In 2020, serial entrepreneur Bryan Oh and Kenneth Palmer identified a growing challenge that would shape the future: the recycling of lithium batteries. Traditional recycling methods were not only expensive but also lacked scalability, creating a pressing need for innovation. Driven by a vision to develop a cost-effective, scalable, and environmentally friendly solution, the duo founded NEU Battery Materials.

“We wanted to make a real impact by enhancing the value chain managed by existing recycling players and making life better for everyone,” Oh shared with e27.

A groundbreaking approach to recycling

Based in Singapore and co-founded by Oh and Palmer, NEU Battery Materials has developed a proprietary electrochemical redox-targeting technology designed for the sustainable recycling of lithium-ion (Li-ion) batteries. Unlike conventional methods like hydrometallurgy and pyrometallurgy, NEU’s process uses electricity as the sole consumable and employs regenerative chemicals, avoiding toxic waste and harsh acids.

Also Read: NEU Battery Materials scores US$3.7M for sustainable recycling of Li-ion batteries

Oh explained that this approach is less polluting than traditional methods, paving the way for the widespread adoption of a cleaner, more sustainable way to recycle lithium batteries, including lithium iron phosphate (LFP) batteries. The technology produces battery-grade lithium, which can be supplied directly to manufacturers, contributing to a circular economy in the battery industry.

An alternative to conventional recycling

Traditional recycling techniques, such as pyrometallurgy and hydrometallurgy, are resource-intensive and often deprioritise lithium extraction. NEU’s innovative electrochemical redox process relies solely on water and electricity, which means it can leverage renewable energy sources to produce green lithium and green hydrogen as by-products.

“We can recycle LFP batteries more economically compared to incumbent technologies. At the same time, we can drive positive climate impact by reducing the carbon footprint and pollution levels while meeting the exponential demand for batteries,” Oh said.

The process eliminates the need for wastewater treatment, reduces operational pollution, and significantly lowers costs by avoiding chemical usage. Its scalable electrolyser technology allows NEU to expand without requiring massive infrastructure investments. “Instead of duplicating facilities, we simply add cells to the electrolyser, enabling us to process 100,000 to 300,000 tonnes of batteries per year from the same commercial assets,” Oh elaborated.

This modular approach–likened to building with Lego blocks–positions NEU as a leader in scalable recycling solutions.

Addressing a gap in LFP recycling

Unlike nickel and cobalt, which dominate traditional battery recycling due to their high economic value, lithium often receives less attention. NEU’s focus on lithium positions it as a unique player in the industry. “By supplying recycled lithium, we help companies meet regulatory requirements and embrace sustainable practices,” Oh said.

NEU’s expertise has attracted interest from larger recycling firms specialising in nickel and cobalt, who now look to NEU for collaboration on LFP recycling. This symbiotic relationship allows NEU to fill a critical gap while larger players focus on their strengths.

Scaling for global impact

NEU Battery Materials operates a 150-square-metre pilot recycling plant in Singapore, with the capacity to process approximately 200 tonnes of lithium batteries annually. However, the company’s ambitions stretch far beyond its home base.

“Recycling is a global challenge that requires collaboration and innovation. From the start, our goal has been to become a global recycling company,” Oh said. NEU takes an open, partnership-driven approach, working with local recycling companies worldwide to complement their expertise and leverage their networks.

Also Read: German Li-ion battery recycling startup tozero wins EPiC 2024 in Hong Kong

Oh emphasised that NEU’s technology offers a competitive advantage, enabling the company to address a pressing global issue while adding value to ecosystem players. “With recent geopolitical tensions and proposed trade restrictions, NEU is positioned to play a critical role in securing access to these vital materials for nations around the world.”

In 2023, NEU Battery secured US$3.7 million in an oversubscribed seed funding round led by SGInnovate, with participation from ComfortDelGro Ventures, Shift4Good, Paragon Ventures I and angels.

