The Indian workers training AI robots to take their jobs

AFP
June 11, 2026 

Indian housewife Nagireddy Sriramyachandra wears a smartphone on her head as she records her actions through motion capture while slicing mangoes at her home in Chennai – Copyright AFP R.Satish BABU


With a smartphone strapped to her head, Indian housewife Nagireddy Sriramyachandra films herself slicing mangoes to train AI-powered robots to take on household jobs in the future.

Earning just over two dollars for an hour of video, her mundane recordings are invaluable for global tech companies teaching machines how to move like humans in the real world.

The 25-year-old is one of a growing army of thousands of AI system trainers in the world’s most populous country.

“Who else will give you 250 rupees an hour just for doing housework?” said Sriramyachandra from her kitchen in Chennai in southern India’s Tamil Nadu state.

“I may get a robot myself in the future,” she added.

Artificial intelligence chatbots and image generators crunch reams of digital data, but building systems to navigate real-life environments is more challenging.

Developers think feeding first-person footage, called “egocentric data”, into specialised AI models will help robots copy humans.

Some AI trainers work at home, others in factories or specialised studios — using video glasses, head-mounted cameras and motion sensors.

“It blares ‘hands not detected’ when I’m not recording properly,” said Sriramyachandra, who sends recordings via a special app to the AI data company Objectways.

The firm, which has offices in India and the United States, lists Fortune 500 multinationals as clients. It works with Amazon SageMaker, a platform for machine learning models.



– ‘Better things’ –



The humanoid robot market is booming, with investment bank Morgan Stanley predicting there could be over a billion in use by 2050, mostly for industrial and commercial purposes.

“Folding clothes, coffee making… cooking a very specific thing, sandwich making,” Objectways head Ravi Shankar said, listing videos requested by clients.

“Some jobs are supposed to be taken over, so humans can go and do better things.”

In India, the emerging field of spatial AI is providing new employment — for now.

The 50-year-old CEO is US-based, but hires workers from Tamil Nadu, where he grew up, one of India’s international technology hubs.

At a Karur textile factory, busy with workers attaching labels to caps and ironing cloth bags, AFP saw eight people wearing head cameras and smart glasses supplied by Objectways.

India has positioned itself as a global middleman for the creation, processing and annotation of AI data.

“It’s likely that these data collection services will increase”, said digital labour expert Aditi Surie, from the Indian Institute for Human Settlements in Bengaluru.



– Informal workers –



India is aggressively developing its AI industry, but its leaders are aware that, alongside the technology’s much-hyped benefits, automation poses risks.

Government think-tank NITI Aayog said that most discussions around artificial intelligence and labour “focus on white-collar professionals and predict an almost certain loss of jobs in the segment” without urgent action.

“Little attention, if any, is paid to how AI can serve India’s 490 million informal workers, the very people who form the backbone of our economy,” it said in a report released ahead of a global AI summit in India this year.

The think-tank has examined how the technology could help or harm dozens of professions — from cobblers to sewer cleaners, farmers to tea sellers.

For the last decade, 55-year-old Ponni has sat on a roadside in Bengaluru, the city known as India’s Silicon Valley, making flower garlands.

She, too, has been paid to have a phone strapped to her forehead.

“The next generation… who might have to do work similar to mine — they will face a problem,” Ponni said.



– Always wearing a camera –



At an Objectways studio, AI system trainers film themselves performing household tasks in fake, fully furnished apartment rooms.

After several thousand hours of filming, the wallpaper is changed to provide clients with variety.

“Today I sit here, tomorrow I stand there,” said engineering graduate Rani N., 21, on a break from filming herself, once again, folding a towel.

Each video lasts about four minutes, and she records around 90 a day — on nearly every conceivable spot on the bed.

She says the job is “tolerable”, but feels like she’s always wearing a camera.

In other rooms, colleagues arranged pencil sharpeners, water bottles and crayons in patterns, recording with depth-sensor cameras.

Qanat Consulting Services in Andhra Pradesh, an Objectways subcontractor, supplies about a dozen larger data firms with recordings.

Some of its 2,000 contributors perform tasks with motion-sensor bands on their “wrists, hands and legs”, CEO Thaslim Pattan said.

Manish Agarwal of Bengaluru-based Humyn Labs, not related to Objectways, records conversations as well as videos.

Contributors discuss assigned topics — ranging from politics to entertainment — for clients wanting to process speech patterns.

Agarwal denies that robots will steal jobs, believing that networks of humans and robots “will work together” one day, he said.

“A welder in India could be managing a welder-robot in Prague,” he said.

Struggling German auto supplier Bosch pivots to robots

AFP
June 10, 2026

Bosch says the market for its sensors that are used in robots is growing rapidly – Copyright AFP/File RONALDO SCHEMIDT

German industrial giant Bosch said Wednesday it will step up efforts in the field of humanoid robotics, as its traditional auto parts business comes under increasing pressure.

The world’s biggest auto supplier, Bosch makes everything from braking systems to sensors, but has suffered as European carmakers battle fierce overseas competition and weak demand.

However, the rise of humanoid robots, powered with generative AI models and capable of performing complex tasks, offers an opening for the group, chief executive Stefan Hartung said.

“With the advent of humanoid robotics, the demand for Bosch components and solutions is increasing,” he said in a statement.

The market for specialised MEMS sensors is expected to grow to over $19.2 billion by 2030 and hit an annual growth rate of four percent, according a study by consultancy Yole Group, which was presented by Bosch.

Bosch is a key producer of the tiny sensors, which are crucial in robotics.

At an event in Berlin, Hartung stressed the importance of the components in improving the dexterity of robots.

These sensors determine whether the robot “should tighten its grip or not, whether it is dealing with a sturdy object, or whether it needs to act delicately because it is an egg,” he said.

“Humans have four million touch sensors. If we were to build robots equipped with as many sensors, four years of global sensor production would barely be enough to equip 12,500 robots,” he added.

