Artificial intelligence is rapidly becoming part of everyday business operations, with companies embedding AI into workflows, products, and decision-making. Large language models are central to this shift, enabling automation and insight at scale.

In Southeast Asia, adoption is accelerating, but many startups and SMEs still face barriers around cost, access, and implementation. This creates demand for solutions that make AI more practical and scalable across diverse markets.

Qwen, a family of large language models developed by Alibaba Cloud, is positioned to support this transition. By combining advanced AI capabilities with cloud infrastructure, it enables organisations to build and deploy AI applications more efficiently.

From experimentation to real-world AI adoption

The company’s focus on driving AI and cloud adoption reflects a broader shift from experimentation to real-world use. For startups, this means faster product development. For enterprises, it enables integration into existing systems without unnecessary complexity.

Meeting Qwen at Echelon Singapore 2026 offers founders, technical leaders, and operators a chance to explore how AI can be applied in practical ways. As a Bronze Sponsor and AI Workflow Competition Partner, Qwen is engaging directly with the ecosystem to support conversations around implementation and scale.

Also Read: Building real traction: Echelon Singapore 2026 introduces demo stage

Enabling AI workflows at scale

Qwen’s integration within Alibaba Cloud’s ecosystem provides access to both powerful models and the infrastructure required to deploy them. This reduces the need for organisations to build AI systems from scratch, allowing them to focus on application and outcomes.

Its approach centres on enabling real workflows, from automating internal processes to improving customer interactions. This makes AI more usable for businesses that need clear, operational impact.

Supporting ecosystem innovation

Through its role as an AI Workflow Competition Partner, Qwen is contributing to initiatives that encourage practical AI development. These programmes help startups and developers translate ideas into real solutions that address business needs.

At the same time, its platform supports experimentation and scaling, making it accessible to startups, SMEs, and larger organisations looking to expand their AI capabilities.

Meet Qwen at Echelon Singapore 2026

Qwen will be present at Echelon Singapore 2026, engaging with attendees across the exhibition floor. Visitors can connect with the team to explore use cases, understand capabilities, and discuss how AI can be integrated into their operations.

As Southeast Asia’s digital economy continues to grow, the ability to implement AI effectively will shape how companies scale and compete. Platforms like Qwen are helping make that transition more accessible across the region.

The region is evolving quickly, and Echelon 2026 offers the right place at the right moment to be part of what comes next. Register here to join the conversation.

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Singapore Management University (SMU) has launched a US$10 million co‑investment fund to accelerate early‑stage deeptech startups focused on urban sustainability across Asia.

The Urban SustaInnovator (USI) Fund will invest in ventures emerging from SMU’s USI accelerator and the Lee Kuan Yew Global Business Plan Competition (LKYGBPC) pipeline, aiming to speed the route from validation to real‑world deployment in Asian cities.

The fund is anchored to SMU’s Urban SustaInnovator accelerator, a 12‑month, hybrid, zero‑fee and equity‑free programme launched at the 12th edition of LKYGBPC in September 2025. The vehicle is being positioned as one of Southeast Asia’s first university‑anchored co‑investment vehicles dedicated to urban solutions and sustainability.

Also Read: Singapore university unveils Urban SustaInnovator accelerator for global deeptech startups

SMU says the fund will co‑invest with established venture capital partners on prevailing market terms, providing capital that aligns with the long deployment cycles common in physical and infrastructure‑adjacent technologies.

For catalytic capital and market traction

Professor Sun Sun Lim, SMU’s Vice‑President (Partnerships & Engagement) and chair of the USI Programme Management Committee, framed the fund as a pragmatic response to a persistent financing gap. “Urban sustainability innovation often fails not for lack of ideas, but for lack of capital that understands early‑stage risk and long deployment cycles,” she said. The USI Fund, she added, will “co‑invest with leading venture capital partners and back deep‑tech startups from SMU’s robust global pipeline, providing targeted early‑stage capital to help founders scale proven solutions into Asian urban markets.”

SMU has positioned the fund as catalytic: it will not typically lead rounds but intends to partner with lead investors to provide follow‑on and anchor capital that can de‑risk pilots and first commercial deployments. The university expects the first investments to come from the inaugural accelerator cohort, with deployments slated to start by the fourth quarter of 2026.

The fund’s stated remit covers a broad set of urban sustainability themes — decarbonisation, energy transition, the built environment, mobility and circularity — which reflect both technological opportunity and city policy priorities across Asia. SMU also highlights the fund’s integration with its “Singapore Inc” Advisory Board, a collective of VCs, corporates, scientists and regulators designed to give startups sectoral guidance and market access.

A built‑in deal flow from LKYGBPC

SMU is leveraging the LKYGBPC competition as a deal funnel. The competition drew more than 1,500 applications from over 90 countries, from which seven startups were selected for the USI accelerator’s first cohort. That cohort has already demonstrated commercial movement, according to SMU, suggesting the fund will have privileged access to companies with traction and sector fit.

Several cohort members have recorded visible wins. Malaysia’s Qarbotech, which develops photosynthesis‑enhancing nanomaterials for agriculture, won the Grand Prix at SusHi Tech 2026 and secured a pilot with Tokyu Fudosan Group. UK‑based Mimicrete, a developer of self‑healing concrete, has started a pilot in Singapore with The GEAR by Kajima. Both startups are reportedly in the process of establishing or expanding operations in Singapore, reinforcing the city’s appeal as a regional staging post for deep tech commercialisation.

Also Read: Urban solutions, sustainability take centre stage at SMU’s LKYGBPC startup challenge in 2025

The cohort also includes diverse technologies: US‑based MacroCycle is chemical upcycling PET plastics and has drawn institutional capital from Volta Circle; Singapore’s Inviscid AI claims eightfold revenue growth after joining USI by applying physics‑informed neural networks to thermodynamic simulation; Sesame Sustainability, an MIT alumni‑founded industrial decarbonisation software firm, has secured paid pilots with ABB; Smart Tire Company is piloting airless tyres developed from shape‑memory alloys with links to NASA programmes; and French Pronoe is pursuing modular ocean alkalinity carbon removal arrangements with Frontier, the carbon‑removal market platform backed by Meta and Google.

