on GitHub without requiring any coding expertise. This integration allows customers to request plain-text breakdowns of portfolio performance, pull real-time market data, set custom price alerts, place market and limit orders, and manage open positions directly within the Revolut X app.

Revolut’s head of product for crypto, Leonid Bashlykov, noted that AI agents provide faster workflows, smarter execution, and tighter integration with trading tools. However, users remain responsible for reviewing and approving all orders before execution, as Revolut does not endorse or guarantee third-party tools and is not liable for losses or erroneous trades stemming from AI errors.

Revolut has over 75 million retail customers and more than 16 million crypto users worldwide. The company’s cryptocurrency services are provided by Revolut Digital Assets Europe Ltd., licensed by the Cyprus Securities and Exchange Commission as a crypto asset service provider under MiCA.

The integration of AI assistants with Revolut X comes as several other crypto firms increasingly incorporate these tools into their trading workflows. For instance, Gemini has rolled out Agentic Trading, allowing users to connect Claude and ChatGPT directly to their accounts via the MCP open standard. Liquid also launched Co-Invest in May, bringing live trade execution into ChatGPT and Claude.

Revolut’s crypto exchange, Revolut X, was initially launched as a desktop-only platform for UK retail customers in May 2024 before expanding to 30 European markets that November and adding mobile app support for UK and EEA users in March 2025. The company’s move towards integrating AI assistants with its exchange highlights the growing importance of data analysis tools in cryptocurrency trading.

The use of natural language prompts allows users to describe complex trading ideas, such as a grid strategy on bitcoin over the last 90 days, and receive historical performance, risk metrics, and optimization results directly within the Revolut X app. This feature streamlines the trading process by providing users with real-time data analysis capabilities without requiring them to navigate multiple platforms or tools.

Revolut’s decision to integrate AI assistants with its exchange underscores the company’s commitment to leveraging cutting-edge technology in the cryptocurrency space. As more crypto firms explore agentic trading and AI-powered trading workflows, Revolut is positioning itself at the forefront of this trend by providing users with a comprehensive platform for analyzing markets and executing trades using natural language prompts.

Pension administrators in North America are getting a boost thanks to a partnership between Procentia and Wipro. The two firms have joined forces to build an administration platform that leverages generative-AI assistants to automate tasks and make work easier.

The new platform is designed for insurers and public pension plans across the continent, with a focus on pension risk transfer and AI-assisted member services. By integrating their technologies, Procentia’s IntelliPen platform will be paired with Wipro’s operations and consulting services through its Wipro Intelligence offering.

Wipro brings to the partnership a strong history of working with insurers, wealth managers, and retirement providers on modernizing core operations. Their AI-powered operations aim to improve efficiency, advance digital transformation, and reduce risk for pension plans.

Procentia’s IntelliPen is a system that quickly processes large volumes of data while offering self-service tools to administrators and retirees. This platform will serve as the foundation for the new partnership’s administration technology.

The companies have identified key areas where their combined offering can make an impact: commercial off-the-shelf administration platforms built for high-volume, complex plans; end-to-end support for pension risk transfer; generative-AI administration services that assist administrators in retrieving information and making decisions. These are just a few examples of how the partnership will help.

Wipro will handle the operational side of things, simplifying processes, automating effort, and consistently meeting service commitments. According to Jason Gopaul, Procentia’s chief executive officer for North America, clients will run on Procentia’s technology platform supported by Wipro’s work.

The partnership aims to ‘industrialize pension risk transfer operations’ as Ritesh Talapatra, senior vice-president of capital markets and insurance at Wipro, put it. This collaboration also strengthens their joint approach to the retirement and life insurance market.

Travel agents know all too well the challenges of building multicountry itineraries by hand. It’s a slow, messy, and inconsistent process that can be frustrating for both agents and clients. Vio Travel has been working to address this problem over the past few years, combining direct supplier contracts across 15 destinations with a platform that lets agents design and price trips in real-time.

The company recently became the first destination management company (DMC) in Asia to be certified under the Preferred by Nature Standard for Sustainable Travel Activities. This certification is recognized by the Global Sustainable Tourism Council and was achieved after an independent audit found Vio’s Indonesia operations 100% compliant across responsible management, people, nature, and climate.

We spoke with co-CEOs Dominik Schaufler and Michael Lynden-Bell to learn more about their platform and its capabilities. They explained that the goal is not to replace human judgment but rather to automate as much of the process as possible, freeing up agents to focus on what matters most – providing exceptional travel experiences.

For Schaufler, the starting point was recognizing that too many tour operators are still building itineraries manually. He noted that a significant number of tour operators are doing this the slow way and that Vio set out to remove that complexity. By giving agents access to local suppliers directly contracted across 15 destinations, they can deliver proposals faster, impress clients sooner, and close deals at a higher rate.

Lynden-Bell framed the change in behavior as friction removal rather than a rip-and-replace of existing relationships. He explained that Vio’s platform allows agents to build and price itineraries in minutes instead of waiting days for quotes or requesting availability. The pitch itself, he said, isn’t about technology but convenience, speed, and consistency.

When asked what the platform is doing differently from a traditional DMC’s product team, Lynden-Bell pushed back on the idea that software replaces people. He noted that the real advantage of Vio’s platform is its ability to process thousands of variables simultaneously and do it instantly – something technology can’t replicate with human judgment.

The speed at which agents can build and price itineraries is one of the most significant benefits of Vio’s platform. Lynden-Bell explained that there’s no queue, allowing an agent in London to build a 12-day multidestination itinerary without waiting for Bangkok to open. However, he was clear about the limits – technology can’t replace human judgment on non-standard situations.

Schaufler put a number on the automation ceiling, stating that technically they could automate 99% of the booking journey and are close to achieving it. But the 1% that matters most is human judgment. He emphasized that no algorithm captures earned instinct and that Vio’s goal is to protect this layer rather than shrink it.

When asked about getting hundreds of suppliers online, Schaufler noted that contracting directly with local suppliers across 15 destinations sounds clean on a slide but was actually a complex process. Many operators work with limited resources and aren’t built for speed – the opposite of what Vio Travel stands for.