Looking ahead

As the world transitions to cleaner energy and electrification, the demand for sustainable battery recycling will only grow. With its innovative technology, commitment to collaboration, and focus on scalability, NEU Battery Materials is poised to lead the way in transforming how batteries are recycled, ensuring a cleaner and more sustainable future.

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Biotechnology companies invest significant time and resources in developing scientific content for regulatory submissions, educating the scientific community and patients, training internal teams, and differentiating themselves from competitors. In an era where misinformation is prevalent, these companies must swiftly and consistently produce high-quality, targeted content.

Beyond drug discovery and manufacturing, content creation is a core activity across various departments, including marketing, medical affairs, research and development, regulatory, and pharmacovigilance. Essentially, biotechnology companies create large volumes of content.

Writing high-quality, compliant content is complex and demanding, especially in a highly regulated industry. There is often more work than available personnel, and even when writing is outsourced, it requires time to supervise and review agency outputs. Budget constraints and layoffs further limit resources for content development.

Writing is just one facet of the content development workflow. Content creators must educate themselves, sift through an ever-growing volume of scientific publications, draft outlines, collaborate with stakeholders, and review drafts. These tasks are time-consuming and labour-intensive.

Given these challenges, innovative biotechnology companies, use Artificial Intelligence (AI) for scientific content generation. AI is not only a technological advancement but a strategic necessity. As the industry discovers new products, the demand for efficient, accurate, timely, and cost-effective content development is crucial for success.

Generative AI has the potential to revolutionise how scientific data is processed, analysed, and presented, pushing biotechnology leaders and scientists to rethink their content generation strategies.

The role of generative AI in scientific content generation

AI-powered tools can be utilised in all phases of content creation, enabling faster and more accurate generation of scientific documents. Creating scientific content manually can take weeks or even months. AI drastically reduces this time by automating repetitive tasks and providing data-driven insights that accelerate the writing process.

Also Read: Generative AI for sustainability: How these startups are saving the planet with the technology

For example, AI can significantly reduce the time needed to search literature databases, draft outlines, and review content. AI writing assistants are invaluable for paraphrasing, writing titles, checking spelling and grammar, changing tone, generating plain language summaries, seamless citation generation, and language translations—tasks that are often time-consuming or require external expertise.

Examples of AI-generated content in biotech

Biotech companies can leverage AI to create diverse content types, including clinical study reports, regulatory submissions, slide presentations, posters and abstracts, marketing materials, journal articles, medical information letters, training materials, patient information leaflets, and plain language summaries. Announcements about using AI for drug discovery generate more attention. 

However, companies are also using AI in other areas. Recently, Moderna announced a collaboration with OpenAI to integrate AI across all departments and business processes. Agencies that produce clinical study reports and other content for biotech companies are also adopting AI tools, further highlighting its versatility.

Impact of AI on content creation cost

The cost of generating scientific content can be substantial. Developing a slide presentation can cost between $20,000 and $60,000 when outsourced to an agency. Biotechnology companies spend millions annually on content development. AI can help mitigate these costs by automating many aspects of the content creation process.

Experts estimate that generative AI tools can reduce the time to write a clinical study report by nearly half, improving the speed of regulatory submissions by 40 per cent, while significantly reducing costs across regulatory teams.

Moreover, AI enhances content quality by minimising human errors and ensuring consistency across documents. This quality improvement can save costs by reducing the need for extensive revisions and rework.

Concerns about using AI in scientific content generation

Several concerns must be addressed to ensure the effective use and adoption of AI tools in scientific content-generation workflows. These concerns include accuracy, data safety and privacy, the availability of fit-for-purpose solutions, the cost of implementation, and the learning curve associated with using AI tools effectively.