Taken together, the cohort illustrates the diversity and ambition of startups that the fund intends to support, from materials science and hardware to software and carbon removal.

Filling a financing gap for long‑cycle, capital‑intensive ventures

Investors and founders have long argued that deeptech ventures addressing urban systems face a distinct funding problem: they require patient capital and credible pilot pathways with corporates, utilities and municipalities, and often take longer to demonstrate commercial returns than software plays.

University‑linked funds, particularly those integrated with accelerators and research groups, attempt to bridge some of that gap by combining early capital with access to testbeds, expertise and talent.

SMU’s model emphasises a “teaching accelerator” approach: students are embedded in evaluation, due diligence and portfolio support activities so that learning and capital deployment are intertwined. Prof Lim said selected students will participate in startup evaluation, market analysis and diligence, gaining “venture literacy, climate and sustainability insight, commercialisation know‑how and applied decision‑making skills beyond the classroom.”

The pedagogical angle may offer dual benefits: students obtain practical training while the fund taps university resources for additional screening and advisory capacity. Critics, however, caution that university involvement must not substitute for professional investment management; co‑investment partnerships with experienced lead investors will therefore be critical to provide market discipline and exit pathways.

Regional ambitions and Singapore’s role

SMU is marketing Singapore as the launchpad for the USI Fund’s Asia‑wide ambitions. The city‑state’s strengths, regulatory stability, deep corporate networks, and a concentration of engineering and project partners, make it a logical base for pilots and Asia roll‑outs. The fact that several cohort companies are already establishing local footprints supports this narrative.

However, scaling urban solutions across Asia will require navigation of varied regulatory environments, differing infrastructure standards and fragmented procurement processes. Co‑investments that pair local corporate or institutional partners will be necessary to convert pilots into city‑wide deployments.

What to watch next

The USI Fund’s impact will depend on the speed and scale of its first investments, the quality of its co‑investment partners, and its ability to shepherd pilots into sustained commercial contracts. Observers will also watch whether the fund follows through on support beyond capital — for example, by facilitating regulatory approvals, municipal pilots, or industrial partnerships.

Also Read: 60 global startups to compete for US$2M prize at LKYGBPC grand finals

For the city’s startup ecosystem, the fund represents an experiment in combining academic resources, student learning and catalytic capital. If SMU can demonstrate that university‑anchored funding materially improves the odds of scaling urban deep tech in Asia, the model may be copied elsewhere. If not, it risks becoming another software‑oriented VC wannabe without the patient capital and bespoke operational support these ventures need.

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Southeast Asia’s technology ecosystem is entering a new phase of growth, with startups and enterprises increasingly building AI-driven products, cloud-native platforms, and compute-intensive applications that require scalable infrastructure. As demand for artificial intelligence, machine learning, and high-performance computing continues to rise, access to powerful and efficient computing platforms is becoming a key factor in how companies innovate and compete across the region.

For many organisations, the challenge is no longer whether to adopt AI, but how to scale it effectively while managing performance, flexibility, and operational costs. This has created growing demand for technology providers that can support workloads across cloud environments, data centres, edge computing, and personal devices while enabling businesses to adapt quickly to evolving market needs.

AMD addresses these needs through a broad portfolio of AI-optimised CPUs, GPUs, networking technologies, and software designed to support next-generation computing experiences. From cloud and AI infrastructure to embedded systems and gaming, AMD technologies power billions of experiences globally while helping organisations build scalable solutions for an increasingly intelligent digital economy.

Advancing AI infrastructure

AMD’s mission is centred around building technologies that accelerate innovation across AI, cloud, edge computing, and high-performance workloads. Guided by its “together we advance” principle, the company works closely with partners, developers, and ecosystem players to make transformative computing technologies more accessible across industries.

This approach is particularly relevant in Southeast Asia, where startups and enterprises are increasingly exploring AI applications in sectors such as fintech, healthtech, SaaS, and smart cities. As companies scale compute-intensive workloads, the ability to access flexible and high-performance infrastructure becomes increasingly important for supporting growth and experimentation.

AMD’s technologies support a wide range of use cases across cloud computing, AI infrastructure, enterprise workloads, and edge deployments. Its focus on full-stack AI solutions allows organisations to manage demanding workloads while maintaining scalability across different environments and applications.

Also read: From idea to impact: Startups redefining what’s possible in Southeast Asia

Ecosystem collaboration

At Echelon Singapore 2026, AMD is looking to engage directly with startups, ecosystem builders, investors, and enterprise leaders across Southeast Asia. The company is particularly interested in collaborations involving AI, cloud computing, high-performance computing, and data-intensive applications.

AMD is also focused on supporting startups through access to its technology ecosystem, including computing platforms for commercial clients, servers, and cloud environments. Its participation reflects a broader effort to strengthen partnerships across accelerators, venture networks, cloud providers, and innovation ecosystems throughout the region.

For founders and operators building AI-native products, conversations around scalable infrastructure, compute performance, and ecosystem partnerships are becoming increasingly important as regional markets mature. Events such as Echelon Singapore create opportunities for startups and technology providers to exchange ideas, explore collaboration opportunities, and better understand the infrastructure shaping the future of AI innovation in Southeast Asia.

Also read: 10 ecosystem players shaping how startups scale at Echelon Singapore 2026

Meeting AMD at Echelon Singapore 2026

AMD joins Echelon Singapore 2026 alongside founders, investors, corporates, and ecosystem leaders gathering at Suntec Singapore CEC on 3–4 June 2026. The event brings together Southeast Asia’s startup and technology community through content stages, exhibitions, networking opportunities, and knowledge-sharing sessions designed to support regional innovation and growth.

Attendees visiting AMD can learn more about how the company’s technologies support AI workloads, cloud computing, and high-performance applications across industries. AMD will also offer invited startup workshops focused on AI performance and scaling, alongside cloud credit sponsorship opportunities for participants in the workflow programme.

As Southeast Asia’s digital economy continues to evolve, technologies that enable scalable AI and high-performance computing will likely play a growing role in how startups and enterprises expand regionally and globally. Echelon Singapore 2026 provides a space for ecosystem players to explore these developments while building the partnerships that could shape the region’s next stage of growth.