The focus on automation is also meant to boost the competitiveness of Bosch’s German factories and plug shortages of skilled labour.

Bosch, also known for making a wide range of industrial equipment and household appliances, struck a deal with German robotics firm Neura in January to gather data on factory work.

Under the partnership, several thousand workers in some of Bosch’s 350 facilities worldwide will wear sensor suits to glean training data for Neura robots.

Neura said Wednesday it had raised up to $1.4 billion in fresh fundraising from backers including Bosch, chip giant Nvidia, Amazon and crypto group Tether.

The funding will be used to accelerate Neura’s activities, ranging from the deployment of robots to the rollout of “gyms” for clients to train bots, and the development of the company’s physical AI systems.

The factory floor’s last manual process gets an AI upgrade

Jon Stojan

DIGITAL JOURNAL
June 10, 2026 

Photo courtesy of Envato.

Opinions expressed by Digital Journal contributors are their own.

Inside an RV plant in northern Indiana, a curved fiberglass countertop that used to take an hour to hand-finish now leaves the line in six minutes. Sanding and polishing a complex surface to a uniform sheen had long resisted autonomous finishing, and for a counterintuitive reason: the variation inherent in geometry and surface condition made it the hardest job on the floor to teach a machine. By 2023, more than four million industrial robots were operating on factory floors worldwide, according to the International Federation of Robotics’ World Robotics report. While CNC machining handled the cuts and robotic assembly handled the bolts, surface finishing held out longer than either of them.

The floor’s last rule: Hands only

Every autonomous process on a factory floor follows a predetermined path:

A weld traces the seam an engineer drew
A mill follows the tool path a CAM system generated
Even bin-picking, long the benchmark problem for difficult autonomous applications, became tractable once vision systems learned depth perception

Surface finishing has no such path. Part geometry and material thickness vary. The surface flaws on any given part, such as a small ridge left by a mold or a soft spot in the gel coat, also vary from piece to piece. A skilled finishing operator senses pressure through the wrist, hears the tone of the abrasive shift, watches resin dust change color as it heats, and adjusts continuously. That kind of real-time adaptive judgment can’t be reduced to a routine written months earlier in an engineering office.

GrayMatter Robotics, a Physical AI company building Factory SuperIntelligence (FSI) for manufacturing, deploys autonomous finishing cells designed for exactly that constraint. Where software AI systems learn from internet data, Physical AI systems operate in and learn from the physical world. GrayMatter Robotics’ autonomous finishing cells draw on ATLAS, the company’s proprietary data regime comprising 7 petabytes of real-world surface finishing data accumulated across 30 million square feet, 20-plus industries and 11-plus sensing modalities. That foundation develops Process Intelligence, the learned understanding of how tools, materials and surfaces interact under real manufacturing conditions, enabling the system to adapt in real time to whatever geometry and surface condition the part presents, without pre-programming. The result is closer to what a craftsperson does than to what a conventional robot does.

The craft behind the calluses

A traditional finishing apprenticeship begins with feel. The early months are less about technique than about calibration: learning how the body interprets pressure and how the same motion produces different results under different conditions. Apprentices learn abrasive selection and progression. They learn how a given resin responds to heat and how ambient humidity affects how a coating lays. That education has genuine value, but it requires four to six months to reach a productive level and years to reach mastery. In a labor market where manufacturing competes against every sector for the same generation of workers, that timeline is increasingly difficult to sustain.

“Surface finishing has always been treated as an art, something you learn through years of practice. But it is physics, and once you model it correctly, you can build systems that learn and adapt in ways that traditional robots can’t,” said Ariyan Kabir, Co-Founder & CEO of GrayMatter Robotics. “The breakthrough for us came when we realized that the skill operators develop over years is really their internalized understanding of physics in action. Encode that physics in software and you can deploy that capability anywhere.”

Autonomous finishing cells change what operator training needs to produce. Workers who once required deep manual craft now need systems fluency, the understanding of what the machine is doing and why, rather than the physical capacity to replicate it.

Five jobs gone, or five jobs changed?

The arithmetic that initially looks like subtraction often resolves differently. When a single autonomous cell handles the work that previously occupied six finishers, the instinct is to read that as five positions eliminated. In practice, facilities that deploy these systems tend to expand rather than contract. The throughput gains that justify autonomous finishing are the same gains that make it possible to take on volumes previously out of reach. Two or three cells running in parallel require upstream support and operational oversight the prior headcount wasn’t providing. The floor is busier, not quieter, and the roles shift accordingly.

A second force is accelerating this transition. CAD and CAM tools have made it cheaper to design parts with curves and undercuts that an engineer would have avoided a decade ago because no one could finish them consistently at volume. Research published in Materials found that conventional CNC machining strategies relying on fixed step sizes are inherently inefficient for surfaces with rapidly varying curvature and that aligning tool paths to local surface geometry reduced form error by 48.4% in a single pass.

Facilities still relying on manual finishing are quietly discovering that the geometry coming out of engineering has outpaced what a human finisher can produce consistently at scale. Geometry-agnostic autonomous systems convert that pressure into a competitive advantage. The complex surface that once represented a bottleneck becomes another part moving through the queue.

FAQs

1. Why has surface finishing resisted autonomous solutions longer than other manufacturing processes?

Every other major manufacturing process follows a predetermined path, but surface finishing doesn’t. Geometry variation is the primary factor: no two parts present exactly the same surface, and materials behave differently under heat and pressure. Surface flaws vary part to part, and pre-programmed systems that execute fixed instructions lack the continuous adaptive judgment that finishing requires.

2. How do vision systems enable robots to adapt to different part geometries?

Vision systems allow autonomous finishing cells to read each part’s actual geometry in real time rather than executing a path written to a CAD model. Because part dimensions vary within normal manufacturing tolerances, a system that adapts to what is physically present rather than what the drawing specifies handles geometry variation and surface irregularities without manual reprogramming between parts.

3. What integration challenges should manufacturers expect when adding autonomous finishing to existing production lines?