• AI accuracy: AI systems rely on algorithms and data inputs, which have the potential to lead to errors or misinterpretations. Ensuring the accuracy of AI-generated content is critical, particularly in fields requiring precision, such as biotechnology. With human oversight and guided prompts, AI can produce accurate outputs comparable to those of subject matter experts.
• Data safety and privacy concerns: AI systems require access to large datasets, raising concerns about the safety of sensitive or proprietary information. Companies can mitigate risks by restricting AI use to non-sensitive data and employing models that do not train on proprietary information. Robust data protection measures, like encryption and compliance with privacy regulations such as GDPR or HIPAA, are essential for safeguarding data.
• Fit-for-purpose AI solutions: Generic AI models alone are often insufficient for creating life sciences content. Companies should collaborate with life sciences AI vendors to develop tailored solutions that integrate into existing workflows. Thorough evaluations ensure AI tools align with organisational needs and effectively support content generation processes.
• Cost of implementation: Deploying AI involves expenses for software, infrastructure upgrades, and maintenance, requiring a cost-benefit analysis to assess ROI. Scalable and cloud-based AI solutions, along with pilot projects, can reduce upfront costs and test suitability before full implementation. Most companies cannot afford bespoke large language models, making scalable solutions more practical.
• Training and workforce development: Successful AI adoption requires employees to gain skills through comprehensive training programs. Fostering a culture of continuous learning with workshops, online courses, and seminars is key to equipping teams to leverage AI. Cross-functional collaboration and celebrating AI-driven successes can enhance adoption and effectiveness.
• Job displacement concerns: While AI may replace certain tasks, it cannot replicate human experience, strategic thinking, or judgment. Instead of replacing jobs, AI enhances professional capabilities and creates new opportunities. Workers proficient in AI are more likely to succeed than those who resist leveraging it effectively.

Also Read: The rise of generative AI: 6 ASEAN countries leading the charge

Embracing AI: The key to revolutionising biotechnology’s content future

Integrating AI in biotechnology content generation presents a transformative opportunity to enhance efficiency, accuracy, and productivity. Biotechnology leaders and scientists must take proactive steps to integrate AI into their content generation workflows.

This involves investing in fit-for-purpose AI solutions, ensuring data privacy and security, and fostering a skilled workforce ready to embrace technological advancements. By doing so, companies can increase efficiency, focus more on strategy and innovation, and maintain a competitive edge.

The question is no longer whether AI should be used but how to effectively integrate AI into biotechnology content development workflows.

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AI-generated deepfakes in crypto have emerged as the most alarming threat to the ecosystem, with a projected US$25 billion in losses by the end of 2024. In the first half of 2024, crypto scammers siphoned US$679 million in user funds. This figure is closely in line with the last two quarters of 2023, showing scammers have no plans of slowing down.

“Pig butchering” is a form of these scams that has proven very successful. Amongst many other examples, in October 2024, Hong Kong authorities uncovered an operation where deepfake technology was used to create fake romantic profiles, eventually leading victims into fraudulent crypto investments.

For context, over 15 billion AI-generated images were created in a single year, equating to 150 years of photography. While this is a staggering development, it has become a Pandora’s box of digital fraud that poses a serious threat to all digital transactions.

AI deepfakes in crypto: A new era of crime

With minimal technical knowledge, AI can now generate hyper-realistic deepfake content. As a result, it is becoming increasingly hard to distinguish between legitimate and deceptive media.

Deepfakes are particularly concerning in crypto for two reasons:

• Sybil attacks: Criminals can exploit AI-generated identities to create multiple fake identities, manipulating blockchain governance systems and consensus mechanisms. These attacks harm the fundamental trust of decentralised systems.
• Bypassing KYC: Deepfakes enable bad actors to appear as legitimate users and bypass Know Your Customer (KYC) checks, allowing them access to financial services using false identities.

The computing power for AI training has quadrupled year on year for the past decade, accelerating the sophistication of deepfakes to a point where this technology is accessible to anyone, including bad actors. These scammers can now create convincing videos impersonating influential figures promoting fake crypto schemes, as seen with Elon Musk.

This rapid progression, paired with the potential for misuse highlights the critical need for solutions that ensure these powerful technologies are developed and used responsibly.

Also Read: Cross-chain interoperability: The key to unlocking crypto’s true potential

Tokenised identity: A defence against deepfakes

The rise of deepfakes has exposed vulnerabilities in traditional security systems, particularly those reliant on outdated KYC and anti-fraud measures. As these defences struggle to keep pace, tokenised identity emerges as a powerful solution.

