The once-thriving billion-dollar startup bubble is rapidly losing air, leaving over $1 trillion in trapped value within companies that now face dwindling opportunities. It may seem like a distant memory, but before artificial intelligence took over as Silicon Valley’s main obsession, the startup ecosystem was booming with innovations across various sectors. By the peak of the Covid-era tech surge in 2021, more than 1,000 venture-backed startups had secured valuations exceeding $1 billion, joining the unicorn club. Among them were Impossible Foods, known for its plant-based meat, Thumbtack, a platform for home services, and MasterClass, a popular online education company. However, this momentum quickly faded due to rising interest rates, a slowdown in the IPO market, and a growing perception that non-AI startups were falling out of favor. A Long-Awaited Reckoning Becomes Reality What had long been anticipated is now hitting home. In 2021 alone, 354 startups reached unicorn status, but according to Stanford professor Ilya Strebulaev, only six have successfully gone public since then. Another four took the SPAC route, and 10 managed to secure acquisitions—though several were valued at under $1 billion when sold. Some, like Bowery Farming (indoor agriculture) and Forward Health (an AI-driven healthcare company), have shut down entirely. Even once-promising businesses like Convoy, a freight logistics company once worth $3.8 billion, collapsed in 2023, with Flexport acquiring its remaining assets for a fraction of their previous value. Many startups now feel as though the floor has disappeared beneath them, says Sam Angus, a partner at Fenwick & West. The reality of fundraising has fundamentally changed, making it much more difficult to secure new capital. The Rise of "Zombie Unicorns" Welcome to the era of zombie unicorns—startups that once commanded billion-dollar valuations but are now stuck in limbo. CB Insights reports that 1,200 venture-backed unicorns have yet to go public or be acquired, with many forced into desperate financial maneuvers. Late-stage startups face particularly harsh conditions, as they typically require large amounts of funding to sustain operations. However, investors who once eagerly wrote checks for billion-dollar valuations have become far more selective. For many, down rounds, fire-sale acquisitions, or steep valuation cuts are the only options left to avoid complete failure—risking a fate where they become unicorpses rather than unicorns. A Harsh Funding Reality The fundraising downturn began in 2022, largely triggered by the Federal Reserve's series of interest rate hikes, which ended a decade of easy access to capital. As borrowing costs surged, companies across industries cut expenses and laid off employees, with tech-sector layoffs peaking in early 2023, according to Statista. Some startups that were previously focused on rapid expansion at any cost have since pivoted to prioritizing short-term profitability in an effort to reduce reliance on venture capital funding. The Aftermath of the Boom  Number of unicorns going public via IPO per year (Source: CB Insights) However, many startups were built on high-growth models that disregarded short-term profitability, assuming they could continue raising funds at increasingly higher valuations. That assumption has backfired in the current market. According to Carta Inc., a fintech firm that tracks startup funding, fewer than 30% of 2021’s unicorns have raised additional financing in the past three years. Of those that have, nearly half have done so at significantly lower valuations—a sign of distress for many companies. For instance, Cameo, a platform for celebrity video greetings, once boasted a $1 billion valuation but raised new funds last year at a staggering 90% discount, according to a source familiar with the matter. Similarly, fintech company Ramp, which was valued at $8 billion in 2021, has since raised two major funding rounds at lower valuations. In some cases, down rounds have helped struggling startups regain stability. ServiceTitan, a contractor software company, initially raised money under unfavorable terms in 2022 but later exceeded those valuations when it successfully went public in 2024. It now boasts a market cap of $9.4 billion, aligning with its peak private valuation of $9.5 billion in 2021. The Vicious Cycle of Down Rounds However, restructuring efforts like job cuts and valuation declines can create a downward spiral. Startups rely on momentum to attract investors, and when they start sacrificing growth for financial discipline, it becomes much harder to maintain confidence in their future prospects. For employees, one of the biggest incentives to work at a startup is the potential for valuable equity stakes. But as valuations decline, many workers begin looking for opportunities elsewhere, causing further instability within these companies. Creative Measures to Avoid Valuation Declines Startups in relatively stable financial positions are resorting to various tactics to avoid openly admitting valuation declines. Some are classifying new fundraising rounds as extensions of previous ones, allowing them to maintain the illusion of a flat valuation rather than acknowledging a decrease. In this tough market, even flat rounds are now considered a success.  Average time from unicorn valuation to IPO, in years (Source: CB Insights) Other companies are being forced into far less favorable deals. Some funding agreements now include structural changes in ownership, such as pay-to-play provisions, which require previous investors to participate in new rounds or risk losing their equity stake. These deals are often deeply unpopular among existing shareholders. In 2023, Ryan Breslow, the co-founder of payments startup Bolt, attempted to raise funds through a pay-to-play round, only to face strong pushback from major investors—eventually derailing the effort. The Harsh Reality for Struggling Startups For some, taking on onerous financing terms is simply delaying the inevitable. The digital pharmacy Truepill, for example, was acquired after a pay-to-play round—but at a valuation nearly two-thirds lower than its 2021 peak, according to PitchBook Data. To many investors, such high-risk deals are a clear red flag that a company is on its last legs. Jeff Clavier, founder of Uncork Capital, puts it bluntly: "If a company has to resort to these kinds of funding deals, it's probably doomed anyway." Who’s Buying? The Role of Private Equity For the startups that still hold value, deep-pocketed firms like private equity investors may step in to acquire them. However, expectations should be tempered—businesses simply aren't going to command the kind of valuations they once did, says Chelsea Stoner, general partner at Battery Ventures. What’s Next? A Long Shot for Recovery For the few optimists still holding out hope, there’s speculation that a fresh wave of investor enthusiasm could turn things around. Some believe a potential shift in U.S. regulatory policies—such as a Trump administration without FTC Chair Lina Khan—could reignite M&A activity and boost IPO markets. However, Greg Martin, founder and managing director at Archer Venture Capital, remains skeptical. "Unless we see another bubble fueled by zero-interest rates—like the one we saw during the pandemic—many of these zombie unicorns are headed straight for the graveyard," he warns. Source: https://www.bloomberg.com/news/articles/2025-02-14/silicon-valley-unicorn-startups-are-desperate-for-cash?srnd=phx-technology-startups