The most common issues involve floor space allocation, electrical and pneumatic capacity, and workflow sequencing. Autonomous finishing cells are designed as standalone stations that fit into existing layouts without requiring line redesigns. Most facilities run autonomous and manual processes in parallel during an initial validation period before transitioning fully.

Thinking before picking: Smarter harvest robots and the impact on modern farming

Dr. Tim Sandle

DIGITAL JOURNAL

June 6, 2026

Sprinklers water a lettuce field in California’s Imperial Valley, a vital part of America’s huge agricultural sector, in February 2023 – Copyright AFP FOCKE STRANGMANN

Robotic harvesting has long been positioned as a solution to one of agriculture’s most persistent challenges, which is labour shortages. But while automation has advanced rapidly in areas such as seeding, spraying, and monitoring, harvesting delicate crops has remained a stubborn frontier. Tomatoes exemplify the problem. They grow in dense clusters, ripen unevenly, and are easily damaged, making them difficult targets for machines that rely on simple detection and repetitive motion.

A new research effort from Osaka Metropolitan University suggests a shift in approach. Rather than asking whether a robot can identify and pick a tomato, the system evaluates how easy it will be to harvest each fruit before attempting the task. This change, from detection to decision-making, signals a broader evolution in agricultural robotics, one that aligns more closely with the realities of commercial farming.

Most harvesting robots today are built around visual recognition systems. Cameras and machine learning models identify ripe fruit, then robotic arms attempt to pick it. The limitation is that identification alone does not account for the physical complexity of the plant environment. A tomato may be fully ripe but partially hidden, blocked by stems, or positioned in a way that increases the risk of damage.

Assistant Professor Takuya Fujinaga’s system introduces what can be described as a “harvest probability” model. The robot analyses multiple variables, variations like fruit position, stem orientation, surrounding leaves, and occlusion, before calculating the likelihood of a successful pick. It then selects the approach angle most likely to succeed.

In testing, this method delivered an 81% success rate, with a notable proportion of successful picks coming after the robot adjusted its strategy mid-task. When a frontal approach failed, the system recalculated and attempted a side-angle harvest, demonstrating a level of adaptive behaviour that has been largely absent from earlier systems. For agricultural operators, this represents a move toward machines that behave less like automated tools and more like semi-autonomous workers capable of situational decision-making.

Why this matters for commercial farming:

Harvest efficiency is not simply about speed; it is about yield quality, labour substitution, and operational continuity. Failed picks can damage fruit, slow down processes, and reduce overall productivity. By introducing a system that evaluates how likely a task is to succeed before execution, the research addresses several operational constraints:Reduced damage rates, as difficult picks can be avoided or approached differently
Higher overall throughput, as time is not wasted on low-probability attempts
Improved resource allocation, allowing robots to focus on high-yield tasks

This approach aligns with how human pickers operate. Experienced workers instinctively assess whether a tomato can be picked cleanly and adjust their movements accordingly. Translating this judgement into machine logic is a significant step toward closing the gap between human and robotic performance.

Canadian agriculture: early signals of a similar shift

While the specific “harvest-ease” framework is emerging from Japan, Canadian agriculture has already been moving in a similar direction, particularly in greenhouse operations.

In Ontario and British Columbia, greenhouse tomato producers have invested heavily in automation, driven by labour shortages and rising costs. Companies such as Nature Fresh Farms and SunSelect Produce have adopted advanced environmental controls, robotics, and AI-based crop monitoring. Although most harvesting in these facilities is still human-led, there is growing experimentation with robotic assistance systems.

Canadian agricultural technology firms have also begun to focus on decision-support rather than pure automation. For example startups in Ontario have developed vision systems that assess fruit ripeness, orientation, and picking readiness, feeding this information to human workers or semi-automated tools. With a different development, robotics developers in Quebec have explored adaptive gripping systems that adjust force and angle based on fruit characteristics. Furthermore, greenhouse operators are trialling AI-driven crop analytics to prioritise which sections of a crop should be harvested first

These efforts reflect a broader trend: automation in agriculture is shifting away from rigid, pre-programmed actions toward systems that evaluate conditions and adapt in real time.

Human-robot collaboration, not replacement

One of the more practical implications of the research is the model of shared workload it enables. Rather than replacing human labour entirely, the system supports a division of effort. Here, robots handle straightforward, high-probability picks and human workers focus on complex, delicate, or obstructed fruit.

This hybrid model is already emerging in Canadian greenhouses, where labour shortages have made full staffing difficult. In British Columbia’s Lower Mainland, growers have reported challenges in maintaining consistent harvest teams, leading to interest in technologies that can stabilise output rather than eliminate labour altogether. A robot that can filter out easy tasks and leave only the more complex work for humans reshapes productivity. Instead of matching human performance across all tasks, robots can contribute where they are most effective, improving overall efficiency without requiring full autonomy.

A critical next step for systems like Fujinaga’s is moving beyond controlled test environments into real farms, where variability is the norm. Factors such as changing light conditions, plant density, humidity, and unexpected obstructions introduce layers of complexity.

Canadian greenhouse operations, with their controlled yet dynamic environments, may provide an ideal testing ground. These facilities already maintain detailed environmental data and standardised crop layouts, making them suitable for integrating adaptive robotic systems. Field agriculture presents a greater challenge. Outdoor conditions introduce variability that requires even more sophisticated decision-making. However, the principle of assessing task difficulty before execution remains applicable across both settings.

Design implications for agricultural technology

The concept of “harvest-ease estimation” has broader implications for how agricultural robots are designed. This leads to systems built to prioritise tasks based on probability of success, rather than attempting all tasks equally. It is similarly important that robots adjust their approach dynamically, rather than relying on fixed movement patterns. These forms of decision-making models can be combined with crop monitoring systems to optimise harvesting schedules.