The region is evolving quickly, and Echelon 2026 offers the right place at the right moment to be part of what comes next. Register here to join the conversation.

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As Southeast Asia’s startup ecosystem continues to mature, cross-border collaboration is becoming increasingly crucial for founders, investors, and innovation agencies seeking to scale beyond their domestic markets. Startups today are expanding internationally far earlier than previous generations, driven by growing investor appetite, regional digital adoption, and the need to build partnerships across multiple innovation hubs.

At the same time, governments and public accelerators are playing a larger role in helping startups navigate international expansion. Beyond funding, many ecosystem organisations are now focused on creating stronger pathways for market entry, strategic partnerships, and venture connections that allow startups to compete globally while remaining rooted in their local innovation ecosystems.

This growing emphasis on regional collaboration has created stronger ties between Northeast Asia and Southeast Asia, particularly in sectors such as AI, deep tech, advanced manufacturing, content, and digital transformation. For organisations supporting startup growth, Singapore increasingly serves as a gateway into ASEAN markets, connecting founders with investors, enterprises, and ecosystem builders across the region.

SBA (Seoul Business Agency), the official public accelerator of the Seoul Metropolitan Government, sits at the centre of this movement by supporting startups and SMEs across industries ranging from AI and deep tech to gaming, beauty, fashion, and consumer products. Through funding initiatives, R&D support, and ecosystem-building efforts, SBA works to accelerate the global growth of Seoul’s startup ecosystem while strengthening Seoul’s position as a global innovation hub. At the heart of SBA’s supportive infrastructure is the ‘Seoul Startup Hub’. As Korea’s premier and largest startup hub, it brings together spaces, programs, and networks under one roof, empowering companies at every stage from initial launch to global scale-up.

Supporting startup expansion

SBA plays a key role in Seoul’s broader startup development strategy by supporting both early-stage founders and scaling companies through funding, acceleration, and ecosystem programmes. Seoul Metropolitan City and SBA co-manage the Seoul Vision 2030 Fund, which is targeting approximately US$3.7 billion in committed capital by 2026 across sectors including digital transformation, bio, advanced manufacturing, and creative industries.

Alongside investment initiatives, SBA also provides approximately US$31.5 million in annual R&D grants that support high-growth startups and emerging technologies. This combination of capital access, public sector support, and ecosystem connectivity allows Seoul startups to build stronger foundations before expanding internationally.

These efforts are reflected in Seoul’s growing global standing. As of 2025, Seoul ranks 8th globally and 2nd in Asia in startup ecosystem rankings, with a total ecosystem value of approximately US$112 billion. According to a February 2026 report by Korea’s Ministry of SMEs and Startups, 20 of Korea’s 27 unicorn companies are based in Seoul, underscoring the city’s position as the country’s most concentrated hub of high-growth ventures.

The agency’s mission focuses on accelerating the global growth of Seoul startups by fuelling technological innovation, supporting high-potential ventures, and building stronger connections between founders and international markets. As regional startup ecosystems become increasingly interconnected, organisations such as SBA are helping bridge opportunities between Korean startups and Southeast Asian investors, enterprises, and technology partners.

Also read: From idea to impact: Startups redefining what’s possible in Southeast Asia

Cross-border collaboration

At Echelon Singapore 2026, SBA is bringing five promising Seoul startups to engage directly with Southeast Asia’s venture ecosystem. Its participation reflects a broader strategy centred around cross-border investment, commercial partnerships, and international market expansion.

The agency’s primary focus is to link startups with high-impact matchmaking opportunities involving venture capital firms, multinational corporations, and regional enterprises. Beyond investment, SBA is also looking to support proof-of-concept collaborations, open innovation initiatives, and commercial partnerships that can help startups establish a stronger foothold in ASEAN markets.

This approach aligns with broader regional trends, as startups increasingly seek partnerships that go beyond funding alone. For many growth-stage companies, entering Southeast Asia successfully requires local operational support, enterprise relationships, distribution partnerships, and market validation. Ecosystem organisations that can help facilitate these connections are becoming increasingly valuable as cross-border expansion accelerates.

Also read: Startups driving AI automation, fintech, and accessibility gather at Echelon Singapore 2026

Meeting Seoul Startups at Echelon Singapore 2026

SBA joins Echelon Singapore 2026 alongside founders, investors, corporates, and ecosystem leaders gathering at Suntec Singapore CEC on 3–4 June 2026. The event provides a platform for startups, accelerators, public agencies, and technology companies to exchange ideas, build partnerships, and explore new regional growth opportunities.

As Southeast Asia and South Korea continue building stronger innovation ties, organisations such as SBA are helping create pathways for startups to scale internationally while contributing to a more connected regional startup ecosystem. By visiting the SBA Seoul pavilion, attendees can connect directly with the innovative Seoul-based startups that SBA supports. Echelon Singapore 2026 offers an opportunity for founders, investors, and ecosystem leaders to explore how these cross-border collaborations could shape the next phase of growth across Asia’s technology landscape.

The region is evolving quickly, and Echelon 2026 offers the right place at the right moment to be part of what comes next. Register here to join the conversation.

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In our hyper-competitive world, the mantra is hustle and grind. We treat constant activity as proof of productivity, seeing intentional rest as a luxury, or worse, a sign of weakness. This belief is the single greatest bottleneck to sustained high-level performance and long-term business growth. It leads to decision fatigue, creative stagnation, and burnout.

The most resilient and creative leaders know a powerful secret: the renewal advantage. They understand that intentional rest is not a break from progress; it is the secret fuel that accelerates progress. Short, deliberate pauses like daily stillness, weekly unplugging, or seasonal reflection actually restore the clarity, energy, and cognitive capacity needed for strategic breakthroughs. This allows leaders and teams to remain inspired, creative, and resilient over the long haul.

The math of diminishing returns

The human brain is not a machine that offers linear output. After a prolonged period of intense focus (exploitation), the quality of work decreases rapidly, even if the quantity of hours remains high. Trying to force strategic thinking or creative problem-solving when energy is depleted is an exercise in futility, as you are trading valuable time for minimal return.