The Challenge of Machine Learning Startups In the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML), one of the biggest challenges faced by startups is the data paradox—a dilemma where an ML model requires high-quality training data to function effectively, but such data is difficult to acquire without first launching a viable product. This paradox creates a vicious cycle where an AI-driven startup struggles to achieve accuracy and credibility without access to reliable datasets. This issue is particularly prominent in models that depend on social media data and user-generated interactions. While platforms like Instagram, Twitter, and Facebook provide vast amounts of publicly available content, scraping this data often violates their Terms of Service (TOS), leaving ML startups with limited legal avenues to obtain the information necessary for training their algorithms. Given these constraints, alternative approaches must be explored to develop high-quality datasets while remaining compliant with ethical and legal guidelines. The Legal and Ethical Constraints of Data Acquisition Many startups rely on web scraping to gather public data. However, major social media platforms explicitly prohibit this practice in their TOS, leading to potential legal consequences and service bans. In recent years, lawsuits against data-scraping companies, such as LinkedIn’s battle with hiQ Labs, have underscored the risks associated with unauthorized data collection. Purchasing pre-existing datasets is another option, but this method comes with challenges such as high costs, data irrelevance, and quality control issues. Furthermore, many commercially available datasets lack diversity or fail to capture the real-world nuances that modern AI systems require to make accurate predictions. Faced with these limitations, many ML startups are turning to user-submitted data as a potential solution. The Role of User-Generated Data in Model Training One ethical and scalable approach to solving the data paradox is to crowdsource the dataset through direct user participation. This method involves creating a system where individuals voluntarily submit data about their own experiences and interactions, helping to build a high-quality dataset before a product is officially launched. However, convincing users to contribute data without an established platform remains a significant challenge. To encourage participation, startups often deploy various incentive models, including: Early Access to Premium Features – Allowing contributors to test beta features before the general public. Verified Status or Recognition – Providing a credibility badge to early adopters within the platform. Exclusive Insights and Analytics – Offering AI-generated reports based on user-submitted data. Gamification and Rewards – Creating engagement-driven incentives such as leaderboards or community perks. While these strategies can help gather 50-100 high-quality submissions, reaching a statistically significant dataset remains an uphill battle. Alternative Approaches to Building a Dataset Aside from user-generated data, ML startups can explore several other methods to compile a foundational dataset: Leveraging Publicly Available Datasets Several organizations and universities maintain open-source datasets that can serve as a starting point for model training. Platforms like Google Dataset Search, Kaggle, and data.gov offer a variety of datasets covering different industries. While these sources may not be customized to a startup’s specific needs, they provide a useful baseline for early-stage model development. Partnering with Niche Platforms Unlike major social media platforms, smaller networks or industry-specific platforms may be open to data-sharing partnerships. Collaborating with influencers, content creators, or private communities could provide a steady stream of valuable data while maintaining ethical compliance. Crowdsourcing Through Paid Participation Platforms like Amazon Mechanical Turk (MTurk), Prolific, and Appen allow startups to collect structured data by compensating participants for completing tasks related to the dataset. While this method requires an initial investment, it offers greater control over data quality and diversity. Synthetic Data Generation Recent advancements in AI-generated synthetic data provide another potential solution. Using generative adversarial networks (GANs) or data augmentation techniques, startups can create artificial datasets that mimic real-world interactions. While this approach is not a direct substitute for real user data, it can help enhance model robustness in the absence of large-scale datasets. Balancing Data Collection, Compliance, and Model Accuracy For any AI-driven startup, the key challenge is balancing data accessibility, legal compliance, and model effectiveness. While scraping public data might seem like the easiest path, the risks of TOS violations and ethical concerns make it an unsustainable long-term strategy. Instead, companies must focus on creative, user-centric, and legally sound approaches to data acquisition. By leveraging a mix of user-generated data, partnerships, public datasets, and synthetic data, ML startups can navigate the data paradox and lay the foundation for scalable, compliant, and high-performing AI models. Final Thought The ML-startup paradox presents a fundamental challenge in AI development, but with innovative data collection strategies and user-driven contributions, it is possible to overcome these barriers while maintaining ethical standards. As the AI landscape continues to evolve, companies that prioritize transparency, user trust, and regulatory compliance will be better positioned for long-term success in the competitive world of machine learning startups.