The idea of a robot “thinking before acting” may seem incremental, but it marks a shift in how automation is conceptualised in agriculture. Instead of focusing solely on mechanical capability or visual recognition, the emphasis moves toward decision-making under uncertainty. For Canadian farmers, particularly those operating in high-value greenhouse sectors, this aligns with ongoing efforts to modernise production without compromising quality. As automation becomes more adaptive, the barrier to adoption may lower, opening the door to wider deployment across farms of varying sizes.

In practical terms, this approach supports more efficient harvesting, reduced waste, and a clearer path toward collaboration between human workers and machines. For an industry managing labour constraints, cost pressures, and the need for consistent output, the ability to prioritise and adapt may prove as important as the ability to automate itself.

AI hype becomes the latest weapon in cybercrime

Dr. Tim Sandle

DIGITAL JOURNAL
June 10, 2026

The cyberattacks had allegedly targeted MPs from several parties. — © AFP/File ADEK BERRY

The rapid adoption of artificial intelligence across business and society is reshaping not only how organisations operate, but also how they are attacked. A recent Microsoft Threat Intelligence report highlights a notable shift: cybercriminals are increasingly impersonating well‑known AI brands such as ChatGPT, Microsoft Copilot, Anthropic’s Claude, and DeepSeek as part of sophisticated social‑engineering campaigns designed to steal credentials, distribute malware, and commit fraud.

At first glance, the tactic appears novel. In reality, it represents an evolution rather than a revolution. The underlying attack methods remain familiar—phishing emails, malicious links, fake downloads, and impersonation—but the context has changed. As Microsoft observes, attackers are “leveraging the wider global interest around AI itself as a social engineering lure,” exploiting both the trust associated with these platforms and the urgency surrounding their adoption. What is different is scale, timing, and psychological leverage.

Familiar attacks, new context

Microsoft’s findings show that these campaigns rely on long‑established techniques: urgency‑driven messaging, impersonation of trusted brands, and increasingly complex redirection chains that pass through legitimate services to evade detection. The aim is simple—convince the user that the message is both credible and timely.

Examples range from fake billing notices for ChatGPT subscriptions to fraudulent “early access” downloads for newly released AI models. In one documented case, a DeepSeek V4 launch was mirrored by a malicious GitHub repository appearing within minutes, ranking highly in search results and distributing infostealer malware to unsuspecting users.

Such campaigns illustrate how attackers are aligning themselves with real‑time technology trends. Instead of sending generic phishing emails, they are inserting themselves directly into the innovation cycle. The faster a technology gains popularity, the quicker it becomes a target.

Importantly, Microsoft emphasises that these attacks do not involve vulnerabilities in the AI platforms themselves. They are deception campaigns—purely human‑focused exploits designed to bypass technical controls by manipulating behaviour.

The effectiveness of these attacks lies in a subtle shift in psychology. Traditional phishing relies heavily on fear, such as account suspension warnings or financial penalties. AI‑themed phishing, however, often leverages curiosity and professional ambition.

Mayank Kumar, Founding AI Engineer at DeepTempo, captures this dynamic succinctly, in conversation with Digital Journal: the core mechanics are unchanged, but the “blast radius” has expanded. In his view, the difference is that users do not yet have intuitive instincts for how legitimate AI‑related communications should look. A fake banking email may raise suspicion; a message offering access to a new AI model often does not.

This shift matters. In many cases, the victim is no longer reacting defensively to a perceived threat, but proactively engaging with something they believe offers value. The dynamic flips—from attacker‑initiated deception to user‑initiated interaction. As Kumar notes, in the case of tools or installers, “the victim walks to the lure.”

The current AI hype cycle amplifies this effect. New tools are launched rapidly, often with limited or evolving official communication channels. This creates ambiguity around what is legitimate, providing an opportunity for attackers to insert convincing fakes into the gap.

John Joyner, Senior Director of Technology at Corsica Technologies, reinforces this point in a statement sent to Digital Journal. According to Joyner, AI‑themed phishing succeeds because people are still trying to determine which tools are real and relevant. This uncertainty makes fake notifications, such as billing alerts or account warnings, appear normal and credible within a fast‑moving digital environment.

Trust, speed, and automation

Beyond psychology, AI is also enhancing the mechanics of cyberattacks. Threat actors are increasingly using AI to create more convincing phishing emails, automate campaigns, and personalise messaging at scale.

This evolution is part of a broader trend. AI‑driven social engineering is now widely regarded as one of the most significant cybersecurity threats facing organisations. Experts note that attacks are becoming more realistic and harder to detect, blending traditional techniques with automation and adaptive targeting.

The Microsoft report aligns with this trajectory. Campaigns increasingly incorporate features such as CAPTCHA‑style interactions to evade automated analysis, adversary‑in‑the‑middle techniques to capture authentication tokens, and multi‑stage redirection chains that conceal the final malicious destination.

The implications are significant. Once credentials or tokens are compromised, attackers can gain direct access to corporate systems, enabling follow‑on attacks that are more targeted and difficult to detect. As Joyner highlights, stolen data can be reused to impersonate trusted users and launch subsequent attacks that appear even more legitimate.

The growing prominence of AI brands adds a new dimension to the threat landscape. Just as banks and technology giants were historically impersonated due to their trust value, AI platforms are now becoming high‑value targets for brand abuse.

The reason is straightforward: AI tools are increasingly embedded in everyday workflows. Users expect to receive updates, notifications, and access links from these services. This expectation lowers suspicion and increases the likelihood of engagement with fraudulent messages.

Furthermore, AI adoption is often driven by competitive pressure within organisations. Teams are encouraged to experiment, adopt new tools quickly, and keep pace with innovation. This creates a fertile environment for attackers, who can exploit both urgency and enthusiasm.

Mitigation: controlling the narrative

From a defensive perspective, the challenge is not purely technical. Traditional awareness training may be insufficient because these attacks do not always resemble classic phishing scenarios.