The real bottleneck wasn’t willingness; it was infrastructure. Suppliers had to navigate technology literacy and bandwidth issues, which made getting pricing standardized and expectations aligned challenging. What moved the needle was having an in-destination team on the ground who could guide suppliers step-by-step.

Lynden-Bell emphasized that Vio’s recent Preferred by Nature certification wasn’t a marketing move but rather a reflection of their commitment to sustainability. The company applies this filter to every supplier decision, and if they don’t meet the bar, they don’t qualify as a partner. This is a firm line – not a purity test with no way back in.

Schaufler added that Vio doesn’t walk away from suppliers who genuinely want to improve but rather works with them to implement concrete sustainability initiatives. He emphasized that growth alone isn’t the objective; they want to build a business that’s scalable, profitable, and recognized for consistently delivering high-quality travel experiences.

When asked about competing with companies like Booking.com and Klook, Lynden-Bell noted that Vio doesn’t see itself as directly competing with them. They solve different problems – Vio focuses on multiday, multicountry travel across Asia Pacific involving complex logistics. This is where an agent needs white-label proposals, direct supplier relationships, and someone to call when something goes wrong.

Lynden-Bell was candid about the tech layer not being hard to defend but acknowledged that replicating the relationships, product curation, and operational infrastructure across 15 destinations overnight would be much harder. He noted that technology can help monitor quality, collect feedback, and improve processes but delivering exceptional travel experiences still depends on people.

The hardest part of Vio’s platform is encoding nuance – things like a change in a local operator’s team or a quality issue they’ve flagged but haven’t fully resolved. Lynden-Bell emphasized that technology can help see problems faster but doesn’t fix them on its own.

Looking ahead, Schaufler said Vio is building toward a world where any agent with a fair knowledge of a destination can design a complete, tailored trip for their client in minutes – no friction, no ceilings, and no waiting. He noted that curation stays human even as the process gets faster, and the quality and curation still come from exceptional handpicked products.

Lynden-Bell pointed to fixed-price packages, wholesale distribution, groups, and API-driven products as areas of opportunity for Vio’s growth. However, he emphasized that their priority is building a sustainable business with strong foundations, great people, and lasting partnerships – not just focusing on growth at any cost.

German telecommunications giant Deutsche Telekom is undergoing a fundamental transformation, leveraging artificial intelligence (AI) to revolutionize customer service, network operations, and employee workflows. The company’s ambitious goal: to become one of the world’s first AI-native telcos.

With over 300 million customers across Europe and the United States, managing vast customer service operations and complex network infrastructure is a daunting task. However, Deutsche Telekom sees an opportunity beyond productivity gains with accelerated generative AI capabilities.

The leadership team views AI as a fundamental transformation of how decisions are made, how customer journeys are designed, and how telecommunications services are delivered. Rather than treating AI as another software rollout, the company aims to redesign the work itself.

We spoke with Jonathan Abrahamson, Chief Product & Digital Officer at Deutsche Telekom, about their journey towards becoming an AI-native telco. He emphasizes that this transformation combines top-down leadership with broad employee adoption and experimentation.

The first phase focused on empowering employees with ChatGPT Enterprise, encouraging them to experiment with the tool in various ways. The result was rapid adoption, as employees quickly grasped the potential of AI in their personal lives and applied it to their work.

Deutsche Telekom began redesigning critical customer-facing workflows simultaneously, starting with customer care. Abrahamson believes that AI-powered customer service is still in its early stages but holds significant medium- and long-term potential.

As these systems gain more context, learn from every interaction, and eliminate common frustrations like handoffs and wait times, they can ultimately outperform traditional support models in certain scenarios. This thinking extends beyond customer service into the core communications experiences customers use daily.

Working closely with OpenAI and other companies, Deutsche Telekom is bringing AI directly into customer interactions through capabilities such as live translation, in-call assistants, and post-call summaries without requiring new applications from customers.

Beyond customer interactions, AI is increasingly embedded into network operations. The company uses AI with various partners to optimize mobile network performance in real-time, adjusting resources dynamically as demand shifts throughout the day.

One of Deutsche Telekom’s most ambitious initiatives focuses on the future of voice communications. For decades, telco providers focused on connecting people, but Abrahamson believes AI creates an opportunity to fundamentally reinvent the voice experience itself.

Using several models, Deutsche Telekom is exploring capabilities including real-time translation, intelligent call assistance, and automated summarization. These innovations move AI out of standalone applications and into communication channels customers use daily.

The company sees this as part of a broader mission to democratize access to AI, generating real added value for people and businesses without requiring specialized devices or technical expertise.

Deutsche Telekom’s transformation combines several key strategies: treating AI transformation as an operating-model redesign rather than technology deployment; making leaders accountable for driving process change; focusing on redesigning workflows rather than simply adding AI to existing work;

Broad employee experimentation is also crucial, balancing top-down direction with bottom-up adoption. The company builds toward AI-native operations one business process at a time, starting with high-volume customer interactions where AI can improve both experience and efficiency.

Data protection, sovereignty, and security are always kept in mind to maintain customer trust. Employees have access to AI tools early on to accelerate learning and adoption. Core workflows that can be redesigned rather than simply automated are identified for this purpose.

The next phase is focused on bringing AI directly into the communications experiences customers use every day through capabilities such as real-time translation, intelligent call assistance, and automated summarization.

With over 300 million customers, Deutsche Telekom sees an opportunity to make AI accessible through the networks people already rely on. For them, becoming AI-native is not a future vision but a transformation reshaping telecommunications today.

LigoLab has joined forces with MarginLogic Health AI to bring in a new way of processing lab data. They’re using artificial intelligence-powered optical character recognition technology to automate tasks that were previously done by hand.

The integration allows clinical labs and pathology groups to send requisition intake directly into the LigoLab platform for faster processing. This cuts down on paper-based orders, which can be slow and prone to mistakes.

Laboratory workers struggle with manual data entry because it’s so tedious. According to Jenny Bull, success director at LigoLab: ‘Requisition intake is where labs usually lose time before a specimen even hits the lab information system.’ They’re trying to fix this by automating those tasks.

The MarginLogic Health AI system recognizes handwritten and printed requisitions using contextual artificial intelligence. It flags any fields that seem unclear, so workers can take another look and make sure orders get processed with minimal manual intervention.