Artificial Intelligence is evolving at an unprecedented pace, driving innovation across industries. However, as AI adoption becomes widespread, a new phenomenon has emerged: AI Inflation. This refers not only to the rising costs of AI infrastructure but also to the trend of businesses integrating AI into their systems as a “nice-to-have” feature rather than a necessity. As a result, AI is often misapplied, leading to wasted investments, inefficiencies, and diminishing returns. But what exactly is AI inflation? How is it affecting the industry? And what can be done to ensure that AI remains a tool for value rather than a corporate gimmick? Understanding AI Inflation: When AI Becomes Overused   AI Inflation occurs when companies integrate AI into their systems without a clear need or strategic purpose, leading to an over-saturation of AI-powered solutions with little added value. Unlike traditional inflation, which affects consumer prices, AI inflation is driven by over-investment in AI tools, unnecessary AI-based features, and increased costs without tangible returns. Several key factors contribute to AI inflation: Hype-Driven Adoption: Many businesses feel pressured to adopt AI simply because competitors are doing so, rather than identifying a real use case. Overcomplicated Solutions: Companies sometimes replace simple, effective workflows with AI-powered alternatives that add complexity instead of efficiency. Rising AI Development Costs: The cost of acquiring AI talent, computing resources, and large datasets has increased dramatically. Underutilized AI Features: Many AI tools are deployed in products or services where they offer little actual value, leading to inflated operational costs. Marketing Over Substance: Some companies market their AI capabilities more than they optimize their performance, leading to superficial integrations rather than meaningful innovations AI inflation is particularly dangerous for startups and smaller businesses that invest heavily in AI without a clear strategy, often leading to financial strain and unsustainable business models. The Reality of AI Inflation: How It Affects Businesses The consequences of AI inflation are becoming increasingly visible across industries. Companies often integrate AI into their products for the sake of being perceived as “innovative” without ensuring that it genuinely enhances efficiency or user experience. Key Industry-Wide Effects of AI Inflation: Increased Costs with Limited ROI: Businesses invest in AI-driven features that fail to generate significant revenue or cost savings. Customer Confusion and Frustration: AI-powered chatbots and automation tools, when poorly implemented, can degrade user experience rather than enhance it. Market Saturation of AI-Driven Solutions: The oversupply of AI-powered apps, tools, and platforms leads to redundancy, making it harder for truly valuable AI innovations to stand out. Diminishing Trust in AI Products: As more businesses integrate AI superficially, customers may become skeptical of AI-powered solutions, seeing them as unnecessary rather than beneficial. A prime example of AI inflation is the increasing use of AI-powered virtual assistants across various industries. While AI chatbots can be useful, many businesses integrate them into customer service without considering whether human support would be more effective, leading to frustrating and inefficient interactions. Case Study: The Overuse of AI in E-Commerce and SaaS Platforms One of the most striking examples of AI inflation can be seen in e-commerce and software-as-a-service (SaaS) platforms. Many companies integrate AI-driven recommendation engines, automated chatbots, and machine-learning analytics without assessing whether these tools significantly improve sales or customer experience. Key Issues in AI-Driven E-Commerce & SaaS: Overcomplicated Recommendation Engines: AI-powered product recommendations often add little value if they are poorly optimized, leading to higher operational costs without significantly increasing sales. AI Chatbots Replacing Human Support Prematurely: Many companies introduce AI chatbots that fail to resolve customer issues efficiently, frustrating users and damaging brand loyalty. Data-Heavy AI Tools That Slow Down Platforms: Some AI analytics tools process enormous amounts of user data but fail to provide actionable insights, making them an unnecessary expense. Subscription Costs for Unused AI Features: Businesses pay for AI-powered SaaS solutions that offer advanced capabilities, but many of these features remain underutilized. Companies that introduce AI without aligning it with actual user needs often find themselves spending more money on AI than they gain in efficiency or revenue. Recommendations for Startups and Businesses: Smart AI Integration To avoid falling into the trap of AI inflation, companies need to take a more strategic and disciplined approach to AI adoption. Here are some key recommendations for startups and businesses looking to integrate AI meaningfully: Assess the Real Need for AI – Before implementing AI, companies should identify whether automation or machine learning actually improves efficiency, customer experience, or revenue. Prioritize Cost-Efficient AI Solutions – Instead of investing in expensive, complex AI systems, startups should explore leaner AI models and pre-built AI tools that offer cost-effective solutions. Invest in AI Only When It Adds Value – Businesses should measure AI’s effectiveness through clear performance metrics, ensuring that AI features contribute to real business improvements. Optimize Before Scaling AI – Instead of integrating AI across an entire platform or service, businesses should test AI on a small scale and analyze the impact before making large investments. Be Transparent About AI Use – Companies should avoid over-marketing AI capabilities and instead focus on genuine improvements that enhance user experience. Balance AI with Human Input – In industries like customer service and healthcare, AI should complement human expertise rather than replace human workers prematurely. The Future of AI: A More Sustainable Approach As AI inflation continues to reshape the industry, businesses must move away from the mindset of integrating AI for the sake of appearing advanced and focus instead on strategic, meaningful AI implementations. The future of AI lies in practical, problem-solving applications rather than hype-driven integrations. Companies that prioritize efficiency, cost-effectiveness, and user experience will emerge as leaders in the AI-driven economy, while those that engage in AI inflation risk diminishing their credibility and financial stability. AI can be a transformative force—but only when it is used with purpose, precision, and practicality. Instead of treating AI as a must-have feature, businesses must ask: Is AI truly necessary here? If the answer is no, it may be better to hold back and invest in AI where it genuinely makes a difference.

