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/

As the prospect of Artificial General Intelligence (AGI)—AI capable of surpassing human performance across most tasks—looms closer, Donald Trump’s presidency marks the beginning of a transformative era. Yet, his early comments on AI reflect a mix of enthusiasm and confusion, leaving his strategic direction unclear.   In a podcast interview with YouTube influencer Logan Paul, Trump referred to superintelligence as “super-duper AI,” revealing a limited grasp of the technology. While he voiced alarm over the dangers of deep fakes, calling them “scary” and “alarming,” he was equally captivated by large language models capable of drafting impressive speech scripts. Praising their speed and output, Trump joked that AI might one day replace his speechwriter.   Trump and Logan Paul on the Impaulsive podcast. Source: YouTube   These remarks illustrate Trump’s dual perspective: a fascination with AI’s transformative potential paired with a lack of nuanced understanding of its risks.   Silicon Valley and the Battle Over AI Regulation   The tech industry is deeply divided over the future of AI development, with two dominant camps shaping the conversation. On one side are “accelerationists” (or “e/accs”), who oppose regulation and advocate for unbridled technological advancement. On the other side are proponents of “AI alignment,” who focus on ensuring AI systems adhere to ethical standards and human values to mitigate risks.   Accelerationists often dismiss safety advocates as “decelerationists” or “doomers,” while alignment proponents warn of catastrophic outcomes if AI development proceeds recklessly. Within this polarized landscape, Trump’s administration is expected to favor accelerationist ideals, minimizing regulation to promote rapid innovation.   Prominent accelerationist figures have celebrated Trump’s election as a win for their cause. @bayeslord, a leader in the movement, declared on X: “We may actually be on the threshold of the greatest period of technological acceleration in history, with nothing in sight that can hold us back, and clear open roads ahead.”   However, this accelerationist optimism clashes with concerns from AI safety advocates, who argue that unchecked development could amplify societal risks, from biased algorithms to existential threats.   Policy Implications: AI Regulation and the CHIPS Act   Trump’s approach to AI regulation is expected to be shaped by his broader anti-regulation stance. He has already signaled plans to rescind President Joe Biden’s 2023 executive order on AI, which aimed to address risks such as discrimination in hiring and decision-making processes. Republicans have criticized these measures as excessively “woke,” with Dean Ball of the Mercatus Center noting that they “gave people the ick.”   Additionally, Trump’s administration may target the US AI Safety Institute, an initiative launched to ensure the safe development of AI technologies. Led by alignment advocate Paul Christiano, the institute represents a focal point for the Biden-era regulatory framework that Trump is likely to dismantle or reshape.   On the semiconductor front, Trump has criticized the CHIPS and Science Act, which was designed to bolster US semiconductor manufacturing—a critical component of advanced AI systems. However, there is bipartisan hope that his opposition is mostly rhetorical. Maintaining leadership over China in AI development is likely to influence Trump’s eventual support for policies that strengthen the semiconductor supply chain. During his podcast with Logan Paul, Trump underscored the importance of AI leadership, stating, “We have to be at the forefront. We have to take the lead over China.”   AI Safety and Republican Perspectives   Despite expectations of a deregulation-focused agenda, AI safety advocates believe Trump’s administration could be more open to their concerns than accelerationists assume. Sneha Revanur, founder of Encode Justice, points out that partisan lines on AI policy are not clearly defined, leaving room for nuanced discussions about risk mitigation.   Surprisingly, elements within Trump’s orbit have already engaged with safety-focused perspectives. In September, Ivanka Trump posted about “Situational Awareness,” a manifesto by former OpenAI researcher Leopold Aschenbrenner that warns of AGI triggering a global conflict with China. The post sparked widespread discussion, with some speculating about Ivanka’s potential influence on Trump’s policy decisions.   Other Republicans have raised concerns about AI’s societal impact. Senator Josh Hawley has criticized lax safety measures at AI companies, while Senator Ted Cruz proposed legislation to ban AI-generated revenge porn. Vice President-elect JD Vance has pointed to left-wing bias in AI systems as a significant issue.   