Also Read: Board diversity 2.0: The strategic advantage Asian boards are still underestimating

The Renewal Advantage flips this equation. Intentional rest is the recharge phase, during which the brain actively engages in crucial low-level processing: consolidating memory, integrating new information, and most importantly, making connections between previously disparate ideas. The biggest leaps in strategy and innovation almost never happen while staring at a spreadsheet; they happen when the mind is allowed to wander, often during a deliberate pause.

Executive testimonials: The pause that paid off

Uplifting accounts from high-performing executives consistently credit strategic rest for their biggest breakthroughs. They have learned that time away from the problem is time spent solving it in a non-linear way.

One CEO, struggling with a major acquisition strategy, mandated “deep work silence” every afternoon. Instead of answering emails, he spent 30 minutes walking without his phone. He credits a solution that saved the company millions to a moment of clarity that occurred during one of those silent walks, not during a high-pressure board meeting.

Another executive requires her team to take a “seasonal reflection day,” a paid day off every quarter, with the single mandate to spend time in nature and reflect on the past three months without any work communication. She found this simple ritual led to a dramatic reduction in team conflicts and a 20 per cent increase in unsolicited, novel product ideas the following week.

These leaders treat rest not as something to be earned after the work is done, but as an input necessary for the highest quality of work.

Also Read: The digital economy’s broken promise: How tech restructured inequality instead of erasing it

Accessible rituals for sustained clarity

The good news is that accessing the Renewal Advantage doesn’t require a tropical vacation; it requires accessible, intentional rituals.

• The 15-minute daily stillness: Block 15 minutes in the middle of your workday for absolutely nothing. No phone, no music, no specific task. Just sit, close your eyes, and allow the cognitive dust to settle. This restores focus better than any cup of coffee.
• The weekly unplug covenant: Negotiate a clear, non-negotiable window (perhaps Saturday afternoon to Sunday morning) when the entire leadership team agrees not to send or check work communications. This creates psychological safety and allows everyone to fully disconnect, knowing they aren’t missing a critical fire.
• The transition ritual: Design a simple, physical act to mark the end of your workday. It could be changing clothes, listening to one song, or reading a chapter of a book. This signals to your brain that the high-intensity strategic phase is over and the recovery phase has begun, preventing mental capital from leaking into your personal time.

Intentional rest is not a sign of weakness; it is the ultimate expression of strategic discipline. By deliberately managing your energy and allocating time for deep recovery, you are fuelling sustained creativity, resilience, and the clarity required for making truly expansive strategic decisions.

Are you treating rest as a luxury to be squeezed in, or as a strategic fuel source to be prioritised?

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A difficult workplace conversation used to be something people mulled over with a trusted friend, a mentor, or a therapist. Now, many are rehearsing it with AI first. That can be useful. The trouble starts when the tool moves from helping someone phrase a message to helping them decide who the other person is.

I am seeing this more often in my clinical work with clients navigating workplace stress, conflict, and burnout. People are bringing AI into the room before they bring the conversation to another human being. They use it to rehearse a difficult exchange with a colleague, make sense of tension with a manager, or test whether their response sounds reasonable.

Used that way, it can be genuinely helpful. But some are going further, pasting in accounts of workplace conflict and asking the tool to explain the other person’s behaviour. The AI can then return a confident-sounding interpretation: narcissistic, manipulative, toxic. By the time that person speaks to me, those words may already be shaping the story. In session, I am increasingly hearing AI-generated certainty before we have had the slower, more careful conversation that the situation deserves.

I recognise the pattern because I see a version of it in my own use of AI. When I use these tools to brainstorm social media ideas on neuroscience, mental health, and nervous system topics, I can see how easily the output slips beyond the evidence. Clinical language arrives fast, interpretive leaps follow close behind, and the whole thing is written in a calm, polished tone that can sound trustworthy on first read. My background makes that easier to catch. For someone looking for clarity, speed, or relief in the middle of a stressful moment, those leaps can be much harder to spot.

That is where this becomes a workplace issue, not just a technology one.

As AI tools become more embedded in everyday work, and more agent-like in how they guide tasks, decisions, and communication, their influence is spreading beyond productivity. In some workplaces, they are also starting to shape how people interpret conflict, read colleagues, and decide what to do next.

Also Read: The use of GenAI is turning innocent employees into insider threats: Here’s how to fix it

In a clinical setting, careful interpretation takes time. It depends on history, pattern, differential thinking, and the ability to sit with ambiguity before deciding what the behaviour means. In a workplace setting, good judgment also depends on context: power, pressure, communication style, culture, and what else may be happening around the interaction. AI does not pause to sit with ambiguity in the way a thoughtful human might. It tends to move quickly towards explanation. When the explanation sounds psychologically literate, people can give it more weight than it deserves.

Brown University researchers recently found that AI chatbots prompted to act like therapists routinely violated core mental health ethics standards, including failures in contextual adaptation and responses that reinforced false beliefs. The study focused on therapy-style use, but the concern is relevant to workplace conflict, too. When someone feeds an AI a one-sided account of a difficult boss or colleague, the system can still produce a confident interpretation that feels validating without being especially sound.

Part of the problem is that AI speaks very fluently in the language many people already know from social media. Terms like narcissist, gaslighting, trauma response, emotional abuse, and boundary violation now travel widely online, often with uneven precision. AI is very good at picking up that language and handing it back in a smooth, coherent form. Those terms can be useful in the right setting, but they lose precision quickly when they are pulled out of context and applied too loosely.

For workplaces, this raises a more uncomfortable question. When employees would rather take a difficult interaction to AI than to a manager, colleague, mentor, or trusted professional, the issue is rarely just convenience. AI is available at the exact moment the person feels tense, uncertain, or exposed, and it offers a version of perspective without the friction of another human response.
That kind of private rehearsal can change what happens next.

Also Read: AI adoption in Southeast Asia: Balancing automation gains with the rising threat of cyberattacks

A reply that may have been rushed or poorly worded can start to feel like evidence. A tense meeting can get pulled into a bigger story about culture, and a difficult personality can be wrapped in diagnosis-shaped language before anyone has had a careful look at the context. The tool may be trying to help, but the output can quietly narrow the way the person reads the situation.