The AI landscape is rapidly evolving, and the emergence of DeepSeek, a new artificial intelligence model from China, is directly challenging ChatGPT’s dominance. AI is transforming industries, and comparing these two models provides a glimpse into where the next wave of AI innovation is headed. While OpenAI’s ChatGPT has been a leading name in conversational AI, DeepSeek offers a cost-effective alternative that is gaining traction. But how do they really compare? And what does their competition mean for the future of AI? China vs. USA: The Battle for AI Supremacy The USA and China are leading the AI race, each advancing the field in different ways. The United States, home to industry giants like OpenAI, Google, and Microsoft, has focused on scalability, ethical AI, and commercial applications. OpenAI’s ChatGPT exemplifies this approach, delivering sophisticated natural language processing capabilities and widespread adoption across industries. China, however, has taken a different path, investing heavily in AI to promote cost efficiency, mass adoption, and technological self-sufficiency. DeepSeek is a prime example of this strategy, demonstrating how China is developing powerful AI models at a lower cost, making advanced AI more accessible while reducing reliance on Western technology. Government-backed AI research and a focus on homegrown innovation keep China in direct competition with the U.S., ensuring it remains a major player in the field. While the U.S. maintains an edge due to its advanced semiconductor technology and cloud computing resources, China is rapidly developing alternatives to bypass restrictions on AI training hardware and infrastructure. This competition is shaping the global AI landscape, influencing technological advancements, policies, and regulations worldwide. DeepSeek and ChatGPT: A Tale of Two AI Models ChatGPT, developed by OpenAI, has set the benchmark for conversational AI. Its advanced reasoning, contextual awareness, and content generation abilities have made it indispensable across many industries. However, the model’s high computational costs remain a significant drawback, limiting AI accessibility for smaller businesses and developers. DeepSeek, on the other hand, offers a cost-effective and performance-driven alternative. Developed by Hangzhou DeepSeek Artificial Intelligence Basic Technology Research Co., Ltd., it uses a "Mixture of Experts" model, which activates only the necessary computing resources for a given task. While ChatGPT relies on high-compute resources, DeepSeek achieves similar efficiency with much lower costs. Face-Off: DeepSeek vs. ChatGPT A direct comparison of DeepSeek and ChatGPT highlights their respective strengths and weaknesses: Feature   DeepSeek   ChatGPT   Developer   DeepSeek AI (China)  OpenAI (USA)  Launch Year   2023  2022  Model Type   Mixture of Experts  Transformer-based LLM  Computational Efficiency   Highly cost-efficient  High resource usage  Training Cost   ~$6 million (optimized)  Substantially higher  Open-Source   Yes  No (Proprietary)  Primary Strengths   Cost-effective, strong in technical tasks  Advanced reasoning, broad adaptability  Weaknesses   Limited political discourse, early-stage development  Expensive to operate, slower in processing complex queries Best For   Coding, mathematics, structured responses  General AI assistant, creative content generation, business applications  This comparison shows that DeepSeek is designed to be more efficient and affordable, whereas ChatGPT remains a highly capable and adaptable generalist AI with superior creative and analytical skills. AI Market Disruption: The Impact of DeepSeek DeepSeek’s arrival has disrupted the AI market, challenging conventional assumptions about cost and performance. While AI companies have traditionally focused on scalability and accuracy, DeepSeek introduces a new priority: efficiency and accessibility. The launch of DeepSeek had an immediate impact. Nvidia’s stock saw a decline in valuation, a sign of how emerging AI models can shift the industry landscape. DeepSeek’s cost-effective approach demonstrates that AI training and inference don’t have to be prohibitively expensive, making AI more accessible to businesses worldwide. Meanwhile, ChatGPT continues to lead the global AI space, powering businesses, customer service platforms, and creative applications. Its vast dataset and extensive training give it exceptional contextual understanding, making it the preferred choice for high-quality natural language generation and advanced reasoning. However, with DeepSeek proving that high-performance AI can be achieved at lower costs, the industry may be entering a new phase—one where AI development is focused on efficiency rather than sheer computational power. The Future of AI: Key Trends and Takeaways The rivalry between DeepSeek and ChatGPT reflects a broader trend in AI: the shift towards optimization and accessibility. As the AI race progresses, several key trends are becoming clear: Efficiency Will Define the Next AI Leaders – While ChatGPT set the standard for conversational AI, DeepSeek’s success suggests that future AI models must balance performance with affordability. Open-Source Models Will Gain Influence – DeepSeek’s open-source nature allows developers more flexibility and control, posing a potential challenge to proprietary AI like ChatGPT. AI Will Become More Widely Available – As cost-efficient models emerge, smaller businesses and independent developers will gain access to high-performing AI, leading to wider adoption and innovation. Ultimately, the competition between DeepSeek and ChatGPT signals a shift in AI development—from raw power to intelligent efficiency. While it remains to be seen whether DeepSeek can surpass ChatGPT, one thing is certain: the AI industry is entering a new era of cost-effective, high-performance solutions that will shape the future of artificial intelligence.

