In the high-pressure world of startups, burnout and resilience are major concerns for founders striving to build sustainable businesses.

The Echelon X panel discussion titled “Sustainable Hustling and Resilience for Startup Entrepreneurs: Tactics Founders Have Implemented to Reduce Burnout and Play the Long Game When Building Their Startups” offered valuable insights into navigating these challenges.

For founders grappling with burnout or seeking ways to bolster resilience for themselves and their businesses, the panel provided effective tactics for reducing burnout and fostering a sustainable approach to entrepreneurship. The discussion covered practical tips on managing stress, staying motivated, and ensuring long-term success while avoiding common pitfalls.

Moderated by Terence Chia, Co-Founder of Folklory, the panel featured:

• Joan Low, Founder and CEO of ThoughtFull
• Jx Lye, Founder and CEO of Acme Technology
• Evan Heng, Founder and CEO of Zenith Learning Group
• Henry Motte de la Motte, Founder and CEO of EDGE Tutor

By sharing their personal experiences and strategies, the panelists provided a roadmap for maintaining mental, physical, and emotional well-being while navigating the challenges of startup life. Their insights underscored the importance of self-care, community support, and a balanced approach to work and life, equipping entrepreneurs with the tools they need to play the long game and achieve sustainable success.

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In the startup and VC world, Generative AI (Gen AI) is certainly creating a big wave. If you are an entrepreneur and would like to start a business leveraging the power of the Large Language Models (LLMs) — or the name of it — what is the battle plan? What are the areas you may want to bear in mind?

I’ve been speaking to leading experts in the field. One of them is Dr. Dan Roth, Ph. D., who is a Distinguished Professor of Computer and Information Science at the University of Pennsylvania. He has decades of experience in the technology, software and AI innovation space. With one foot in the technical world and the other in the entrepreneurial world, Dr. Roth has a bird’s eye view of the Gen AI wave — as well as its nuances.

Here are the learnings:

Utilise existing language models

Building your own model from scratch requires significant investment and expertise. Leveraging existing models can save time and resources, allowing you to focus on fine-tuning for specific applications.

Identify your differentiators

Determine what sets your approach apart. This could involve using better or unique data, or applying data in more innovative ways. Fine-tuning models with high-quality, application-specific data can significantly enhance performance.

Also Read: Beyond the hype: Taking Gen AI mainstream with next-level automation

Save costs

• Use smaller models and optimise inference: Large models like GPT-4 are powerful but costly. Smaller models, even as compact as 3B or 7B parameters, can be highly effective and more economical. Fine-tuning these smaller models on your own data can further reduce costs. Investing in efficient inference technologies, such as model quantisation, can also significantly cut expenses.
• Distill models for cost-effective inference: Employing methods to distill smaller models can enhance their efficiency, making them more cost-effective for production use. This approach is being adopted by several startups to improve inference efficiency.
• Consider simpler models when appropriate: Not all problems require large language models (LLMs). For specific tasks like information extraction, smaller, fine-tuned models can outperform even the largest LLMs. Understanding the tasks your application needs to perform will help you choose the most appropriate and cost-effective model.
• Develop a robust evaluation protocol: Establish a comprehensive evaluation protocol that includes both automatic metrics and human assessments. This builds trust with investors by demonstrating a thorough understanding of your technology’s capabilities and limitations.
• Address hallucinations: Implement systems to evaluate and mitigate hallucinations in your models, focusing on both factual inaccuracies and reasoning errors. Utilise metrics like accuracy and F1 score, and ensure human evaluation is part of your assessment process.
• Be mindful of misinformation: Consider the potential for your models to generate toxic information or misinformation. Implement safeguards to prevent misuse and minimise the risk of information pollution.

“You have to think about who will use your tools and whether they will be careful or not. […] It could be a PR disaster if someone generates toxic information or misinformation, which could find its way to [X]. You need to think about this, and it’s a function of who you’re giving the models to. […] Information pollution doesn’t get enough visibility. It’s really a scary space. […] You have to think about whether you care about your model generating toxic information.” —  Dr. Roth, Professor of Computer and Information Science at the University of Pennsylvania.