I use AI myself in limited ways, and I understand the appeal. The value is real. The risk lies in the authority people begin to hand over to a system that sounds composed, informed, and certain while working from a very partial account.

For organisations, AI literacy now needs to include psychological literacy. People need to understand how easily polished language can be mistaken for careful judgment, especially when they are stressed, angry, embarrassed, or looking for relief. They also need better human places to take workplace tension before it becomes an AI-assisted verdict.

AI will keep moving deeper into working life. The real test is whether workplaces build enough human depth around it, so that difficult moments are understood with more context rather than processed with more speed.

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The post When AI becomes the office therapist appeared first on e27.

Most cybersecurity discussions over the past decade have focused on scale. More users, more devices, more data moving across systems.

AI agents introduce a different kind of problem.

They don’t just process requests. They interpret inputs, make decisions, and sometimes take action across systems. In enterprise settings, that can include internal tools, data, and workflows.

That’s where things get tricky. Risk is no longer just about infrastructure or access. It also comes down to how the system processes inputs and what it does with them.

When systems start acting independently

AI agents are built to reduce manual work. They can respond to customers, trigger workflows, and move across tools without much human input.

That’s exactly what makes them useful. But it also makes them harder to control.

Once a system can take action on its own, the question changes. It’s not just “is it secure?” but “can it be pushed into doing something it shouldn’t?”

Most security models assume things are fairly clear:

• Inputs are structured
• The intent is obvious
• Behavior is predictable

In reality, AI agents don’t always work like that. They deal with messy inputs, rely on context, and generate responses based on probability.

That makes them more flexible, but also less predictable.

Prompt injection is already showing up

Prompt injection is one of the more immediate risks.

Instead of breaking the system, it plays with how the system interprets instructions. An attacker can shape an input to change what the agent prioritises or how it responds. Sometimes that leads to data exposure. Sometimes it leads to actions that were never meant to happen.

Also Read: The agentic shift: Why AI agents are rewriting the rules of ERP software in Singapore and Malaysia

A few examples:

• A support agent surfacing internal information
• A workflow agent pulling data it shouldn’t have access to
• A coding assistant producing insecure outputs

What makes this harder is that the input often looks normal. There’s no obvious “attack pattern.” It’s just a request that gets misinterpreted.

This is not just a theoretical concern. Even companies building these systems acknowledge the limitation.

OpenAI recently noted that prompt injection is unlikely to be fully solved, comparing it to scams and social engineering on the web. In their work on AI browsers, they also pointed out that giving agents the ability to interact with the open web expands the attack surface in ways that are difficult to fully control.

That reflects a broader reality. The goal is not to eliminate these attacks entirely, but to reduce how often they succeed and limit the impact when they do.

Data leakage is often unintentional

AI agents get better with more context. That usually means access to internal documents, previous conversations, and connected systems.

That same access creates risk.

In many cases, data leakage doesn’t come from a breach. It comes from how the system is set up and how it responds in context.

Sensitive information can show up because:

• Access is too broad
• Too much context is being pulled in
• The system misreads what the user is asking

As discussed in my earlier article, trust is becoming central to how digital platforms operate. With AI systems, that trust depends heavily on how data is handled in everyday interactions.

Existing security models only go so far

Most traditional security approaches assume systems behave in predictable ways.

AI agents don’t.

They rely on context, probability, and ongoing interaction. That creates gaps in how we usually secure systems.

For example:

• Input validation is harder when everything is natural language
• Access control gets messy when context keeps changing
• Monitoring becomes less useful when behaviour isn’t consistent

Even logs don’t tell the full story. You can see what happened, but not always why.

Also Read: AI agents are entering investment banking, but is the industry ready?

Securing behaviour, not just systems

This is where the approach needs to shift.

It’s less about locking everything down and more about making sure the system behaves within clear boundaries.

In practice, that means:

• Being explicit about what agents are allowed to do
• Adding checks for higher-risk actions
• Limiting access to only what’s needed
• Watching patterns over time, not just single outputs

In real-time environments, this becomes even more important. Systems are making decisions in milliseconds, often with direct user interaction.

The goal is not to restrict what the system can do, but to make sure it behaves predictably under real-world conditions.

What this means going forward

AI agents are already being used across support, operations, and internal tools. That’s only going to increase.

Before scaling them further, teams need to be clear on a few basics:

• What can this agent access?
• What can it do without oversight?
• How does it behave when things are unclear?

These aren’t edge cases. This is how these systems operate day to day.

At that point, security isn’t just about preventing access. It’s about ensuring the system does what it’s supposed to, even when the inputs aren’t perfect.

As CISO, the questions I focus on are the same ones every team deploying agents should be asking: what can this agent access, what can it do without a human in the loop, and how does it behave when inputs are ambiguous or adversarial? In practice, this usually comes down to having clear limits, visibility into how the system behaves, and a way to step in when something does not look right.

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The bank is no longer the full operating environment. Yet many institutions still govern risk as though it is.

That older model assumed the bank was the natural container of risk. Policies, controls, oversight forums, compliance teams, incident processes, and named accountability all sat within a relatively clear institutional boundary. External parties could be managed through contracts, due diligence, and periodic monitoring.

Modern banking runs through a wider ecosystem of cloud providers, software platforms, subcontractors, data suppliers, embedded finance arrangements, service accounts, bots, orchestration layers, and application interfaces. Actions and information now move across multiple organisational boundaries in seconds. The bank may still own the customer relationship and the regulatory exposure, but it no longer owns the full chain through which services are delivered, decisions are influenced, or failures unfold.

That changes the nature of governance. The real question is no longer whether the bank has control over its own operations. It is whether the bank can still trace, challenge, explain, and stop activity once that activity depends on actors and systems that sit partly outside its legal perimeter and often outside its daily line of sight.

This is not just third party risk

A great deal of banking governance still treats this issue as vendor risk with extra complexity. That is too limited.

Traditional third party risk assumes a reasonably clear arrangement. One supplier provides a defined service. The bank performs due diligence, agrees controls, monitors performance, and escalates when standards slip. That model still applies in some cases, but it does not describe the more difficult situations now emerging.