Kumar suggests that organisations should focus on removing the underlying conditions that make such attacks effective. Rather than attempting to train users to recognise every possible variation, companies should provide clear, authoritative guidance on where and how employees should access AI tools. By eliminating ambiguity, the attractiveness of unofficial sources is reduced.

Joyner similarly emphasises the importance of reinforcing basic security practices. Users should avoid responding directly to unsolicited messages and instead verify account status by logging into official services. While simple, such measures remain highly effective against phishing.

At a broader level, Microsoft recommends strengthening identity‑centric controls such as multi‑factor authentication, conditional access policies, and monitoring for unusual token usage. These controls recognise that attackers are increasingly targeting authentication flows rather than exploiting software vulnerabilities.

AI‑themed phishing is unlikely to be a short‑term trend. Microsoft’s analysis suggests that it represents a longer‑term shift in social‑engineering tactics, with attackers continuously adapting their lures to align with emerging technologies and user behaviours.

In many respects, this reflects a broader truth about cybersecurity: the most effective attacks are those that exploit human factors rather than technical weaknesses. AI does not change this principle—it amplifies it.

The challenge for organisations is to respond in kind, combining technical controls with behavioural strategies and clear governance. As AI continues to reshape business processes, it is also redefining the threat landscape.

The same innovation that promises efficiency and insight is, inevitably, being repurposed by adversaries. The result is a cybersecurity environment where the line between legitimate and malicious activity becomes increasingly blurred, requiring vigilance, clarity, and a recognition that in the age of AI, trust itself has become a primary attack surface.

Podcast: Marcura CEO Henrik Hyldahn on AI for Modern Vessel Operations

Published Jun 7, 2026 11:59 PM by The Maritime Executive

In this edition of The Maritime Executive's podcast series, Marcura Group CEO Henrik Hyldahn joined TME to talk about the advent of AI tools for shipping professionals. 

Hyldahn started his corporate career at Coca-Cola and Carlsberg in Denmark, but a meeting with a Norwegian shipowner convinced him to try out the maritime world instead. He became CEO of the digital marketplace platform ShipServ in 2020, then took over as CEO of Marcura Group after the company acquired ShipServ in 2023-4. That merger brought together Marcura's experience on the voyage side of shipping transactions for charterers, traders and operators, along with ShipServ's platform for managing vessel opex and procurement. 

Under Hyldahn, Marcura is introducing AI tools to augment - not replace - the capabilities of human shipping professionals. AI tools can create documents in minutes and review charterparties for inconsistencies, catching mistakes that could cost thousands of dollars. Human specialists still have to validate the output and own the outcome, since mistakes in managing a voyage can result in cascading costs. AI also captures institutional knowledge of existing staff, preserving skills and lessons-learned for the next generation of employees.

"The first thing you want to use AI for is to eliminate as much of the work as possible — to get it completely automated and gone so that they focus on the decision side of things, on what are really the core value levers for them," says Hyldahn. For the details, listen in below. 

The opinions expressed herein are the author's and not necessarily those of The Maritime Executive.

How Fake News Became the Most Dangerous Force in Energy Markets

By Charles Kennedy - Jun 10, 2026

We live in a dangerously synthetic world. Scalper-style traders reportedly generated significant profits on suspiciously timed oil trades surrounding Iran war developments in Q2.

AI-generated content is creating new verification challenges. Fake news about an attack on Saudi oil facilities, for instance, could contribute to crude price volatility.

It’s a dangerous, unsustainable, AI-generated environment that rewards whoever gets information first, whether it’s real or not.

Now, it’s time to level the playing field, turning AI verification itself into an important value chain.

Hydaway Digital, (TSXV:HIDE, OTC:HIDDF) is setting out to capture market share in a digital trust industry that’s worth $535 billion already and on track to hit an unbelievable $3.375 trillion by 2032.

Recently acquired by Hydaway, RealityChek could be the new tool to restore institutional trust to financial markets through real-time verification of major events and developments that move markets aggressively.

Hydaway’s thesis is simple: Financial markets can’t operate indefinitely on information flows that institutions no longer fully trust.

The Digital ‘Truth Layer’

Hydaway’s answer to our digital trust breakdown is DETECT, a verification tool built on top of the company’s RealityChek platform.

Since the rise of generative AI in 2022, more than 15 billion AI-generated images have already entered circulation. More than 34 million AI images are generated every day. Studies now show people are fooled by AI-generated images roughly 40% of the time.

And in 2026, as missiles were flying across the Middle East, clips claiming to show strikes hitting Tel Aviv racked up millions of views in hours before someone realized that the “impact footage” was actually fireworks from a football celebration in Algiers.

And as tensions escalated, a completely AI-generated video of the Burj Khalifa engulfed in flames spread across platforms, drawing tens of millions of views.

That becomes far more dangerous once misinformation starts colliding with financial markets, geopolitical conflict, and commodity pricing.

Iran’s parliament speaker recently accused the U.S. of deliberately manipulating oil prices through false reports tied to negotiations and conflict developments. At the same time, social media platforms have been flooded with fabricated footage from the Iran war, including fake missile strikes, manipulated combat footage, and AI-generated imagery spreading globally before verification could catch up.

DETECT is the new digital police. It allows users to upload images and URLs and receive real-time authenticity analysis powered by Hydaway’s GPU infrastructure and RealityChek’s detection models.

Underneath, the system analyzes multiple forensic layers simultaneously: noise signatures, frequency patterns, compression behavior, metadata inconsistencies, and pixel-level artifacts that can reveal whether content has been manipulated or entirely generated by AI.

The company is also training DETECT through advanced neural networks that continuously evolve as AI-generated content becomes more sophisticated.

“This rapid growth of AI-generated content has continued to lead to widespread misinformation being shared globally online. Never before has misinformation become more mainstream than with the rise of AI-generated content,” said Hydaway CEO Karl Kottmeier. “DETECT is built to counter just that, combining AI with forensic tools to produce a reliable verdict you can trust.”