🐉 As we bid farewell to the Dragon Year, let's reflect on the Highlights of Vietnam's Tech Startup Ecosystem in 2024  🐉  📊 What shaped Vietnam's tech startup ecosystem in 2024? What are the standout highlights, key stats, and emerging trends? Watch the video to explore!  📘 Get deeper insights into Vietnam’s venture capital landscape here: https://xzztlrf6p7q.typeform.com/to/TEMutqq6   📄 Startups, ready to shine? Apply here: https://xzztlrf6p7q.typeform.com/to/kVPON4nj   Wishing you a prosperous and innovative year ahead! 🎉 Happy Lunar New Year – The Year of the Snake!  🐍  #VinVentures #VietnamTechStartupEcosystem2024

In the fast-evolving world of B2B SaaS, countless discussions on platforms like Reddit often spark insightful debates and ideas. This article draws inspiration from a compelling Reddit thread that highlighted the nuances and challenges of building robust B2B SaaS platforms. While AI has revolutionized many aspects of software development, experienced professionals remain indispensable for navigating complexities such as multi-tenancy, scalability, and security. Below, we delve into the key elements, potential pitfalls, and infrastructure essentials for crafting a successful B2B SaaS solution. Key Elements of a Robust B2B SaaS Platform A strong B2B SaaS platform is underpinned by several critical elements, summarized in the table below: Category Key Requirements Multi-Tenancy Framework Tenant isolation, flexible deployment models, tenant-aware operations Identity and Security Advanced authentication (SSO), RBAC, comprehensive audit trails Tenant Management Self-service onboarding, automated provisioning, tenant-specific configurations Operational Excellence Zero-downtime deployments, tenant-isolated debugging, tier-based quotas, backups Scalability Independent scaling, resource isolation, tier-based SLAs, dynamic resource allocation Multi-Tenancy Framework Effective multi-tenancy ensures that the platform remains secure and scalable. Tenant isolation across data, compute, and networking layers is critical. Additionally, flexible deployment models—pooled or siloed—allow customization for different customer tiers. Tenant-aware operations provide clear insights while maintaining isolation. Identity and Security Enterprise-grade authentication, such as SSO, and dynamic Role-Based Access Control (RBAC) are essential for secure access. Comprehensive audit trails ensure compliance and transparency, especially in regulated industries. Tenant Management Simplify operations with self-service onboarding, automated provisioning, and customizable tenant settings. Deliver actionable insights via cross-tenant analytics without compromising data privacy. Operational Excellence Ensure uninterrupted service through zero-downtime deployments and tenant-isolated debugging. Resource quotas, tier-based throttling, and automated disaster recovery further enhance reliability. Scalability A scalable platform adapts to changing demands. Independent scaling of workloads, mitigation of noisy neighbor issues, and dynamic resource allocation ensure robust performance for all tenants. Pitfalls to Avoid in B2B SaaS Development Single-Tenant Database Design:   Avoid rigid database designs that hinder scalability. Hard-Coded Configurations:   Use dynamic configurations for flexibility. Insufficient Tenant Isolation:   Ensure shared services do not compromise tenant security or performance. Lack of Context in Monitoring:   Implement tenant-aware logging and analytics for effective troubleshooting. Overlooking Cost Allocation:   Establish clear tenant-aware cost accounting to manage profitability. Infrastructure Essentials Robust infrastructure complements a strong software foundation. The table below outlines essential infrastructure considerations: Aspect Details Routing Use tenant-aware API gateways to direct traffic. Code Isolation Implement isolation at critical code paths when necessary. Data Storage Employ proper partitioning strategies for secure and scalable storage. Service Allocation Balance shared and dedicated services to optimize resource usage. Conclusion Creating a successful B2B SaaS platform requires aligning architecture with business objectives. Security, scalability, and observability must be part of the foundation, not added later. AI can streamline certain processes, but it cannot replace the insights of skilled architects and engineers. Investing in a capable team with expertise in multi-tenancy, security, and scalability will ensure your platform is robust and future-proof. A well-designed foundation distinguishes scalable and reliable solutions from those that falter under pressure.

Europe’s ambitious plan to ban petrol cars by 2035 is facing serious challenges. Swedish battery maker Northvolt’s collapse, once heralded as a pillar for local EV production, has exposed deeper issues in the continent’s push toward clean mobility. Without affordable EV solutions and a self-reliant production ecosystem, Europe’s goal appears increasingly out of reach. Carmakers, including BMW, Volkswagen, and Renault, have urged the European Union to reconsider or delay its targets, arguing that the infrastructure and market conditions needed to support such a transition are insufficient. China vs. Europe: A Growing Gap While Europe grapples with its challenges, China has emerged as a dominant player in the global EV race. China’s Strengths Europe’s Challenges Affordable EVs like the $10,000 BYD Seagull High-cost models averaging €52,700 in Germany Strategic government subsidies since 2002 Limited incentives and withdrawal of subsidies Adoption of LFP batteries (safer and cheaper) Reliance on costlier lithium nickel manganese cobalt batteries Focused solely on electric mobility Burdened by combustion engine legacy China’s government has long prioritized electric mobility, investing heavily in EV technology and securing access to critical raw materials for battery production. This foresight has allowed Chinese manufacturers to dominate the affordable EV segment, leaving European companies struggling to compete. Economic and Political Risks The stakes for Europe’s car industry are enormous. Employing 13.8 million people and accounting for 7% of the continent’s GDP, the industry plays a critical economic role. Yet, declining car sales have already prompted Volkswagen to announce the closure of multiple factories. Such developments have sparked fears of economic instability, particularly in Germany, a hub of European car manufacturing. Politically, these economic pressures are fueling opposition to environmental policies. Far-right political parties are using the fallout to challenge the 2035 ban, framing green initiatives as threats to jobs and growth. Germany’s push for e-fuels—synthetic fuels touted as a cleaner alternative to petrol—has added further complexity to the debate. Critics argue that e-fuels are inefficient, expensive, and divert attention from the more viable path of EV adoption. Barriers to EV Adoption Consumers remain wary of electric vehicles for several reasons: 1.         