This technology is different from older OCR systems because it actually understands what’s written in the documents. Rather than just converting images to text, it reads between the lines and catches nuances that human readers might miss.

The partnership targets labs that deal with a lot of paper-based requisitions – faxes, handwritten orders, or anything else that requires manual entry. Even places with electronic ordering often have some non-integrated orders that still need to be manually typed up.

When transcription errors happen, they can cause problems down the line – claim denials and issues that hurt lab finances in a big way. The integration wants to minimize those risks by streamlining data entry processes.

This is especially important because it’s not just about typing accurately; it also affects how patient demographics get recorded correctly. Improving these areas means better financial performance for labs in the long run – fewer errors, less lost revenue.

The integration doesn’t mess with existing interfaces, so electronic orders from health records can still flow through as usual. Meanwhile, the AI technology is automating paper-based requisitions behind the scenes.

For LigoLab customers who already have MarginLogic Health AI solutions in place, this partnership means another step towards improving lab efficiency and reducing errors. By taking on data entry tasks, they’re freeing up staff to focus on more important work – like patient care.

B2B sales teams are facing an unexpected challenge: despite the efficiency of AI-driven outreach tools, response rates are dipping and buyers are developing communication fatigue. The reason is simple – technology can help you reach more people, but it can’t make them care. In business sales, using AI to be fast and efficient is just the bare minimum; it won’t make you stand out. The real advantage goes to whoever can build the deepest trust.

The B2B sales playbook has undergone its most radical transformation since the invention of the CRM, with artificial intelligence moving from a futuristic luxury to the operational backbone of modern revenue teams. AI can automate outreach at an unprecedented scale and velocity, drafting personalized emails in seconds, identifying ideal customer profiles through predictive data, and orchestrating multi-channel sequences across thousands of prospects simultaneously.

However, as pipelines flood with algorithmically clean messaging, B2B organizations are hitting a wall. The cost per lead drops, but the volume of outbound activity skyrockets, creating a new problem – business communication now feels cheap and generic. Prospects can spot an AI-generated compliment from a mile away, making personalization feel synthetic rather than genuine.

The limits of AI in B2B sales become prominent when every sales team has access to the same tools, resulting in outreach that sounds remarkably similar. A buyer does not just purchase software or a service; they wager their professional reputation on the vendor’s ability to deliver. An algorithm can state a value proposition, but it cannot assume accountability – it cannot look a client in the eye and say, ‘I will ensure your team successfully adopts this platform.’ This fundamental limitation explains why human trust matters in AI-driven sales.

Trust is incredibly valuable because it smooths out complex business deals, especially when multiple people are involved. Where there is high trust, sales cycles shorten; objections disappear; and price sensitivity is substantially reduced. In contrast, when there is low or no trust in an agreement, every item has been scrutinized under the microscope of contract language before an agreement can be reached.

Psychologists generally classify trust into two categories: capability (assurance that the agent has the proper skills to do the work) and benevolence (assurance that the agent cares about their well-being). When combined, these two elements constitute the basis of all modern-day B2B agreements. The prospect asks two questions – ‘Can this tool do the job?’ And does this team actually care about my business?

Historians and epigraphers now have a powerful new tool at their disposal, thanks to the launch of Google DeepMind’s Predicting the Past Skill. This innovative AI skill is designed specifically for researchers working with ancient inscriptions, allowing them to analyze and interpret these valuable historical artifacts without requiring extensive coding knowledge.

The Predicting the Past Skill connects Gemini, an interactive reasoning platform developed by Google DeepMind, with two specialist models: Ithaca and Aeneas. These models were created to restore, date, place, and contextualize ancient Greek and Latin inscriptions, but their capabilities have now been integrated into a conversational workflow that streamlines the analysis process.

According to Google DeepMind, historians and epigraphers can use this tool to query, cross-analyze, and map massive collections of ancient data as naturally as speaking with a colleague. This means they can explore complex historical questions without needing to write code or rely on manual transcription.

The Predicting the Past Skill is particularly well-suited for researchers working with fragmentary texts, uncertain dates, and unclear places of origin. It’s designed to help them navigate these challenges by providing flexible visualizations, advanced multi-text analysis capabilities, and large language models grounded in evidence and domain expertise.

Google DeepMind has been collaborating with epigraphers on this project for nearly a decade, including the development of Ithaca in 2022 and Aeneas in 2025. Thea Sommerschield, a historian and epigrapher at Durham University, has co-led Google DeepMind’s work in this area.

Ancient inscriptions are central to historical research, but many survive in damaged or partial form. They can include imperial decrees, votive dedications, everyday transactions, and personal appeals – often with missing text, uncertain dating, and disputed origin. Historians need flexible tools that can help them make sense of these complex artifacts.

Google DeepMind has identified three key barriers to AI-assisted historical analysis: researchers require flexible visualizations for individual inscriptions, more advanced multi-text analysis capabilities without specialist coding, and large language models grounded in evidence and domain expertise. The Predicting the Past Skill is designed to address these challenges by linking Gemini’s interactive reasoning with the outputs of Ithaca and Aeneas.

Rather than asking a general-purpose model to work from scratch, the skill draws on specialist inscription models and presents results in a form that historians can inspect. This approach supports restoration, attribution, contextualization, mapping, and comparison across large collections of ancient data – all without requiring extensive coding knowledge.

The Predicting the Past Skill has been tested with three case studies from the Greco-Roman world. The first focuses on Tab.Sulis 97, a curse tablet from Aquae Sulis in Roman Britain. This tablet was written by a woman named Basilia, who cursed whoever had stolen her silver ring – and it’s just one of hundreds of similar tablets found at Bath.

Google DeepMind used Aeneas to place the tablet within its proposed chronological and geographical ranges, producing an explanation that began to resemble epigraphic commentary. The model supported this conclusion using textual features, demonstrating how AI can be used to provide more nuanced historical analysis.

The second case study moves from a single inscription to a wider corpus of votive altars dedicated in 211 CE by Roman officials across the Rhine and Danube provinces. Google DeepMind analyzed regional patterns across related inscriptions and traced how religious practices spread through the movement of people across the Roman Empire – all using the Predicting the Past Skill.