The Opportunity

RealityChek’s DETECT is built as an enterprise-grade SaaS model designed for recurring revenue and scalability across financial institutions, governments, insurance, enterprise systems, communications, onboarding, and transaction verification.

The transition from RealityChek into the publicly listed Hydaway Digital Corp. (TSXV:HIDE, OTC:HIDDF) represents a strategic step in the company’s development. Through this integration, a specialized technology has evolved into a fully integrated cybersecurity company with access to capital markets, scaling infrastructure, and clearly defined growth objectives.

This isn’t just a deepfake detector. Hydaway isn’t dependent on a single market segment or a single AI application.

RealityChek is being built to scale across multiple industries at the same time as AI-generated fraud, impersonation, and misinformation spread through financial systems, enterprise networks, governments, and media platforms.

The platform aims to monetize in multiple ways, including SaaS subscriptions, API licensing, usage-based fee models, data licensing, and custom enterprise datasets. The goal is not one-time software sales. The goal is institutional and recurring.

And the commercial footprint may have applications across multiple sectors.

Combining AI-driven forensic analysis with blockchain-anchored verification, Hydaway is targeting financial services, government systems, media, and enterprise compliance–all of which are expected to be looking to reduce legal, regulatory, and fraud exposure in the coming months and years.

In the markets of cybersecurity and digital trust, deepfake detection and digital verification are emerging as some of the fastest-growing segments.

Monetizing a Return to Trust

The global digital identity market is set to reach $170 billion by 2031, while the separate identity verification market is expected to be near $65 billion by 2035, and fraud detection and prevention is eyeing $252 billion by 2030-2032. They’re all part of the “digital trust industry”—an increasingly important industry in to business.

Hydaway’s RealityChek already holds more than 5 million data points and over 2 million images, with access to datasets that extend into the billions. And its verification process establishes integrity immediately–before it gets acted on.

Digital trust has become a pressing issue almost overnight, and the money required to catch up with AI is enormous, as is the task itself.

Financial institutions are already pouring massive amounts of capital into digital trust, fraud prevention, and verification infrastructure.

Banks and financial institutions are expected to spend some $40 billion on fraud detection and prevention systems by 2030. In 2025 alone, they spent an estimated $21 billion.

Deloitte’s 2026 banking outlook says financial crime is “escalating in scale, speed, and sophistication,” driving higher compliance costs and operational strain on banks. It also says banks submitted a record 2.6 million suspicious activity reports in fiscal 2024, or roughly 7,100 per day, while warning that malicious AI agents can generate “fraudulent, human-like behavior,” evade detection, and anonymize identity.

“The industry cannot rely on siloed data and legacy systems to deliver meaningful outcomes against external attacks, geopolitical events, and regulatory scrutiny,” Deloitte said in its 2026 report.

That’s what Hydaway is banking on, and it’s not just about financial institutions.

Governments are spending big, as well. They’re deploying billions into cybersecurity, identity authentication, zero trust systems, and digital resilience infrastructure as manipulated information, synthetic identities, and AI-driven fraud increasingly become national security concerns.

This broader push toward digital trust is also benefiting some of the largest names in cybersecurity and data intelligence. Palantir Technologies (NASDAQ: PLTR) continues to expand its role in government and enterprise data analytics, helping organizations process vast amounts of information and identify actionable insights across increasingly complex digital environments. Meanwhile, CrowdStrike Holdings (NASDAQ: CRWD) and Palo Alto Networks (NASDAQ: PANW) remain at the forefront of cybersecurity, providing AI-powered threat detection, identity protection, cloud security, and threat intelligence solutions as institutions race to defend themselves against increasingly sophisticated cyber threats and AI-generated fraud.

With either $100 oil or $200 oil, across the board, there is a growing institutional fear that traditional verification systems are no longer sufficient. Once manipulated media, synthetic identities, fake documentation, and AI-generated misinformation begin moving at machine speed across financial and other systems, traditional verification processes may face increased strain.

It’s a fast-paced game of catch-up with AI, and Hydaway is seeking to address these challenges.

By. Charles Kennedy

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OpenAI makes move to go public one week after rival Anthropic

AFP
June 8, 2026

Founded in San Francisco in 2015 as a nonprofit research lab by Altman, Elon Musk and others, OpenAI burst into the mainstream with the launch of ChatGPT in 2022 – Copyright AFP MARCO BERTORELLO

ChatGPT-maker OpenAI on Monday took the first step toward going public, one week after arch-rival Anthropic announced its own filing, as both companies look to raise the massive sums needed to expand.

In a social media post, the Sam Altman-led company said it had confidentially submitted an S-1 registration statement to US securities regulators but had “not decided on timing yet” for any potential debut.

OpenAI’s move follows a confidential filing by Anthropic, the maker of the Claude chatbot, which announced last Monday that it had taken the same step.

“It may be a while because there are things we want to do that are likely easier as a private company,” OpenAI said, while noting the filing gives it “the option to go public sooner if that ends up being best.”

Going public — known as an IPO, or initial public offering — means ordinary investors can buy a slice of a company for the first time. It is also one of the biggest ways a company can raise money fast.

Founded in San Francisco in 2015 as a nonprofit research lab by Altman, Elon Musk and others, OpenAI burst into the mainstream with the launch of ChatGPT in November 2022.

The company has since restructured as a for-profit corporation and has grown into one of the world’s most valuable private companies.

Training and running cutting-edge AI models require billions of dollars in computing infrastructure, and both OpenAI and Anthropic have been spending heavily to secure data center capacity and chips amid fierce competition to lead the industry.

Both companies have also raised record amounts of private investment to meet their ambitions, a process that may have reached its limits, forcing a turn to Wall Street for still more funding.

But going public requires making a company’s internal finances public, a process that both loss-making companies will want to delay for as long as possible.

OpenAI acknowledged the decision involved “a complicated set of tradeoffs” but said the filing preserves its flexibility.

Anthropic, valued at $965 billion following a $65 billion fundraising round, has positioned itself as a safety-focused rival to OpenAI in the generative AI race.