High Costs : EVs are perceived as unaffordable for many households. 2.         Safety Concerns : Myths surrounding battery safety persist. 3.         Infrastructure Gaps : Limited charging networks and slow deployment of renewable energy discourage adoption. These challenges have been exacerbated by the withdrawal of government subsidies, leading to a 1.7% drop in EV sales in Europe in 2024. Policymakers and manufacturers must work together to address these pain points, including expanding charging infrastructure and lowering costs through discounts and rebates. Proposed Solutions and Optimism Despite the obstacles, industry experts remain optimistic about Europe’s ability to meet its 2035 target. Carmakers like Volkswagen are developing affordable models, such as the ID.1 and ID.2, which could make EV ownership accessible to a wider audience. However, any delay in the deadline risks undermining public confidence in the transition to electric mobility. Volkswagen’s efforts exemplify the kind of innovation needed to revitalize the market, but broader collaboration is essential. Governments must provide robust infrastructure support, while manufacturers need to focus on affordability without compromising profitability. Comparisons to the global adoption of smartphones highlight the inevitability of this shift—better technologies ultimately win. Conclusion Europe’s journey toward electric mobility is fraught with challenges, but the potential rewards are immense. A successful transition will require a multi-faceted approach: addressing consumer concerns, building charging networks, and fostering collaboration between policymakers and manufacturers. With strategic investments and a clear focus, Europe can reclaim its position as a leader in the global EV revolution, paving the way for a sustainable and innovative future.   Source: https://www.wired.com/story/europe-wanted-to-lead-the-world-on-evs-carmakers-cant-keep-up/

NVIDIA, a global leader in AI computing, has shown significant interest in Vietnam, reflecting the country’s economic potential and proactive technology development strategy. Alongside other tech giants expanding into Southeast Asia, NVIDIA's initiatives mark a transformative shift for Vietnam’s tech sector. NVIDIA’s Commitment to Vietnam’s AI Ecosystem On December 5, 2024, NVIDIA signed a Memorandum of Understanding (MOU) with the Ministry of Planning and Investment to establish two major AI centers: the Vietnam Research and Development Center (VRDC) and an AI Data Center. This partnership aims to drive technological breakthroughs, enhance infrastructure, and develop Vietnam’s AI talent pool. NVIDIA’s commitment was further demonstrated by the acquisition of VinBrain, a Vingroup subsidiary specializing in AI-integrated medical solutions. VinBrain’s DrAid, an AI-powered diagnostic tool, is already in use in major hospitals domestically and internationally. This acquisition highlights the integration of AI in Vietnam’s healthcare sector. In April 2024, NVIDIA also partnered with FPT Corporation to create a $200 million AI factory in Hanoi, focusing on AI-driven solutions and education programs for 30,000 students. The first AI factory began operations in November 2024, with profitability expected by 2025. NVIDIA’s collaboration with GreenNode, a subsidiary of VNG Corporation, is another milestone. GreenNode offers AI and GPU services using NVIDIA’s technology, enhancing Vietnam’s AI infrastructure through its data hub in the Tan Thuan Export Processing Zone. Vietnam’s Strategic Role in the Global Tech Landscape Vietnam’s AI market is projected to reach $753 million in 2024, with a CAGR of 28.36% through 2030. This growth aligns with the regional pace, demonstrating Vietnam’s ability to compete on a global scale. NVIDIA’s investments play a pivotal role in Vietnam’s transition from low-tier manufacturing to a hub for high-tech innovation. Several factors contribute to this transformation: Strategic Geopolitical Advantage : Trade tensions and tariffs during the Trump administration accelerated the shift of tech manufacturing from China to Vietnam. This trend continues, positioning Vietnam as a key player in global supply chain realignment. Increasing Foreign Direct Investment (FDI) : Vietnam is attracting significant investments from major technology firms like NVIDIA, Microsoft, Google, AWS, and OpenAI. These investments bolster Vietnam’s infrastructure in AI, cloud computing, and data centers. Young, Skilled Workforce : Vietnam boasts over 5,000 engineers and 7,000 AI experts, supported by a literacy rate of 96.6%. The country’s average wage remains competitive, making it an attractive destination for high-tech manufacturing and innovation. Government Support : Proactive policies, strategic trade agreements, and initiatives promoting innovation-friendly environments create fertile ground for AI and tech investments. NVIDIA’s investments in Vietnam—through AI research hubs, strategic partnerships, and acquisitions—are pivotal in transforming the country into a regional AI innovation hub. Combined with Vietnam’s youthful workforce, thriving startup ecosystem, and supportive government policies, these developments offer a solid foundation for long-term technological growth. As Vietnam continues to evolve, the convergence of foreign investment, strategic geographical positioning, and technological talent sets the stage for a promising future in the global AI and high-tech sectors. Source: https://www.vietnam-briefing.com/news/nvidia-expansion-into-vietnam-potential-for-ai-sector-growth.html/