The third case study uses lead oracular tablets from Dodona in northwest Greece, a sanctuary where visitors asked divine guidance on various topics. Thousands of these tablets survive, many in highly fragmentary condition. Google DeepMind used the collection to move beyond individual inscription attribution and reconstruct a wider community of people who came to the sanctuary.

This work shows how the Predicting the Past Skill can be used not just as a restoration engine but also as a tool for exploring connections across large historical datasets. Researchers can examine Dodona not just as a collection of texts, but as a network of connected individuals moving through the ancient Mediterranean – all without requiring extensive coding knowledge.

The case studies demonstrate both the potential and limitations of this new AI skill. A single inscription may need restoration and dating, while a corpus requires comparison, mapping, and pattern detection across many damaged objects. Google DeepMind is working to address these challenges by providing more advanced multi-text analysis capabilities and large language models grounded in evidence and domain expertise.

The Predicting the Past Skill draws on datasets from various sources, including Dodona Online, Ithaca’s use of the Searchable Greek Inscriptions database, and Aeneas’ training data from Epigraphic Database Roma, Heidelberg, and Clauss Slaby. This comprehensive approach ensures that the tool is grounded in specialist inscription models and evidence rather than unsupported AI output.

The Predicting the Past Skill is now available through Google Antigravity, allowing researchers to analyze patterns and produce visualizations ‘in a matter of minutes.’ By streamlining this process, historians can focus on interpreting their findings rather than spending hours transcribing or coding. This new tool marks a significant step forward in data analysis for historical research – one that could have far-reaching implications for our understanding of the past.

An Italian professional football club, Como 1907, is partnering with Google’s artificial intelligence research division, DeepMind, to host a two-day hackathon focused on developing AI tools for use in the sport. The event, called Data on the Lake, aims to bring together data scientists, machine learning engineers, and sports technology professionals to build practical solutions that can support football decision-making.

The hackathon is part of Como 1907’s broader strategy to leverage its position as a professional club to drive innovation in the industry. Founded in 1907, the club has recently returned to Serie A and has established itself as a hub for football and lifestyle activities, including retail, hospitality, media, and tourism.

As part of this effort, Como 1907 operates through affiliated companies under a Multi-Club Servicing business model, providing services and expertise to other football organizations. This structure gives Data on the Lake a potential route beyond a one-off event, with successful tools potentially being tested inside the club and applied across its operations.

The hackathon will take place in Como, Italy, from July 24th to 25th, 2026, immediately before the second edition of Football on the Lake and the Como Cup. The invitation-only event is open for applications, but places are limited and subject to approval by the organizers.

Data scientists, machine learning engineers, developers, sports technology professionals, and technical founders are invited to participate in the hackathon, which will focus on developing AI tools designed for use inside a professional football club. Participants will work with a curated dataset provided by Como 1907 and build solutions that can tackle real-world problems in football.

The event is aimed at exploring how artificial intelligence can support football decision-making, particularly in areas where data collection and operational use are currently disconnected. According to Mo Dabbah, Chief Technology Officer of Como 1907 and SENT Entertainment, ‘Football produces an extraordinary amount of information, but the real opportunity lies in how clubs turn that information into better decisions.’

The hackathon will run under the theme The Future of Football Intelligence, with teams competing across two judging categories: technical innovation and practical utility. A curated dataset provided by Como 1907 will be made available to participants, along with access to tooling and focused build time.

A judging panel comprising technical, football, and business experts will evaluate submissions based on their technical strength and potential usefulness inside a professional club. The event will also include contributions from the club’s data science and football operations teams, Google DeepMind representatives, Como Ventures, and The Players Fund.

The program will cover various aspects of AI tooling, football data, startup innovation, and applied technology in sport. Participants can expect to engage with technical briefings, project presentations, networking opportunities, and sessions on AI tooling and its applications in the sports industry.

Registration for Data on the Lake requires host approval, but applications are now open for interested participants. The event is expected to provide a platform for innovators to showcase their ideas and potentially pilot or scale their solutions within Como 1907’s football ecosystem.

The Association for Advancing Automation (A3) has announced the success of its Automate event, which took place in Chicago from June 22-25. The show drew a record-breaking number of attendees and exhibitors to McCormick Place, with over 50,000 registrants and 1,230 companies showcasing their latest technologies.

The event covered an impressive 425,000 square feet of floor space, featuring the latest innovations in robotics, artificial intelligence (AI), machine vision, motion control, and industrial automation. The record-setting attendance reflects the growing demand for automation solutions across various industries as companies seek to improve productivity, address workforce challenges, strengthen supply chains, and remain competitive.

According to Jeff Burnstein, president of A3, Automate 2026 was ‘the strongest show we have ever had’ in terms of both size and quality. He noted that the event’s energy was palpable throughout McCormick Place, with keynotes packed throughout the week, product launches, live technology demonstrations, and programs focused on emerging professionals drawing strong participation.

The Latin America Networking event and Women’s Empowerment Forum were particularly well-attended, underscoring the industry’s growing focus on leadership, inclusion, and talent development. The conference program also reached a new high, with over 1,600 registrants participating in more than 140 sessions focused on industrial AI, robotics adoption, workforce transformation, U.S. competitiveness, supply chain resilience, and automation beyond traditional manufacturing.

One of the major draws at Automate was the Humanoid Robot Pavilion, sponsored by NVIDIA. This pavilion gave attendees a close look at humanoid robots and the enabling technologies behind them. The new forum also featured live demonstrations and discussions on the latest advancements in this field.

The Automate Startup Challenge saw early-stage robotics and automation companies competing for $10,000 and the title of Champion. Mbodi was named the winner for its platform that allows users to teach industrial robots new tasks through natural language and simple demonstrations.

Other notable highlights from the event include the Automate Innovation Awards, which recognized new products and technologies introduced in 2025. The winners were CeiliX InfinityCrane and SkyRunner in Automation Systems; Standard Bots’ Flux AI in Vision, AI, and Software; and Synapticon ACTILINK-JD featuring POSITRON Safety AI in Components, Hardware, and Enabling Technologies.