OpenAI was valued at $852 billion in March, putting it behind Anthropic by that measure heading into what analysts say could be a landmark period for AI listings.

Both companies appear set to follow Musk’s SpaceX to Wall Street.

SpaceX, which absorbed Musk’s xAI lab, could see shares begin trading as early as Friday, targeting a valuation of roughly $1.75 trillion in what would be the largest IPO in history.

OpenAI said it decided to get ahead of the news, predicting the document would surface regardless. “We expect it to leak so we’re just announcing it,” the company said.

EU orders Meta to open WhatsApp to rival AI chatbots for free

AFP
June 9, 2026

The EU kickstarted a investigation into Meta in December into whether Meta’s policy blocking access for AI providers, other than Meta AI, to WhatsApp breached antitrust rules – Copyright AFP/File Kirill KUDRYAVTSEV


The EU ordered Meta on Tuesday to give rival AI chatbots access to its WhatsApp platform for free within five working days as it carries out an antitrust probe, or risk a heavy fine.

The measure follows the launch in December of an EU investigation into the US firm’s policy of blocking access for AI providers other than Meta AI.

The European Commission, the EU’s digital watchdog, said Meta will have to maintain access to competitors until Brussels wraps up its probe.

“Today, we require Meta to restore access to WhatsApp for competing AI assistants while we investigate whether the restrictions may infringe EU competition rules,” EU antitrust commissioner Teresa Ribera said in a statement.

“This will prevent serious and irreparable harm to competition in this growing market by Meta’s conduct, which at first sight infringes EU competition rules,” the commission said in a statement.

The EU had warned Meta it faced interim measures if it did not open WhatsApp to rival AI assistants in February. The company then introduced an access fee — a remedy the EU rejected in April as unsatisfactory.

Traditional antitrust probes can take years and European officials believe the decisions, often fines, come too late to see any positive change to address the harm already done.

The EU’s goal is that Meta reinstates third-party AI assistants’ access to WhatsApp under the same conditions as before its October 2025 policy change when it “effectively” barred them.

The commission said it has the power to impose a fine of up to 10 percent of the company’s total turnover in the business year preceding the infringement if Meta “either intentionally or negligently” contravenes the decision on interim measures.



– Protecting a ‘growing market’ –



Brussels said the fee offered earlier this year “at first sight” was “in practice equivalent to the previous access ban”.

The commission described an “urgent need” to protect a “growing market for general-purpose AI assistants” and give space for smaller players and new entrants to challenge large incumbents.

There is no legal deadline for the EU’s investigation to end.

The commission has had several run-ins with Meta as part of a broader clampdown on abusive Big Tech practices.

In April, EU regulators found Meta was failing to keep under-13s off its Facebook and Instagram platforms in breach of the bloc’s digital content rules.

As part of that same probe, EU regulators are looking into how Meta protects users’ physical and mental wellbeing, as well as the “addictive” design of Facebook and Instagram.

Meta has also appealed a 200 million euro ($231 million) fine imposed last year by the EU under the online competition law, the Digital Markets Act (DMA).

The DMA is not popular across the Atlantic, with neither the US administration under President Donald Trump nor the American giants themselves.

Apple criticised the law on Monday when it blamed the DMA for its delayed rollout of the AI-enhanced voice assistant Siri, which the EU flatly rejected.

Nvidia unveils AI infrastructure deals in South Korea

AFP
June 8, 2026 

 

The deals were unveiled after Nvidia CEO Jensen Huang (R) spent the weekend in South Korea – Copyright POOL/AFP –


U.S. chip titan Nvidia on Monday announced a large-scale data centre construction project in South Korea with SK Telecom, among a raft of other business deals in the country.

Nvidia, the world’s most valuable company, also said it would work with chipmaker SK hynix to develop the advanced memory components that help run AI systems and are currently in short supply.

The tie-ups were unveiled after CEO Jensen Huang spent the weekend eating barbecue in Seoul with the country’s tech leaders and appearing on a popular TV show.

SK Telecom and Nvidia plan “to build a gigawatt-scale AI Cloud in Korea… with the first AI factory planned to come online in 2027”, a joint statement said.

The project “will support sovereign, physical and agentic AI services for enterprises and industries across Korea, with the vision to expand to greater Asia regions”, it added.

No figure was given for how much the two companies will invest in the data centres.

SK Telecom operates under the same parent company — SK Group — as SK hynix, which on Monday announced a “multi-year technology partnership” for memory chips with Nvidia.

“The agreement supports supply for advanced memory, addressing the extended development cycles, advanced fabrication and capital investments to sustain the global buildout of AI factories,” their statement said.

“Through this partnership, SK hynix will diversify into new markets Nvidia is creating — spanning AI infrastructure, personal AI and physical AI,” through co-developing memory components for Nvidia hardware, it said.

As governments and companies pour hundreds of billions of dollars into AI infrastructure, Nvidia’s value has topped $5 trillion, more than the gross domestic product of Japan or India.

The race to build AI data centres has created a global shortage of memory chips — sending profits skyrocking for manufacturers like SK hynix and rival Samsung Electronics, whose workers’ union recently agreed a deal with management on bonuses, averting a strike.

SK Group chair Chey Tae-won last week vowed to double production capacity of silicon wafers used to make memory chips.

But he also reiterated his prediction that shortages could persist until 2030, with chip factories taking at least three years to build.

Nvidia’s Huang signed a memory chip display at the SK hynix booth at the Computex trade show in Taipei, writing: “Please make more”.

When he landed in South Korea on Friday, Huang said he had “brought a lot of business to Korea”, promising some new “surprises”.

On Monday the California-based company also announced AI-related collaborations with tech giant Naver, and with Doosan Group on robotics.

Nvidia is best known for its GPUs, specialised computer chips originally designed to render video game graphics at high speed.

These chips have become the engine behind AI tools from chatbots to image generators and agents that can carry out tasks for users.