Southeast Asia has become a focal point for global tech giants like Nvidia and Microsoft, who are investing heavily in cloud services and data centers. These investments, projected to reach $60 billion over the next few years, are fueled by the region's young and tech-savvy population embracing trends like video streaming, e-commerce, and generative AI. However, the region’s own AI startups have not been able to capitalize on this momentum. Skepticism about the scalability and innovation potential of local startups has led to cautious investment, leaving many of these firms struggling to secure funding. The Funding Gap in Numbers Despite its promise, Southeast Asia’s AI startups secured only $1.7 billion in funding in 2024, a small portion of the $20 billion invested in AI across the Asia-Pacific region. Moreover, only 122 funding deals were recorded in the region compared to 1,845 deals across APAC. This funding disparity highlights the difficulties Southeast Asia faces in competing with the US and China, the world’s AI powerhouses, which attracted $68.5 billion and $11 billion in AI investments, respectively. Source: Preqin. Note: as of Dec 4, 2024 The Potential vs. Reality At first glance, Southeast Asia appears well-positioned to thrive in the AI landscape. With over 2,000 AI startups, the region outnumbers South Korea and comes close to Japan and Germany in terms of entrepreneurial activity. Singapore stands out, ranking third in the Global AI Index, thanks to its concentration of AI talent and robust infrastructure. However, the broader region—including nations like Indonesia, the Philippines, Thailand, and Malaysia—faces unique challenges. Differences in culture, language, and infrastructure create barriers to developing unified datasets, which are essential for scalable AI solutions.   Barriers to Scale and Growth The challenges facing Southeast Asia’s AI sector are not limited to cultural and linguistic diversity. The region’s startups also lack access to foundational AI technologies and large-scale software engineering capabilities. Unlike Silicon Valley or China, Southeast Asia does not yet have the infrastructure to support the development and deployment of cutting-edge AI systems at scale. The venture capital ecosystem is further hindered by limited exit opportunities, such as IPOs, which are exacerbated by underperforming public markets. Research from Google, Temasek, and Bain & Co. indicates that private funding for Southeast Asian startups has dropped to its lowest levels in years. This decline reflects a broader hesitancy among investors who view the region as lacking the profitability and scalability seen in more established markets. Source: Google, Temasek, Bain e-Conomy report 2024. Government Efforts and Regional Collaboration Despite these challenges, governments in Southeast Asia are actively working to foster AI innovation. Countries like Singapore have established national AI frameworks and provided funding to startups through government-backed investment programs. However, regional collaboration remains a significant hurdle. Nations in Southeast Asia often prioritize vastly different agendas—some focusing on high-tech development, others addressing basic infrastructure needs. This divergence makes it difficult to create a cohesive plan for AI-driven growth across the region. Experts emphasize the need for coordinated efforts among governments to prioritize “moonshot” innovations that could transform Southeast Asia into a global AI hub. Without such alignment, the region risks missing out on opportunities to leverage its growing digital economy.   Opportunities in Data-Driven AI Southeast Asia’s competitive advantage may lie in early-stage AI opportunities, particularly in data collection and organization. Building high-quality datasets can provide a foundation for creating scalable AI solutions. Singapore-based Patsnap exemplifies this approach. Over 17 years, the company has developed vast datasets covering patents, chemicals, and food industries, which now serve as the backbone of its sector-specific AI models. Similarly, Indonesia’s Alpha JWC is fostering AI innovation through programs that connect startups with large corporations. These initiatives aim to bridge the gap between emerging talent and real-world applications, offering a blueprint for sustainable growth in the region.   The Role of Digital Economy and Geopolitics While its AI sector faces hurdles, Southeast Asia’s digital economy is growing at double-digit rates, driven by a rising middle class, increasing mobile and internet penetration, and a youthful, tech-savvy population. Moreover, the region is relatively insulated from geopolitical tensions between the US and China, making it an attractive destination for foreign investors. This growth offers a strong foundation for the development of AI applications in e-commerce, fintech, and digital infrastructure. However, capitalizing on this momentum requires a cohesive ecosystem where governments, regulators, investors, and startups work in synergy.   The Path Forward Southeast Asia holds immense potential to become a global player in AI, but significant challenges remain. The region must address gaps in funding, infrastructure, and collaboration to unlock its full potential. By focusing on early-stage opportunities like data-driven innovation and fostering regional cooperation, Southeast Asia can position itself as a key player in the global AI landscape. The road ahead requires a shared vision and collective effort from all stakeholders—governments, investors, startups, and corporations alike. With the right strategies in place, Southeast Asia can ride the AI wave and solidify its position in the global tech ecosystem.   Source: https://www.bloomberg.com/news/articles/2024-12-19/southeast-asia-startups-miss-out-on-region-s-ai-fueled-tech-boom

OpenAI is exploring a groundbreaking method to enhance AI models by having AI assist human trainers. This builds on the success of  reinforcement learning from human feedback (RLHF) , the technique that made ChatGPT reliable and effective. By introducing AI into the feedback loop, OpenAI aims to further improve the intelligence and reliability of its models.   The Success and Limits of RLHF RLHF relies on human trainers who rate AI outputs to fine-tune models, ensuring responses are coherent, accurate, and less objectionable. This technique played a key role in ChatGPT’s success. However, RLHF has notable limitations: •   Inconsistency : Human feedback can vary greatly. •   Complexity : It’s challenging for even skilled trainers to assess intricate outputs, like complex code. •   Surface-Level Optimization : Sometimes, RLHF leads AI to produce outputs that seem convincing but aren’t accurate. These issues highlight the need for more sophisticated methods to support human trainers and reduce errors.   