The harder cases involve layered dependency. A platform depends on another platform. A subcontractor relies on specialist providers. An interface feeds data into a hybrid service that is partly run by the bank and partly by someone else. A service account moves information between systems with no human present at the point of action. A bot performs work that looks internal to the customer while being partly external in execution. A regulated decision may be shaped by data, workflow, or prioritisation logic sourced from several organisations, even though the customer experiences it as one seamless journey.

Governance weakens when the boundary disappears

One of the most important shifts in modern banking is that dependency no longer looks like dependency.

In older models, outsourced activity was visibly separate. There was an external provider, a known handoff, and often a clear awareness that the work had moved outside the bank. Today, that separation has become harder to see. An interface call, a rules engine, a token-based service account, or a white-label capability can make external reliance feel like native infrastructure.

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Once dependency becomes invisible in the flow of work, teams stop feeling the boundary. They behave as though the system is continuous even when accountability is not. They assume an activity is governed because it sits inside an approved process. They assume that if something goes wrong, ownership will become clear later. Often it does not.

The chain beneath the supplier matters most

A bank may have a decent understanding of its primary provider and still have a weak grasp of the subcontractor chain beneath it. Yet this lower chain is often where resilience, security, data handling, service continuity, or model behaviour begins to fray. Governance may be strong at the first layer and much weaker by the third or fourth.

At each step down the chain, the bank becomes more dependent on representation rather than direct understanding. Assurances become more summary-based. Incident response slows down. Contract language becomes a weak substitute for real influence. By the time a problem surfaces, the bank may know that something failed without being able to quickly reconstruct how decisions, access, processing, or service delivery actually moved across the chain.

Interfaces, bots, and service accounts are governance issues

Interfaces create speed and strategic flexibility, but they also create governance tunnels. They allow actions, decisions, data, and dependencies to pass across organisations in ways that are efficient only if visibility has been designed from the start.

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Without that visibility, risk can move faster than accountability. External logic can shape customer outcomes without being experienced as external. Partners may rely on the bank’s controls while the bank quietly assumes the reverse.

The same is true for non-human actors. Service accounts, bots, automation scripts, and machine-initiated workflows now perform tasks that once sat with named employees. They retrieve data, trigger actions, reconcile records, move cases, provision access, and feed operational decision-making. Yet many institutions still govern them as technical artefacts rather than operational actors.

That is a mistake.

If a service account can access broad data sets, trigger downstream actions, or bridge systems across organisational lines, it is part of the operating model. If a bot performs a task in a hybrid service arrangement, its permissions, limits, logging, challenge points, and failure modes deserve governance attention comparable to a human role doing similar work.

Banks need to stop treating bots as mere automation projects. Functionally, they are now part of the workforce.

Hybrid products expose the accountability gap

The sharpest governance tension now sits in hybrid products that cross firm boundaries while appearing coherent to the customer. Embedded finance, white-label services, third-party servicing models, and platform-based propositions all create this problem.

The customer sees one service. The legal structure, operational responsibility, and decision chain are split. The complaint may still land with the bank, while the failure may have emerged elsewhere. Data may pass through several parties. The customer may not know, or care, which entity handled which step.

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This is where traditional governance frameworks start to strain. Contractual allocation matters, but it does not solve operational accountability. If a customer suffers harm, who can investigate with end-to-end visibility? If a decision was shaped across a hybrid chain, who can explain it clearly? If the service failed through interaction between systems, who owns remediation?

Complaint handling is an especially useful test. It forces the institution to move from assurance language to traceable truth. If the bank cannot answer, at operational speed, which entity touched the data, which bot triggered the action, which system generated the prioritisation, and which records are authoritative, then its ecosystem governance is weaker than it appears.

What banks need to do differently

Banks do not need another layer of vendor paperwork. They need a governance model built for dependency webs rather than direct suppliers alone.

That starts with mapping the chain at the level where risk actually travels. Not just entity relationships, but data paths, decision paths, credentialed actors, automation flows, subcontractor reliance, and product interactions that cross legal boundaries.

They also need clearer standards for what must be visible, explainable, pausable, and investigable across the chain. If a service cannot meet those standards, the bank should question whether it is governable in its current form, however attractive the commercial case may be.

Most importantly, banks need to govern customer outcomes across the full ecosystem rather than assuming that each party governing its own slice will be enough.

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Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

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We might argue (and for legitimate reasons) that the era of brain-computer interfaces (BCIs) is already underway, albeit in its early stages. Consumer EEG headsets used for neurofeedback and sleep tracking, such as those from NeuroSky, InteraXon’s Muse, and Emotiv, are gaining traction worldwide.

Meanwhile, the number of FDA-cleared AI-enabled devices for neurological monitoring and diagnosis continues to rise each year. And although still largely in clinical trials, companies like Neuralink periodically announce their breakthroughs in headlines all over the media. Finally, researchers at the ATR Computational Neuroscience Laboratories in Kyoto have been working on decoding dreams, using AI to interpret EEG and fMRI data with reported accuracies of around 60–70%.

But right now the “firewall” around the mind is still under construction. So what’s the deal?

While extremely popular, Neuralink is far from the only player working to bridge human thought with machines. Other major contenders include: the multi-institutional BrainGate program; Synchron, backed by big tech founders; Paradromics, whose Connexus interface records activity from individual neurons; Blackrock Neurotech, developer of the widely used Utah-array of microelectrodes; and Precision Neuroscience, founded by a former Neuralink executive.

These companies are also advancing different approaches, ranging from high-bandwidth cortical implants to less invasive stent-mounted or skull-penetrating arrays, each trading off data quality against surgical risk, with early trials showing promise for restoring communication and movement in paralysed patients and even targeting mood disorders, though broad applications like human enhancement remain far from reality.

Yet while headlines fixate on futuristic visions of mind-reading or memory hacking, the real threat is quieter and closer: the systematic failure to apply rigorous cybersecurity and data-privacy protections to the most sensitive data stream ever collected: the human neural code.

This isn’t science fiction. It’s a new digital frontier. And it’s expanding faster than the safeguards meant to protect it. Despite not being cleared by regulators yet, BCIs are not something new. In 1973, Jacques Vidal at UCLA coined the term brain-computer interface (BCI), supported by the U.S. National Science Foundation and later DARPA. His early experiments used electroencephalography (EEG) to translate brain signals into simple outputs: a cursor moving on a screen or a light turning on.