Nvidia last week unveiled a powerful laptop chip for Windows machines, staking its claim in the market for next-generation consumer PCs integrated with AI.

UK government warns big tech over nude images sent by children

AFP
June 8, 2026  

British PM Keir Starmer tackled the issue of children sending and receiving sexually explicit content, speaking at the London Tech Week conference – Copyright POOL/AFP Isabel Infantes


Tech giants must stop children in Britain from being able to send and receive nude images on their devices, or be forced to do so by law, the government said Monday.

Britain’s interior ministry said it was giving companies including Apple and Google three months to introduce safety features to block children from taking and accessing naked photos on phones and tablets.

If they do not, the government will introduce legislation to “force them to activate the technology”, the Home Office said in a statement.

“This is not an impossible challenge,” Prime Minister Keir Starmer told delegates at the London Tech Week conference.

“These are some of the most innovative companies in the world and I believe they can solve it, but if they choose not to, then we will act and we will change the law, because when it comes to the safety of our children, standing by is not an option,” he added.

The Labour government said technology companies had a “moral responsibility” to “protect children from coercion, abuse and sextortion”.

It said any future legislation would include fines for companies that fail to comply and possibly even criminal liability for tech bosses.

A law change would stop children from being able to access pornography, while also making it more difficult for child abusers to target children, it said.

The government cited analysis by the Internet Watch Foundation charity that found 91 percent of online child sexual abuse reports recorded in 2024 contained self-generated content from children themselves.

The interior ministry noted that Apple recently rolled out age verification requirements for UK users, making it the first company to activate safety features by default for under-18s.

But nudity detection is not applied to the camera, third-party messaging apps such as Snapchat or search functions, meaning children can still take, view, share and save such pictures, it said.

Starmer is expected in the coming days to announce a ban on children under the age of 16 accessing some social media platforms, UK media has reported.

A government-led consultation where British teenagers trialled social media bans and time limits on apps recently ended.

Australia in December became the first nation to ban people under 16 from social media.

Threats to US lawmakers spiked after Meta eased moderation: watchdog

AFP
June 10, 2026

Image: — © AFP SEBASTIEN BOZON

From explicit calls for murder to sexual harassment, violent threats targeting US lawmakers on Facebook rocketed after tech giant Meta rolled back key content moderation policies last year, a tech watchdog said Tuesday.

The report from the nonprofit Center for Countering Digital Hate (CCDH) analyzed nearly eight million Facebook comments targeting 100 members of Congress in the six months before and after Meta eased safeguards in what was billed as an attempt to protect free speech.

Violent threats targeting lawmakers on both sides of the political aisle — including calls for murder — quadrupled, harassment more than doubled, while racist and gendered abuse jumped on the platform, the report said.

CCDH also found that comments inciting violence against President Donald Trump surged after the policy changes, including one that he “deserves a bullet through his head.”

“When platforms stop enforcing their own rules against threats, hate, and harassment, they become complicit in normalizing intimidation and harassment of elected officials,” said Imran Ahmed, chief executive of CCDH.

“The result is a culture where violence feels easier to justify and radicals feel empowered.”

In a statement, a Meta spokesman said the Palo Alto company regularly issues public reports tracking “violating content” on its platforms and “the prevalence of hateful conduct did not increase throughout 2025.”

AFP shared CCDH’s report with Meta but the spokesman said: “We cannot address the claims in this report as we were not provided it in advance of publication.”

In recent years, politicians as well as election officials across the United States have reported escalating threats, intimidation and harassment.

Minnesota state legislator Melissa Hortman and her husband were shot and killed in a politically motivated attack last year. In April, a shooting disrupted the White House correspondents dinner attended by Trump, who had to be evacuated from the dinner at the Washington Hilton hotel — one of several such incidents.

“When companies reduce oversight in areas like violence, hate, and harassment, it should not be any surprise to see those harms increase,” John Curtis, a Republican senator from Utah, said in a statement to CCDH.

“Similarly, the reported surge in abusive and threatening content directed at public officials is deeply concerning, particularly in light of recent events.”

The CCDH report comes after the tech giant ditched US fact-checkers in January 2025 and turned over the task of debunking falsehoods to ordinary users under a model known as “Community Notes,” popularized by Elon Musk’s platform X.

The decision was widely seen as an attempt to appease Trump’s new administration, whose conservative support base has long complained that fact-checking on tech platforms was a way to curtail free speech and censor right-wing content.

Meta also rolled back speech restrictions around topics such as gender and sexual identity, triggering concern from advocacy groups.

The International Fact-Checking Network has previously warned of devastating consequences if Meta broadens its policy shift related to fact-checkers beyond US borders to the company’s programs covering more than 100 countries.

AFP currently works in 26 languages with Meta’s fact-checking program, including in Asia, Latin America, and the European Union.

Nintendo agrees to €35 million French fine over faulty Switch controllers

AFP
June 8, 2026

The Nintendo Switch was launched in 2017 – Copyright AFP/File Kazuhiro NOGI

Nintendo said Monday that it would pay a fine of 35 million euros ($40 million) to settle a French claim over faulty controllers on its Switch consoles.

A French consumer advocacy group filed the claim alleging “planned obsolescence” in 2020, saying the Japanese giant knew some controllers were failing too quickly.

It noted thousands of complaints by customers about “Joy-Con drift”, with toggles stuck in one direction on the original version of the Switch, even when users were not touching them.

France’s consumer protection agency, DGCCRF, determined that Nintendo Europe had not sufficiently informed consumers of the recurring problem despite the console being sold for years since its 2017 launch.

Its report said Nintendo had “commented only in 2020, and not as soon as it was aware of the malfunctions”, the Commerce Ministry and the court in Nanterre, outside Paris, said in a statement.

It added that Nintendo’s statements on the problem were “patchy”, and as a result many customers simply bought new controllers, instead of pursuing a free fix or replacement via customer service.

Nintendo promised in 2023 that it would repair or replace faulty controllers without cost, even if no longer covered by warranty.