The Joseph F. Engelberger Robotics Awards were also presented during the event, recognizing Hiroshi Fujiwara, Executive Director of the Japan Robot Association (JARA), and Robert Little, co-founder of ATI Industrial Automation, as this year’s winners.

Automate 2026 continued to expand its focus on education and workforce development through various programs aimed at students and educators. The Education Pavilion, A3 NextGen Theater, Student Challenge, A3 NextGen Student Tours, and programming focused on curriculum, career pathways, hiring, and workforce development all contributed to the event’s success.

For those unable to attend Automate 2026 in person, a digital library featuring recordings of keynote sessions, theater talks, interviews, and exhibitor spotlights is now available. This resource ensures that the wealth of knowledge shared at the event remains accessible.

The next installment of Automate will take place in Las Vegas from May 10-13, 2027, at the Las Vegas Convention Center. Companies interested in reserving exhibit space or exploring sponsorship opportunities can contact the A3 sales team for more information.

Hospitals in low-income countries struggle to provide high-quality diagnostic imaging due to a shortage of specialists. To address this issue, researchers at the University of Barcelona are developing new AI assistants that can operate offline on low-power devices.

The project aims to improve healthcare outcomes in resource-poor settings where access to advanced medical technology is severely limited. This goal won’t be easy to achieve, given the challenges these hospitals face every day.

Karim Lekadir and his team have been awarded a Proof of Concept grant worth 150,000 euros from the European Research Council. With this funding, they can develop AI-powered conversational assistants that provide real-time guidance to healthcare professionals with minimal training. These systems could change how medical imaging works in low-resource settings.

The current generation of generative AI technologies is designed for high-income countries and well-resourced hospitals. They’re not suited for resource-limited environments because they require cloud connectivity and complex computational power, which can be hard to meet in areas with limited infrastructure. The GenAIMIX project aims to overcome these challenges by creating solutions tailored to the specific needs of low-resource settings.

The new AI assistants will operate offline, use local languages, and provide intuitive guidance to healthcare professionals. This is essential for improving antenatal care through diagnostic imaging in countries like Kenya, where access to advanced medical technology is severely limited. The project’s impact will be evaluated by deploying the system in three rural maternal health clinics in Kenya.

The ultimate goal of GenAIMIX is to expand its coverage to other African countries and improve healthcare outcomes in resource-poor settings. By developing AI assistants that can operate offline on low-power devices, researchers hope to bridge the gap between high-income and low-income countries when it comes to access to advanced medical technology.

The United Arab Emirates is accelerating its efforts to automate government work using artificial intelligence. The country aims to meet a self-imposed deadline to have at least half of public sector tasks automated, and Microsoft’s technology is playing a key role in this endeavor.

A significant milestone has already been reached with the deployment of Microsoft’s Copilot software on 35,000 computers belonging to Abu Dhabi government employees. This move marks an important step towards streamlining administrative processes and improving efficiency.

The UAE’s ambitions for AI-powered automation are being driven by Inception42, a subsidiary of G42, which is building agents that can work in conjunction with Microsoft’s AI platform. These digital assistants will help with tasks such as procurement, ensuring the right quantities of goods and services are purchased at competitive prices to keep the country running smoothly.

The success of this initiative is measured not by its visibility but by its invisibility, according to Ashish Koshy, CEO of Inception42. ‘Success is when it’s invisible,’ he told Semafor in an interview.

With a population of around 11 million, the UAE serves as a testing ground for automating government services at a time when many countries are taking a more cautious approach. Microsoft has already committed $15.2 billion to the country by 2030 and taken a stake in G42, demonstrating its commitment to the region.

Microsoft President Brad Smith previously stated that the company views the UAE as a gateway for computing power and data storage across the wider region. This strategic partnership is expected to have far-reaching implications for the development of AI-powered automation in government services.

The recent Automate 2026 show marked a significant shift in the robotics industry, away from early-stage humanoid hype and towards practical, real-world deployment of physical AI and edge computing. This trend was evident throughout the exhibition floor, where attendees could witness firsthand the latest innovations in software orchestration, digital twins, and advanced kinematics. These technologies are being leveraged to solve labor shortages and preserve vital manufacturing knowledge.

The show featured a range of innovative companies and individuals showcasing their cutting-edge solutions. Boston Dynamics and Agility were among those highlighted for their industrial humanoids, Atlas and Digit, which were displayed in static floor displays. ABB Robotics’ Craig McDonnell discussed physical AI, AI-powered palletizing, and collaborations with NVIDIA, while FANUC showcased real-time motion tracking in assembly, protein processing automation, and natural language robot programming.

Other notable exhibitors included Sereact, whose Mason Coleman addressed zero-shot picking, e-grocery trends, and workforce reallocation. Schneider Electric shared its views on cloud latency limitations and its push for hardware-agnostic, open automation systems. Siemens explained its hybrid edge/cloud approach, use of NVIDIA Omniverse for synthetic data training, and Eigen Engineering Agent platform.

Rockwell Automation’s Ara Surenian introduced FactoryTalk Orchestration following the acquisition of OTTO Motors. Attendees also noted SEW-EURODRIVE’s gantry solutions and Raymond’s focus on moving workers into higher-value roles. Mech-Mind offered a CAD-free, real-time bin-picking demonstration of clear, varied bottles, while Vention’s automation demonstrations were recognized for their accessibility.

Kassow Robots’ Christian Kassow explained the strategic advantages of 7-axis cobots over traditional 6-axis configurations for mobile manipulators and confined spaces. The show also featured a range of interview vignettes with industry experts, providing valuable insights into the latest trends and innovations in AI tools for business.

The Automate 2026 show demonstrated that the robotics industry is moving beyond early-stage hype towards practical applications of physical AI and edge computing. With software orchestration, digital twins, and advanced kinematics leading the way, companies are finding innovative solutions to labor shortages and manufacturing knowledge preservation. As the industry continues to evolve, it will be interesting to see how these technologies shape the future of automation.

The show’s sponsors included Tiger Data, creators of TimescaleDB, which extends Postgres with time-series primitives, columnar storage, and automatic partitioning so queries stay fast on live data. This technology is particularly relevant for companies looking to streamline their database management and improve query performance.