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By the 1990s, with the famous “Decade of the Brain” in the United States, funding surged. Laboratories implanted electrodes in animal subjects, enabling them to control robotic arms or levers. Neuroprosthetics became the anchor use case: artificial devices designed to replace lost function and restore mobility, speech, or agency to those who had lost them.

Outside the labs, however, culture was already decades ahead. William Gibson’s cyberpunk fiction imagined humans as (digital) data conduits. Johnny Mnemonic (1995, dir. Robert Longo) gave us a data courier with a hard drive in his brain. The cult status achieved by the film continues to inspire small (but passionate and rebellious) biohacking communities worldwide.

Of course, there was also the beloved TV series adaptation of the manga Ghost in the Shell (2002–2005, dir. Kenji Kamiyama), which featured neural implant hackers. Where the labs sought restoration, fiction promised augmentation and conquest. BCIs, in other words, were born twice: once in careful experiments, and again in the imagination of writers. That dual birth continues to shape how the field is perceived even today.

BCIs are classified by how close they get to neurons, and that proximity dictates both fidelity and risk:

• Non-invasive systems such as EEG, magnetoencephalography (MEG), and functional MRI (fMRI) are safe and accessible but provide low-resolution data. Researchers have even demonstrated real-time game control in scanners: famously, two humans playing Pong through fMRI, which I’m sure you have seen if you’re browsing the internet all day like me;
• Partially invasive approaches such as electrocorticography (ECoG) place electrodes under the skull but outside grey matter, while endovascular stent-based BCIs (such as Synchron’s) reach the cortex through blood vessels without open-brain surgery;
• Invasive systems use microelectrode arrays implanted directly in neural tissue. These yield the highest resolution but require brain surgery and carry serious long-term safety trade-offs.

The principle is simple: the deeper the electrode, the cleaner the signal… but also the steeper the ethical AND medical stakes.

That said, the first field where BCIs matter is not entertainment or productivity, but medicine. Neuroprosthetics anchor the discipline in restoring dignity BEFORE pursuing augmentation.

Patients with ALS have used cortical implants to type sentences (at around 10–20 words per minute). Robotic arms have been controlled by thought alone. More recently, experiments have decoded internal speech into text, offering voice to those who had lost it. The most powerful technologies often begin with therapy. In BCIs, the first battlefield is not convenience, but human agency itself.

Every BCI collapses the distance between thought and action. For millennia, human expression was mediated through language, gesture, or tool. Now, neurons themselves can become the interface.

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The first step to a realistic policy is abandoning the idea of a single great risk. BCIs vary enormously in capability and vulnerability. One approach might be looking at the threat landscape in tiers:

• Tier one: The present. Consumer-grade EEG headsets are already shipping. While some process signals locally, others can send raw waveforms and attention metrics to cloud servers. That data (focus, stress, emotional state) is a goldmine for targeted advertising and behavioural analytics. It’s less about hacking and more about legalised exploitation under vague consent forms.
• Tier two: The near future. Implanted medical BCIs present a different (and far more urgent) danger. For a person using a neural implant to control a robotic arm or speech synthesiser, the plausible nightmare isn’t “memory injection” but ransomware or denial of service, not to mention hijacked motor commands, silenced voices. Side note: yes, medical (cyber) hackers are real (I’m going to talk about this another time). Today, hospitals and clinics are the 3rd most targeted type of organisation, although these attacks usually do not put people’s lives at risk. You might remember the ransomware attack on multiple Romanian hospitals in 2024, as well as the famous WannaCry virus from 2017 affecting units in the US and UK.
• Tier three: The long game. Manipulating perception or memories at high fidelity remains speculative, but it’s a useful guidepost. Thinking decades ahead helps engineers and lawmakers design guardrails before technology matures.

Law and ethics are struggling to keep up. However, Chile’s constitutional neurorights amendment (the world’s first country to have legislation to protect mental privacy, since 2021) and the OECD’s guidelines on neurotechnology (the Neurotechnology Toolkit from 2025) are early attempts to define mental privacy and identity. But enforcement is weak, and without clear liability standards, manufacturers have little incentive to prioritise security over speed. International standards and funding are needed to keep neurosecurity from becoming another axis of inequality.

So what can be done? From principles to protocol, we must agree that vague calls for “better encryption” (or similar terms) aren’t enough. Instead, greater focus should be on:

• A security bill listing every software component for regulators to audit;
• User-controlled safeguards such as configurable connectivity and even physical kill switches, balanced with medical necessity;
• Public research into how the brain naturally filters or adapts to spurious signals;
• Mandatory red-team (simulated adversary) penetration testing for high-risk neural devices before they reach market.

Also Read: Mind the gap: How understanding the brain can help your startup succeed

Future frameworks should act as a progressive levy on neurotechnology revenues to fund a billion-dollar trust, rapid-response cyber teams, satellite-linked monitoring of supply chains, and regional hubs to ensure equitable access. Ideally, governance would be shared among governments, industry, and civil society, with an independent ethics committee wielding veto power.

The plan borrows lessons from medical-device regulation, environmental treaties, and financial oversight: clear rules, global coordination, and financial penalties for non-compliance. In this model, neurosecurity becomes a public good (like clean water or air traffic control) rather than an afterthought. It requires neuro-specific amendments to laws like GDPR and CCPA, legally defining neural data as a privileged category.

Of course, one might assume such concerns only become relevant once fully fledged BCIs are approved and on the market. Yet signals such as eye movements, facial expressions, speech patterns, respiration, heart rate variability, inertial measurements, and behavioural telemetry can already be gathered, interpreted, and combined without any invasive or even noninvasive brain scans.

In the end, the neurotechnology race won’t be won by whoever decodes the brain fastest, but by whoever earns public trust. Protecting the “brain as a sanctuary” (BaaS, haha) is less about glossy innovation and more about firmware updates, anomaly detection, liability law, and tedious but essential audits.

If engineers, regulators, and ethicists get it right, BCIs could transform medicine, communication, and human capability. If they get it wrong, they could open the most intimate parts of ourselves to exploitation. The firewall around the mind is still under construction. The question is whether the world will finish it before the threats arrive.