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The Third Evolution of the Creator Economy: A Glimpse into AnyMind’s AnyTag

The creator economy is flourishing in an era of digital innovation and technological advancements. The Echelon X fireside chat titled ‘The Third Evolution of the Creator Economy: A Glimpse into AnyMind’s AnyTag’ provided a deep dive into how AnyMind Group, a key player in the creative industry, is revolutionising influencer marketing through its innovative AnyTag platform.

Moderated by Casey Lau, Head of Asia at RISE Web Summit, the fireside chat featured Otohiko Kozutsumi, Co-Founder of AnyMind Group.

Kozutsumi shared insights into how AnyTag is transforming the landscape of influencer marketing by streamlining processes and enhancing the impact of creator-driven campaigns.

His insights provided valuable perspectives on the evolving landscape and the innovative solutions that are driving the third evolution of the creator economy. By leveraging AnyTag, creators and brands can unlock new opportunities and achieve greater success in the digital age.

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The Asia Pacific region is at a pivotal economic point due to the tightened monetary policies in response to global inflation. By extension, tighter capital liquidity is impacting tech companies far and wide in the first major global recession since 2008.

But the good news is that challenges and opportunities are often two sides of the same coin. The Asia Pacific cloud computing market is steadily accelerating at a compound annual growth rate of 15.6 per cent, despite the economic decline that has hit several industries in the region.

Venture capitalists, in particular, are seeking stable opportunities to mark their bets in the long run. Among the host of digital-centric businesses that have growth potential, the US$480 billion cloud computing market is capturing strong interest.

Why Big Data and AI are driving cloud computing demand

For years, cloud computing has been a core innovation factor for digital transformation. Fast forward, the tech is converging deeper with big data and artificial intelligence (AI) to power a host of business functions.

Although AI is largely known as a stand-alone technology that facilitates in-depth analysis of consumer behavioural patterns, its relationship with cloud computing has gained VC attention. This convergence is instrumental in enhancing productivity, as well as efficiency. For instance, businesses can now deploy applications in the cloud linked to machine learning resources. Firms such as FPT software, Vietnam’s largest ICT company, are investing in deep learning R&D centres to create AI-based solutions that can shield businesses from unknown attacks, such as zero-day malware.

Also Read: How to migrate your small business to the cloud

With this approach, businesses can extend their capacity in terms of data insights, team integration, and agile development to explore further business opportunities. A study by PwC predicts that “AI could contribute up to US$15.7 trillion to the global economy in 2030,” a figure that is drawing in more capital funding.

“Unparalleled opportunity” in digital assets mining

Cloud computing isn’t limited to big data calculations. Another emerging use case for cloud centres is Proof of Work (PoW) mining, which draws similar properties to traditional cloud computing in terms of energy usage and operational management. However, PoW mining facilities benefit more from how physically close they are to the power source, rather than the end customer.

Blockchain networks secured by PoW mining such as Bitcoin require substantial energy and specialised hardware. Having its unique set of requirements, “digital assets mining provides an unparalleled opportunity to Asia-based investors in terms of diversification and upside beta exposure to the cryptocurrency space,” comments Lin Cheung, CEO of JKL Group.

Bitcoin, the longest-running public blockchain to date, has garnered strong institutional interest due to its attractive propositions.

“On one hand, Bitcoin mining delivers a stable future cash flow dictated by the algorithm of Bitcoin blockchain, which provides a solid baseline for valuation. On the other hand, the ROI of digital assets mining mostly depends on four variable factors: price of equipment, electricity rates, digital asset output, and price of the mined digital asset. While these factors can be volatile, it is also up to the investor to determine at what profitability levels to switch the miner on and off to secure the upside beta exposure,” says Cheung.

Also Read: Cloud communications firm Toku nets US$5M Series A+ for APAC expansion

The cloud mining segment is projected to account for the largest revenue share in the estimated US$17 billion global digital assets mining market. Global miners utilise online cloud mining services to optimise cost while securing the Bitcoin blockchain as network miners. This allows miners to easily switch between different cloud services as the profitability rate can fluctuate based on variable energy costs and macro factors.

Maximising energy efficiency in win-win scenarios

Regions with low energy costs have attracted Bitcoin mining operators across the globe. Texas, US, is a prime example. The Electric Reliability Council (ERCOT) regulating the Texas grid rewards energy credit to customers who vary their energy usage in real-time. “It is a win-win scenario since the energy grid and miners respectively benefit from optimised load balancing and cheaper prices,” says Cheung.