A major shift is underway in the world of finance and tax, driven by ongoing vendor consolidation and the increasing integration of artificial intelligence across the entire office of the CFO. Lucanet has responded to this trend with a family of AI agents designed to automate critical processes such as planning, closing, reporting, and environmental, social, and governance (ESG) analysis.

Elias Apel, CEO at Lucanet, notes that these new agents are part of a broader effort to empower financial leadership with intelligence, speed, and confidence. By streamlining complex workflows, Lucanet aims to enable fast, clear, and strategic decision-making, ultimately paving the way toward autonomous finance and tax operations.

The launch marks a paradigm shift in how mid-market and enterprise finance and tax teams manage their end-to-end workflows. The new platform combines 25-plus years of expertise with cutting-edge AI agents to automate high-friction, high-frequency tasks that have long been a source of frustration for financial professionals.

Lucanet’s Analyst Agent is one example of this technology in action. By performing detailed variance and driver analysis across financial and operational data, the Analyst Agent helps reduce the monthly reporting cycle time by surfacing insightful information and automating complex calculations.

The Close Agent accelerates and automates the complexities of the financial close process, including month-end data imports and validation. This agent is particularly useful for companies with multiple subsidiaries or locations, where manual processes can be slow and error-prone.

Other specialized agents include the Modeler Agent, which allows complex financial and operational models to be rapidly built using natural language; the Tagger Agent, which elevates Lucanet’s XBRL and regulatory tagging capabilities to a fully autonomous level, reducing effort by up to 95%; and the Report Agent, which drafts, enhances, and translates annual reports while checking consistency of language.

The ESG Emission Agent is another key component of the platform. This agent generates greenhouse gas (GHG) footprints up to five times faster than manual processes, providing companies with a more accurate picture of their environmental impact.

Lucanet Lume serves as the conversational layer for these agents, allowing users to describe what they need in natural language and routing tasks intelligently to the appropriate agent. This interface is designed to be user-friendly and accessible, even for those without extensive technical expertise.

The division of labor between deterministic calculations and AI-driven reasoning is a crucial aspect of Lucanet’s platform. According to Kevin Smith, CTO at Lucanet, ‘AI is probabilistic in nature and so not suitable to perform deterministic calculations.’ Instead, the core platform handles these tasks while the AI layer manages interpretation, explanation, and natural language processing.

The ultimate goal of this technology is to provide intelligence inside finance and tax that is trustworthy, traceable, and defensible. As Smith notes, ‘intelligence must be trustworthy in front of an auditor,’ a sentiment echoed by Apel’s emphasis on empowering financial leadership with confidence.

Nomagic, an innovative company based in Warsaw, Poland, and Sandy Springs, Georgia, has made significant strides in the field of embodied artificial intelligence. The company’s approach to developing AI systems for robots is distinct from that of its competitors, focusing on creating models that excel at specific tasks right out of the box rather than general-purpose ‘robot brains.’ This strategy aims to eventually build towards a more comprehensive system by mastering individual tasks first.

The concept of embodied AI has gained traction in Silicon Valley, with investors betting big on systems that can interact with the physical world through robotic devices. Many startups are working on developing general-purpose AI models for robots, which would enable them to perform various tasks without extensive programming. However, these models often fall short of human-level accuracy and require significant task-specific training before achieving reliable results.

Nomagic’s approach is centered around creating highly accurate AI robot brains that can tackle specific challenges with ease. To pursue this goal, the company established an AI research lab led by Markus Wulfmeier, a former Google DeepMind robotics researcher who now serves as Nomagic’s chief scientist. This move marked a significant step towards developing more effective and efficient AI systems for robots.

Nomagic has recently announced that it has successfully deployed its first vision-language-action (VLA) model to paying customers. The VLA system is designed to perceive objects in the world, understand text-based instructions from humans, and take actions accordingly. This achievement makes Nomagic one of the pioneers in running VLAs in a live production environment rather than lab experiments or staged demos.

The early results of Nomagic’s VLA deployment are promising, with the company reporting that it has roughly halved the rate of robot-caused interventions in warehouse operations. The system is being used by Brack.Alltron, Switzerland’s second-largest e-commerce platform, which relies on robots from Nomagic to automate order picking and packing tasks.

Roland Brack, founder and owner of Brack.Alltron, expressed his enthusiasm for Nomagic’s VLA systems, stating that they have marked a significant step forward in the company’s automation efforts. According to Brack, the addition of these intelligent systems has enabled them to run autonomous shifts through nights and Sundays without increasing pressure on their human workforce.

Despite its success, Nomagic acknowledges that its VLA system is not yet perfect, with an accuracy rate below 99.9% in specific tasks. However, the company has developed a system around the VLA by integrating it with older ‘classical’ robotics software that acts as a safety net and error catcher. This approach ensures that the entire system can be trusted to operate reliably in customer warehouses.

Kacper Nowicki, Nomagic’s co-founder and CEO, explained that achieving high reliability is crucial for companies operating in the physical world. He emphasized that 99.9% accuracy is not just a marketing number but rather the minimum required to gain entry into most facilities. To address this challenge, Nomagic has developed a harness system that complements its VLA model, allowing it to operate safely and efficiently from day one.

Nomagic’s approach stands in contrast to many of its competitors, which focus on developing general-purpose AI models for robots. Markus Wulfmeier, chief scientist at Nomagic, highlighted the limitations of this strategy, stating that most companies are racing to build highly versatile robot brains without considering the practical challenges of mastering specific tasks.

Wulfmeier pointed out that the physical world is dominated by a long tail of rare situations, making it difficult for AI models to achieve high accuracy. He emphasized that relying on simulation or human teleoperation alone cannot economically close the remaining gap to the level required in real-world settings. Instead, Nomagic trains its VLA model using data from its existing fleet of robots deployed with customers.

Nomagic’s unique advantage lies in its ability to gather vast amounts of real-world data from its operational robots. This data is used to train and refine the VLA model, which Wulfmeier describes as unusually rich and diverse. By leveraging this approach, Nomagic aims to develop more effective AI systems for robots that can automate tasks with greater accuracy.

Tristan d’Orgeval, co-founder and chief strategy officer at Nomagic, stressed the importance of deploying robots in real-world settings first rather than relying on lab experiments or simulations. He noted that this approach allows companies like Nomagic to develop capable AI systems that emerge from practical experience rather than theoretical models.