Introducing CriticGPT To overcome RLHF’s limitations, OpenAI developed  CriticGPT , a fine-tuned version of GPT-4 designed to assist trainers in evaluating code. In trials, CriticGPT: •   Caught Bugs  that human trainers missed. •   Provided Better Feedback : Human judges preferred CriticGPT’s critiques over human-only feedback  63% of the time . Although CriticGPT is not flawless and can still produce errors or "hallucinations," it helps make the training process more consistent and accurate. OpenAI plans to expand this technique beyond coding to other fields, improving the overall quality of AI outputs.   The Potential Impact By integrating AI assistance into RLHF, OpenAI aims to: •   Enhance Training Efficiency : AI-supported feedback reduces inconsistencies and human errors. •   Develop Smarter Models : This technique could allow humans to train AI models that surpass their own capabilities. •   Ensure Reliability : As AI models grow more powerful, maintaining accuracy and alignment with human values becomes crucial. Nat McAleese , an OpenAI researcher, emphasizes that AI assistance may be essential as models continue to improve, stating that "people will need more help" in the training process.   Industry Trends and Ethical Considerations OpenAI’s approach aligns with broader trends in AI development. Competitors like  Anthropic  are also refining their training techniques to improve AI capabilities and ensure ethical behavior. Both companies are working to make AI more transparent and trustworthy, aiming to avoid issues like deception or misinformation. By using AI to train AI, OpenAI hopes to create models that are not only more powerful but also more aligned with human values. This strategy could help mitigate risks associated with advanced AI, ensuring that future models remain reliable and beneficial. Source:   https://www.wired.com/story/openai-rlhf-ai-training/

As 2025 unfolds, the business landscape is undergoing transformative change. From extreme weather events disrupting industries to geopolitical shifts reshaping global dynamics, the world is in flux. Amid these challenges, one force stands out as the most significant driver of disruption and opportunity: Artificial Intelligence (AI). At the recent WIRED World in 2025 event, Azeem Azhar, founder of Exponential View , outlined how generative AI (gen AI) is set to redefine business operations. He likened its impact to groundbreaking technologies like the steam engine and electricity, which fundamentally reshaped economies. “Generative AI is a cognitive steam engine,” Azhar remarked, emphasizing its potential to increase productivity, spur growth, and create prosperity. To capitalize on this transformative power, business leaders must act decisively. Here’s a guide to navigating the opportunities and challenges of AI in 2025: 1. Embrace AI Now—Don’t Wait The rapid advancement of generative AI over the past two years has ushered in unprecedented capabilities. This constant acceleration can make it difficult for businesses to decide when and how to invest. However, waiting too long risks falling behind. Paul Michelman of Boston Consulting Group (BCG) highlighted the importance of proactive adoption. “Do you want to be a passenger or a driver?” he asked. To stay ahead, businesses must experiment with AI tools and integrate them into their operations. BCG’s AI agent, GENE, is a prime example, assisting with tasks like content creation and even facilitating sensitive organizational conversations. 2. Prepare for Two Key AI Shifts Azhar identified two major developments shaping AI in 2025: Mainstream Adoption of AI Tools: Generative AI products are reaching a level of maturity that makes them accessible and effective for widespread business use. Emergence of Autonomous AI Agents: AI systems are evolving to take autonomous actions, powered by reasoning models capable of breaking down complex tasks into logical steps. These agents could soon rival the expertise of top engineers, offering unparalleled support for technical and operational challenges. 3. Prioritize Strategic Problem Selection For businesses looking to leverage generative AI, problem selection is critical. Dorothy Chou of Google DeepMind stressed the importance of starting with areas where data quality is robust. High-quality datasets lead to better outcomes when grounding AI tools in an organization’s needs. Chou also encouraged businesses to tackle ambitious problems in high-impact areas like life sciences, energy, and education, despite the challenges of navigating regulated industries. While these sectors are harder to penetrate, they offer immense societal and commercial value. 4. Address the Scaling Challenge While some worry about a potential “scaling wall” in AI development—where advances in large language models hit diminishing returns—Azhar dismissed these concerns. The true limitation, he argued, lies in infrastructure. The demand for larger data centers to support AI growth is expected to be met as industrial necessity drives investment. 5. Stand Out in a World of AI-Generated Content Content creation is one of the most prominent use cases for generative AI. However, as more businesses rely on similar AI tools, the risk of producing generic outputs increases. To maintain originality, brands must combine AI-driven efficiency with human creativity. “Harness tech-driven efficiency, while ensuring human ingenuity shines through,” advised GENE, BCG’s conversational AI agent. Looking Ahead AI is no longer a distant promise—it’s here, and it’s reshaping industries at an unprecedented pace. For business leaders, 2025 offers an opportunity to redefine their organizations by embracing AI tools, selecting meaningful problems, and maintaining a balance between technology and human innovation. The question remains: Will you be a passenger or a driver in this era of transformation? SOURCE: https://www.wired.com/sponsored/story/how-business-leaders-should-navigate-ai-in-2025/

We are thrilled to present the "Vietnam Tech Startup Ecosystem 2024" , a must-read report for anyone looking to stay ahead in the ever-evolving tech ecosystem. This comprehensive analysis focuses on deal activity and the investment landscape , shedding light on: Key trends driving Vietnam’s position as a leader in Southeast Asia’s tech investment growth.. Insights into deal activity over the last year, from deal sizes to notable transactions Emerging sectors beyond FinTech and Consumer Tech, such as Agritech and Foodtech, paving the way for new opportunities. FILL IN THE FORM TO GET THE REPORT