But these are just my thoughts on this. What do you think? Is there a real risk, or is it just pure science fiction?

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Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

The post Neurosecurity: Building the firewall around your mind appeared first on e27.

Not long ago, building a company as a solo operator was mostly impractical. Too many moving parts, too much effort, too many skills required.

Today, that constraint is rapidly disappearing. With AI, execution has become dramatically cheaper, and in many cases, accessible to a single person.

But this shift hides a deeper truth: Execution is now cheap, thinking is the real differentiator.

A personal shift: From bottleneck to flow

In my own work, this change is not theoretical, it is operational.

In the last month or so, I have been experimenting more seriously with AI assistance, and the result is that productivity has multiplexed, and I now find myself doing what would previously have been difficult to sustain as a solo operator: writing and publishing long-form articles planning and structuring a book and building multiple software ideas in parallel.

Not sequentially. Concurrently.

A few years ago, this would have required coordination across roles, writers, engineers, designers, marketers, or at minimum, months of fragmented effort. Even more importantly, it would have required time that most ideas never survive. Because in reality, most ideas don’t fail.

They simply take too long to execute and quietly disappear. The constraint was never only creativity. It was an idea surviving under execution friction.

From idea scarcity to idea viability

AI does not just speed up work. It changes what is worth attempting in the first place. When the cost of execution drops, the boundary of viable ideas expands.

Things that were previously:

• Too slow, too complex, too resource-heavy
• Are now within reach of a single individual

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This creates something that feels like a blue ocean, but not of ideas. We have never lacked ideas. We have lacked the ability to test enough of them to discover which ones matter. What AI unlocks is not imagination, but iteration at scale for individuals.

The paradox of lower friction

But there is a second-order effect. As friction drops, participation increases.

When more people can build, more people build. And when more people build, outputs begin to converge.

We now see:

• Similar SaaS products
• Repetitive AI-generated content
• Fast-follow implementations of the same ideas

The barrier to entry has collapsed. But so has the barrier to sameness.

Lower friction does not make building easier. It makes standing out harder.

Vibe coding and the illusion of democratisation

This is where a popular narrative emerges, that vibe coding has democratised app development.

There is truth in that.

AI has made it possible for non-engineers to:

• Generate an application prototype
• Ideas launch basic products

But democratisation is only one side of the story.

The more precise framing is this: AI has lowered the floor of app development, but raised the ceiling of what good looks like.

Two individuals can use the same tools and produce radically different outcomes:

• One produces a functional prototype
• Another produces a system with architecture, extensibility, and long-term thinking

The tools are identical. The thinking is not.

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AI as a reflective system

Most people still treat AI as a mechanical tool.

Something deterministic. Something you “use correctly.”

But this view is incomplete.

It is closer to the parable of the blind men and the elephant—each person touching a different part and believing they understand the whole.

AI is not a fixed system that produces fixed outcomes.

In my view, it is a reflective interface—a kaleidoscopic mirror.

What you get is shaped by what you give it.

AI does not think for you—it thinks with what you give it.

It behaves like a cognitive mirror.

A shallow prompt produces shallow output. A structured, thoughtful prompt produces structured, thoughtful systems.

But the deeper point is this: What emerges from AI is not only a reflection of the model—it is a reflection of the operator.

There is something very ontologically philosophical here in this idea, but we save that for some other discourse.

Back to the existential start-up plane, I saw this clearly while building what was intended to be a simple MVP.

A basic prompt would have produced a basic application.

But the way the problem was framed shifted everything.

Instead of just generating code, the system evolved into discussions around:

• Architecture
• System design
• Scalability
• And future roadmap

The same tool.

A completely different outcome.

Not because the model changed—but because the prompt-surfing went to greater heights.

The new skill stack: Breadth and depth

In this environment, the definition of a capable individual is shifting.

It is no longer enough to specialise narrowly. Nor is it sufficient to remain at a superficial level across many domains.

But there is a harder truth beneath this.

While it is increasingly clear that the future rewards both breadth and depth, not everyone will rise to meet it.

For many, the opposite may happen.

As AI reduces the effort required to execute, there is a subtle risk: the outsourcing of thinking itself.

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When answers are instantly available, the incentive to wrestle with problems declines.

When systems can suggest, refine, and even decide, the habit of forming independent judgment can weaken.

Over time, this leads to a quiet erosion:

• Less depth in understanding
• Less clarity in reasoning
• Less ownership over decisions

Not because individuals lack capability—but because the environment no longer demands it.

In that sense, AI introduces divergence. Some will use it to amplify thinking. Others will use it to replace thinking. The difference is not access. It is discipline.

In a world where intelligence is increasingly available on demand, the discipline to think may become the rarest skill of all.

A return to the Renaissance individual

In some ways, this moment feels less like a technological shift and more like a structural return.

We are re-encountering the multi-domain individual. People like Leonardo da Vinci or Isaac Newton did not operate within narrow boundaries.

They moved across domains, science, art, mathematics, and philosophy, because value emerged at the intersections. Industrial systems later pushed us toward specialisation.

AI, paradoxically, pulls us back toward integration. Not because we must master everything. But because we can now operate meaningfully across more than one domain.

What the one-person company really looks like

The one-person company is no longer a fantasy. But it is also not what people assume. It is not a solo operator doing everything manually. And it is not a replacement for teams at scale.

It is something more structural:

A lean human core, amplified by AI systems that extend execution capacity.

The individual becomes:

• An orchestrator
• A decision-maker
• A taste-maker
• A system designer

While execution is increasingly distributed across tools and agents.

Also Read: AI can accelerate execution, but it cannot replace ownership

The real shift

What is changing is not just cost. It is where value accumulates.

When execution becomes abundant:

• Judgment becomes scarce
• Taste becomes leverage thinking
• Becomes the differentiator

The barrier to building has fallen. But the bar for building something meaningful has risen.

Closing reflection

We are entering an era where more people than ever can bring ideas to life. This is both liberating and demanding.

Because in a world where everyone can build, the question is no longer: Can you execute?

But: What are you choosing to build, and why?

The one-person company is not just a new structure. It is a test of clarity, and our coming reality.

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