The company’s focus on automation has earned it recognition within the industry. Recently, Nomagic won the 2026 International Intralogistics and Forklift Truck of the Year (IFOY) Award for its Shoebox Picker device. This achievement highlights the potential of Nomagic’s AI systems to tackle complex challenges in warehouse automation.

Nomagic’s approach marks a significant departure from traditional methods used by many companies working on embodied AI. By focusing on developing highly accurate models that excel at specific tasks, Nomagic aims to create more effective and efficient AI systems for robots.

The recent Automate show in Chicago has left a lasting impression on the industry. The event was marked by high energy levels, dense crowds, and an unmistakable sense of urgency among exhibitors. It’s clear that the automation landscape is undergoing significant changes, with many feeling compelled to adapt or risk being left behind.

One of the key takeaways from the show is that various factors are now converging to drive a US manufacturing renaissance. Policy pressure, onshoring, manufacturing investment, labor constraints, and usable AI with improved reliability are all reinforcing each other, rather than moving in separate directions. This convergence has created an environment where innovation can thrive.

The Monday keynote speakers at Automate 2026 provided valuable insights into the changing nature of manufacturing. Andre Marino from Schneider emphasized that the US cannot compete with major manufacturing centers like China solely on capacity or labor costs. To achieve a ‘manufacturing renaissance’ in the US, it must focus on efficiency, connectivity, and innovation. However, this requires more than just investing in new factories; it demands better factories.

Mike Cicco added to the argument by highlighting that the barrier to automation is decreasing as AI makes systems easier to deploy and use. When combined with North American onshoring pressure, everything starts to fall into place. The forces driving change are no longer theoretical but practical, visible, and increasingly urgent.

The old model of industrial automation is losing its grip. Matt Moschner described why this shift feels so significant: the complex systems of the past were only as good as the day they were deployed. They worked beautifully until the environment changed, the product changed, or the labor model changed – then there was an expensive rip-and-replace standing in the way of competitiveness.

Now, the promise is different. Systems are becoming simpler and more robust, capable of learning and improving over time. AI makes incremental training possible, allowing models to be refined without turning every change into a massive reintegration exercise. This doesn’t mean industrial automation has become easy; it means the old tradeoff between capability and flexibility is starting to break down.

The industry needs to stop treating AI as just another software feature. It’s increasingly becoming a way to make automation less static, with the real shift being that machines are becoming more adaptable in practice rather than simply ‘intelligent’ in theory.

Software-defined manufacturing was a recurring theme throughout the event. Wendy Tan framed it not as whether AI matters but how to make it useful in production. For too long, the industry struggled to move from AI discussion to industrial impact. Her argument is that the time has finally come for software to leverage existing assets and investments, making hardware do things it couldn’t before.

Wendy also made another crucial point: the clunky user experience of traditional industrial automation systems needs to change. If industrial automation is going to scale broadly, it cannot remain an expert-only craft built on obscure workflows and heroic engineering effort. The next phase of adoption depends on simple workflows, ease of use, and a more standardized, software-driven path from intent to execution.

The Automate 2026 panel described a clear sense of urgency driven by tariffs, trade disruption, workforce shortages, and general economic instability. Their response? To become more agile and automate what can be automated – uncertainty is not a reason to wait out the storm but rather a reason to move now before it’s too late.

The old model tolerated long pilots, year-long science projects, and endless experimentation because the operating environment felt more stable. However, if the market is moving fast, policy is changing rapidly, and supply chains remain volatile, then speed and flexibility become part of the value proposition – a crucial aspect for manufacturers to consider in today’s landscape.

Andre Marino warned against underinvesting: there is still plenty of hype in the market, but waiting too long carries its own strategic cost. The industry needs to strike a balance between caution and timely investment to reap the benefits of automation and AI.

The big questions about AI are trust, reliability, safety, and lifecycle validation – crucial aspects that Mike Cicco highlighted as essential for industrial adoption. He emphasized that AI should be combined with hardcoded, reliable systems that the industry already trusts rather than trying to replace them entirely. This approach will enable AI to add value where it matters most.

The Automate 2026 event has left a lasting impact on the industry, highlighting the need for manufacturers to adapt and innovate in response to changing market conditions. As we move forward, it’s essential to address the challenges of automation and AI adoption while leveraging their potential benefits – driving growth, improving efficiency, and enhancing competitiveness.

The future of industrial robotics and physical intelligence will be crucial topics for discussion as the industry continues to evolve. Manufacturers must prioritize innovation, adaptability, and collaboration to stay ahead in today’s fast-paced landscape.

As Amazon’s Prime Day 2026 gets underway, a significant shift in consumer behavior is becoming increasingly apparent. The four-day event, running from June 23rd to 26th, marks a moment when an increasing number of consumers are turning to AI assistants for product research and recommendations rather than traditional search engines or browsing websites. This change in behavior has created a new advertising surface that brands can leverage to reach shoppers at the point of decision-making.

Thrad, an AI-native advertising infrastructure platform, is enabling brands to tap into this emerging trend by placing paid ads within AI conversations. These ads appear as relevant and clearly labelled recommendations inside exchanges between consumers and their AI assistants, providing a more targeted approach than traditional keyword-based search engine marketing. When a shopper asks an assistant for the best noise-cancelling headphones under £200 in the Prime Day sales, the query carries far greater intent than a generic keyword search.

The platform’s advantage lies in its ability to reach across the conversational AI ecosystem rather than being limited to a single destination or platform. Thrad serves native in-chat ads across a network of over 50 AI publishers, spanning general assistants and vertical AI tools in categories such as beauty, electronics, travel, and lifestyle. This comprehensive approach allows brands to connect with consumers at multiple touchpoints throughout their research journey.

Thrad’s platform also enables brands to run ads inside ChatGPT through the same interface, providing a seamless way for advertisers to reach users across various AI tools. Campaigns can go live on the same day, and real-time bidding scores every placement against the live conversation, ensuring that ads are delivered at the most relevant moments. The company already serves millions of ads per day for Fortune 500 advertisers and growth-stage brands.