Tokenised identity leverages blockchain technology to verify and authenticate identities. Against deepfakes, tokenised identity is routed in three principles; dynamic verification, source validation and immutable records. This multi-layered verification approach targets the vulnerabilities exploited by AI deepfakes.

At the forefront of these innovations is the implementation of face comparisons for continuity checks and liveness detection systems. Unlike traditional static checks, like uploading your passport photo, this new dynamic check will ask you to do something in real-time, like move your camera closer to your head.

An advanced deepfake scammer could present a convincing fake photo and fool the system. However, with dynamic verification, the task of fooling the system becomes more challenging. Systems that can prove genuine human interactions are the first step.

The next step of the process extends beyond facial recognition to include source verification to ensure individuals have explicitly consented to the use of their likeness and validate the authenticity of their identity claim. This prevents bad actors from using an Elon Musk deepfake to open a crypto account, even if the video is perfect and bypasses dynamic verification, as they don’t have Elon’s real ID or actual permission. By utilising tokenised identities that prove both consent and authenticity, businesses can establish a powerful defence against deepfakes. 

The final step of tokenised identity when combating deepfakes is the tying of digital assets to verified individuals through immutable privacy-preserving blockchain records. This ensures that once an identity is verified, its proof cannot be tampered with or duplicated when it is used in on-chain use cases. 

For businesses concerned about protecting their platforms and users, these enhanced identity checks become crucial custodians against unauthorised access and identity theft.

Implementing a new standard of security

Across the industry, identity verification is not approached as an exact science. Instead, it’s thought of as a matrix of signals evaluated in aggregate, of which ID document verification is just one potential component. 

Businesses should choose an appropriate level of identity verification based on their specific use case and risk tolerance. In other business use cases where a high degree of certainty is required, checks such as knowledge-based authentication, re-verification at the time of transaction, and document verification may easily be added and help combat AI deepfakes. 

When it comes to beating AI, in-person verification (physically verifying a person compared to their ID) is the ultimate indicator that a person is the individual they claim to be. 

Conclusion

AI deepfakes represent a US$25 billion threat that crypto can no longer afford to ignore. To protect users and restore trust in the ecosystem, the industry must embrace solutions like tokenised identity. These tools can ensure authenticity, safeguard privacy, and enable a more secure digital future.

Collaboration between the blockchain and AI industries will be essential to developing strong measures against deepfake fraud. Action must be taken now so that risks can be mitigated and the full potential of decentralised technology can be unlocked. 

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In today’s fast-paced world, banking isn’t what it used to be. Gone are the days of waiting in line at brick-and-mortar branches; instead, we’re ushering in the era of digital banking, where everything you need is just a tap away on your smartphone.

Digital banking, the heart of the fintech world, has completely transformed the way we manage our finances. No longer confined by physical limitations, digital banks offer a plethora of financial services right at our fingertips. From depositing checks to investing in stocks, everything can be done with the touch of a button.

But as the popularity of digital banking skyrockets, so do concerns about security. After all, with great convenience comes great responsibility — and vulnerability. With sensitive financial information floating around in cyberspace, are these digital banks really as secure as they claim to be?

Let’s delve into why digital banking has become the backbone of the masses. Convenience is the name of the game. Imagine being able to pay your bills while waiting for your morning coffee or transfer funds between accounts without ever leaving your couch. Digital banks offer unparalleled convenience, coupled with lower fees and personalised services tailored to your needs.

Also Read: How an AI cybersecurity company harnesses the power of AI for optimal business performance

However, convenience doesn’t come without its fair share of risks. With cybercriminals lurking around every corner of the internet, security is a top priority for digital banks. Luckily, they’ve stepped up to the plate with state-of-the-art encryption technology and stringent identity verification measures. Two-factor authentication? You betcha. These banks leave no stone unturned when it comes to protecting your hard-earned cash.