More than 53 highly profitable businesses including Tesla and Hewlett Packard Enterprise have moved their headquarters to Texas. Large-scale Bitcoin mining firms such as Marathon Digital and Riot Blockchain have established operations in the region, with more players such as JKL Group ramping up new Bitcoin mining centres.

At a time when every resource counts, the cloud computing sector is positioning itself as a go-to market for venture capitalists. To stay afloat during the economic downturn, business profitability, stability and upside potential are important factors to consider. As once stated by Britain’s former Prime Minister Winston Churchill, “the optimist sees the opportunity in every difficulty.”

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This content was first published by The Human & Machine.

Image Credit: The Human & Machine

This article was first published on November 28, 2022

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The Southeast Asia region has emerged as a hotspot for fintech innovation and growth, with its large population, rapidly expanding middle class, and increasing digital adoption. While the potential rewards are significant, entering the Southeast Asia market as a fintech company comes with a unique set of challenges and complexities.

This article delves into the difficulties fintech companies encounter when venturing into the Southeast Asian landscape.

Regulatory hurdles

Navigating the complex regulatory environment in Southeast Asia can be a formidable challenge for fintech companies. Each country within the region has its own set of financial regulations, licensing requirements, and compliance standards. Achieving and maintaining regulatory compliance can be a time-consuming and costly process.

Compliance variability

Even within a single country, regulatory requirements can vary significantly, posing a compliance challenge. Companies need to stay abreast of changes in regulations, which may be influenced by political, economic, or social factors.

Customer trust and data privacy

Building trust among Southeast Asian consumers is paramount for fintech success. Concerns about data privacy and cybersecurity have grown, making it essential for companies to demonstrate their commitment to protecting user data.

Consumer education

Many consumers in the region may not be familiar with fintech services, necessitating extensive education and awareness-building efforts. Clear communication and user-friendly interfaces are vital to overcoming this challenge.

Currency and exchange rate risk

Dealing with multiple currencies in the region presents currency risk. Fintech companies must devise strategies to manage exchange rate fluctuations and offer multi-currency services.

Competition from established players

Local and international banks and financial institutions often have a strong foothold in the Southeast Asian market. Competing with these established players can be challenging, requiring fintech companies to offer compelling value propositions.

Payment preference variability

Southeast Asia exhibits a diverse range of payment preferences, including digital wallets, bank transfers, cash payments, and mobile money. Adapting to these preferences and integrating with local payment providers is essential.

Infrastructure and connectivity

While urban areas in Southeast Asia are typically well-connected, rural regions may lack reliable internet access and financial infrastructure. This digital divide can hinder the reach of fintech services.

Also Read: Essential tips for scaling in Southeast Asia: 4 key insights to consider

Political and economic instability

Some countries in the region have a history of political and economic instability. Fintech companies need to carefully monitor these developments and assess risks to their operations.

Partnerships and local relationships

Collaborating with local banks or financial institutions may be necessary for certain fintech services. Building these partnerships and navigating local relationships can be complex.

Language and cultural barriers

Language diversity and cultural differences across the region can pose communication and marketing challenges. Tailoring content and services to local customs and preferences is essential.

Access to rural markets

Expanding into rural and remote markets can be logistically challenging. Fintech companies must develop strategies to overcome these geographical barriers and reach underserved populations.

Financial inclusion

Promoting financial inclusion is a significant goal in Southeast Asia. Fintech companies must develop services and strategies to reach unbanked or underbanked populations.

Currency regulations

Some countries may impose strict currency controls or limitations on fund transfers, affecting the operations of fintech companies.

Customer support and localisation

Providing customer support in multiple languages and adapting services to local customs and preferences can be resource-intensive but is essential for customer satisfaction.

Conclusion

While Southeast Asia presents immense opportunities for fintech companies, the journey is riddled with challenges that require careful planning, adaptation, and resilience. Successful market entry and growth in this diverse and dynamic region hinge on a combination of factors, including regulatory compliance, consumer trust, innovation, and effective localisation.

Fintech companies that navigate these complexities wisely can unlock the vast potential of the Southeast Asian market and contribute to financial inclusion and digital transformation in the region.

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