The timing of this development reflects a broader trend in marketing budgets shifting towards AI initiatives. According to Gartner’s 2026 CMO Spend Survey, published in May, chief marketing officers are now allocating an average of 15.3% of their budgets to AI projects. As AI assistants become increasingly prominent as primary surfaces for product discovery, brands that learn to operate effectively within conversational environments during events like Prime Day will be well-positioned to capitalize on this rapidly expanding channel.

Thrad has published a comprehensive guide to help brands navigate the opportunity presented by Prime Day and beyond. The guide outlines how to plan, launch, and measure conversational AI campaigns through the event window and in the days that follow. Notably, many shoppers continue their research during sales events and make purchases within 48-72 hours afterwards – a period where conversational ads often deliver strong returns.

Thrad’s CEO and co-founder, Andrea F. Tortella, emphasizes the significance of this shift: ‘Shopping is becoming a conversation, and Prime Day is the clearest signal yet of that change.’ She notes that people are increasingly turning to AI assistants for recommendations at the point of decision-making, making it essential for brands to be present in these conversations naturally across the entire AI ecosystem rather than focusing on individual platforms.

Roger Dunn, chief commercial officer at Thrad, highlights the value proposition of conversational advertising: ‘Retail media works because it reaches people at the moment they are about to purchase. AI conversation advertising takes this a step further by reaching them when they’re asking what to buy or comparing options – before scrolling past a shelf.’ He emphasizes that Prime Day represents an ideal opportunity for brands to be genuinely helpful in these exchanges and capitalize on the growing importance of conversational marketing.

In light of Thrad’s efforts, it is clear that AI assistants are becoming increasingly integral to consumer behavior during major retail events like Prime Day. As this trend continues to evolve, brands will need to adapt their strategies to effectively engage with consumers through conversational interfaces.

Dukascopy Bank has taken a significant step forward in trading technology by launching an AI-powered feature that enables clients to interact with their trading accounts through chat-based assistants. This innovative system allows users to execute trades, manage positions, analyze markets, and monitor risk using natural language instructions.

The bank’s decision to integrate AI assistants into its platform reflects a broader shift away from traditional trading interfaces. Clients will no longer need to rely on terminals, manual coding, or complex navigation to perform trading operations. Instead, they can use chat-based assistants like ChatGPT and Claude to manage their accounts.

Dukascopy Bank’s recent expansion of its trading infrastructure has laid the groundwork for this new feature. The Swiss broker-bank introduced a flagship mobile application that combines banking services, retail CFD trading, foreign exchange, payments, and investment tools in a single interface. It also launched a dedicated stock trading platform offering more than 25,000 equity CFDs.

The system runs alongside the bank’s JForex ecosystem as part of a modular execution structure. This setup allows users to access various features and tools without having to navigate complex menus or interfaces. The integration with AI assistants is designed to be seamless, enabling clients to execute trades and manage their positions quickly and efficiently.

Dukascopy Bank CEO Andre Duka emphasized the importance of technology in driving innovation at the bank. ‘Technology has always been at the core of Dukascopy’s DNA,’ he said. The company has invested heavily in building advanced trading infrastructure over the past two decades, and this new feature is a testament to that commitment.

The integration with AI assistants allows for a range of trading actions through chat instructions. Users can place orders, analyze instruments, calculate stop-loss and take-profit levels, and check account exposure using natural language commands. For example, they can instruct their AI assistant to ‘Close half of my gold position’ or ‘Analyze the current market trends in the S&P 500 index’.

The system is built on the Model Context Protocol, an open standard that enables AI tools to interact with external applications. This protocol allows for secure access to trading functions and account data, enabling workflows such as proportional position sizing and cross-asset analysis. The setup process takes only a few minutes and does not require programming knowledge.

The bank has taken steps to ensure the security of client accounts by not sharing passwords with AI assistants. Users can connect their AI assistant directly to their JForex trading account through a Model Context Protocol server developed by Dukascopy Bank. This allows for secure access to trading functions and account data without compromising client confidentiality.

The AI-powered feature is currently available on JForex demo accounts using virtual funds. Support for live trading accounts is planned for summer to autumn 2026, allowing clients to take advantage of this innovative technology in their real-world trading activities.

AQSolotl, a specialist in quantum computing hardware and integrated solutions, has formed a strategic partnership with QuantrolOx, developer of automated machine learning-based control software for quantum technologies. The collaboration aims to accelerate the shift from experimental quantum systems to scalable, production-ready infrastructure by integrating AQSolotl’s Chronos-Q control system with QuantrolOx’s Quantum EDGE platform.

The unified stack created through this partnership is designed to reduce manual calibration and improve system stability in quantum computing. By combining high-speed control hardware with automated machine learning-driven workflows, the joint solution aims to deliver a more efficient operating environment for quantum engineers and researchers. This integration will significantly shorten tuning cycles and enable more consistent qubit performance.

One of the sector’s most persistent challenges is the reliance on manual, time-intensive system tuning. As quantum processors scale beyond early-stage prototypes, increasing qubit counts and tighter performance thresholds make manual calibration impractical. The industry is shifting towards automation and integrated control layers to improve reliability, reduce downtime, and enable continuous operation.

The partnership between AQSolotl and QuantrolOx will be executed in two phases. In the immediate term, the companies plan to deploy AQSolotl’s hardware on the QuantrolOx Quantum Testbed and integrate automated control workflows. This joint solution is expected to support emerging workloads such as quantum AI training, where repeatability and system fidelity are critical.

Vishal Chatrath, CEO and Co-Founder of QuantrolOx, emphasized that automation is becoming essential as quantum systems grow in complexity. ‘By bringing machine learning-driven control directly into AQSolotl’s hardware layer,’ he said, ‘we can enable more stable and repeatable performance with significantly less manual intervention.’

AQSolotl specializes in making advanced quantum technology accessible and practical for industry through its modular quantum control platforms and deployable quantum AI solutions. The company focuses on adaptability, cost-effectiveness, and seamless integration with classical computing to address complex computational problems in sectors such as finance, pharmaceuticals, energy, and logistics.

QuantrolOx develops automated machine learning-based control software that is technology agnostic and applicable to all types of quantum technologies. Their goal is to maximize quantum computer uptime and make their hardware more accessible by automating the tuning process.