But security isn’t a one-way street; users must also do their part. From setting strong passwords to avoiding sketchy Wi-Fi networks, there are plenty of steps we can take to keep our accounts safe and sound. After all, it’s a team effort to keep the digital banking ecosystem secure and thriving.

In the grand scheme of things, digital banks aren’t just here to stay; they’re here to revolutionise the way we think about finance. With more players entering the market every day, we’re on the brink of a fintech renaissance.

But it’s up to all of us — banks and users alike — to ensure that the future of banking is not only convenient but secure. After all, when it comes to our money, there’s no room for compromise.

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This article was first published on February 26, 2024

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AI is everywhere these days. It’s in the tools we use at work, the apps on our phones, and even in the suggestions we get while shopping online. For some people, it’s exciting—like living in the future. For others, it’s terrifying—“Will AI take my job?”

But here’s the truth: AI isn’t here to replace us. It’s here to help us. The real question isn’t whether AI will take over but whether we’ll take advantage of it to stay ahead in our careers.

Let’s talk about how to make AI your ally and not your competition.

Why you should embrace AI, not fear it

Remember when calculators became a thing? People thought it would make us bad at math. But instead, it freed us from tedious calculations so we could focus on problem-solving. AI is the same.

Here’s why you should lean into it:

• It saves time: AI tools can handle repetitive tasks, like sorting emails or summarising documents, giving you more time to focus on strategic work.
• It boosts creativity: Struggling with ideas? AI can help brainstorm or refine your concepts, whether you’re designing a project, writing, or problem-solving.
• It keeps you relevant: In many industries, knowing how to use AI tools is becoming a must-have skill.

The key is learning to work with AI, not against it.

How to use AI positively in your career

So, how do you start? It’s simpler than you think.

Learn the tools of your trade

Every industry has its go-to AI tools. If you’re in marketing, it might be analytics platforms or content generators. In design? Think AI-powered creative tools like Canva or Adobe Firefly. Explore what’s trending in your field and experiment with them.

Focus on skills AI can’t replace

AI is great at processing data, but it struggles with soft skills—like empathy, leadership, and critical thinking. Sharpen these skills to stay irreplaceable.

Also Read: 3 ways AI technology can help startups save money

Use AI to supercharge your productivity

Here are some practical ways to use AI every day:

• Streamline research: Tools like ChatGPT or Notion AI can summarise articles or suggest ideas.
• Automate admin work: Use AI to schedule meetings, draft emails, or create reports.
• Up-skill quickly: Platforms like Coursera or YouTube now have AI-curated learning paths to help you master new topics fast.

Stay curious

AI is evolving fast. Stay updated on trends, but don’t overwhelm yourself. Subscribe to a newsletter or follow experts on LinkedIn to learn about advancements at your own pace.

Turning AI into an opportunity

Instead of worrying about AI replacing jobs, think about this: AI is creating entirely new ones. Roles like AI trainers, prompt engineers, and AI ethicists didn’t exist a few years ago. By positioning yourself as someone who understands AI, you can unlock career opportunities you never imagined.

Here’s an example: Imagine you’re a graphic designer. AI can now create stunning visuals in seconds. Instead of fearing it, you could learn to use AI to speed up drafts, focus on high-level concepts, and offer even more value to your clients.

Using AI responsibly

Of course, with great power comes great responsibility. Here are some things to keep in mind:

• Fact-check everything: AI can sometimes give wrong or outdated information. Don’t take it at face value.
• Respect privacy: Be careful when inputting sensitive or confidential information into AI tools.
• Be mindful of biases: AI learns from data, and data can be biased. Use your judgment to ensure fairness and accuracy.

The future is now—Are you ready?

AI isn’t the enemy. It’s a powerful tool we can use to work smarter, be more creative, and unlock new career paths. The sooner we embrace it, the better positioned we’ll be to thrive in an AI-driven world.

So, instead of fearing the future, let’s shape it. Start small. Try out a new AI tool. Learn something new. The opportunities are endless if you’re willing to explore.

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Editor’s note: e27 aims to foster thought leadership by publishing views from the community. Share your opinion by submitting an article, video, podcast, or infographic.

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