These concerns suggest that the GOP’s stance on AI may extend beyond accelerationism to include targeted measures addressing specific risks.   The Role of Elon Musk: Ally or Critic?   One of the most influential voices in the AI debate is Elon Musk, whose views on regulation add complexity to the discussion. While accelerationists hail Musk as a hero, his support for stronger oversight complicates this narrative. Musk has called for a regulatory body to monitor AI companies and supported California’s SB 1047, a rigorous AI regulation bill opposed by major tech firms.   Musk’s advocacy for regulation stems in part from his public fallout with OpenAI, which he co-founded but left in 2018. Since then, he has criticized the organization, launched lawsuits against it, and established his own rival company, X.ai Corp. This rivalry, combined with Musk’s evolving views on AI safety, makes him a wildcard in shaping Trump’s AI policies.   Navigating Contradictions: Innovation vs. Safety   Republicans face a significant challenge in reconciling their stance on AI development. While the party is critical of Silicon Valley and wary of empowering tech giants, it also recognizes the need to stay ahead of global competitors like China. This tension is likely to influence their policy decisions in the coming years.   According to Casey Mock, chief policy officer at the Center for Humane Technology, Republicans are more likely to focus on immediate, tangible issues. Concerns such as deepfake pornography and students using AI to cheat on homework are expected to dominate the agenda, while long-term risks like AGI misalignment may take a backseat.   This pragmatic approach aligns with the party’s broader emphasis on addressing “kitchen table” issues that resonate with everyday Americans.   Shaping the Future of AI   As the first president of the AGI era, Trump’s policies will have far-reaching implications for the future of AI development. His administration’s accelerationist leanings suggest a push for minimal regulation, but internal party concerns and pressure from safety advocates could lead to a more balanced approach.   The AGI era represents a transformative moment in human history. How Trump navigates this period will not only define his presidency but also shape the trajectory of AI’s integration into society. With immense opportunities and significant risks at stake, the world will be watching closely as this story unfolds.   SOURCE: https://www.bloomberg.com/news/articles/2024-11-15/trump-s-anti-regulation-pitch-is-what-the-ai-industry-wants-to-hear?srnd=phx-technology-startups&sref=Tk1DJfhB

The insights below are derived from PwC's Global NextGen Survey 2024, which explores the evolving perspectives and roles of next-generation leaders in family businesses. This international survey, conducted online, gathered reflections from 917 next-generation leaders across 63 territories, including 33 from Vietnam, between November 2023 and January 2024. It offers a unique look into how these leaders are adapting to an increasingly digital and AI-driven business environment. In the context of family businesses, the terms "Current Generation" and "Next Generation" (NextGen) often refer to different generational cohorts within the same family who may be at varying stages of involvement and leadership within the company. The Current Generation typically includes those who are currently in control of the business, often having built or significantly expanded it. They generally adhere to traditional business practices and may be more risk-averse. Embracing Leadership in the Digital Age Vietnamese NextGen leaders are significantly marking their presence in leadership roles within family businesses, with an impressive 52% now holding such positions, a substantial increase from 29% in 2022. This trend underscores a strong generational shift towards greater involvement. the workforce is equipped with the skills needed to handle new technologies. Source: PwC’s Global NextGen Survey 2024 Vietnam report Furthermore, 76% of NextGen leaders in Vietnam have a clear understanding of their personal ambitions and the career paths envisioned by the current generation. They are tackling the complex challenges faced by businesses and society with a strategic approach that integrates human insight with technological advancement. A significant focus among these leaders is enhancing technological infrastructure, evident in 36% prioritizing this area, alongside ensuring that 33% of the workforce is equipped with the skills needed to handle new technologies. Source: PwC’s Global NextGen Survey 2024 Vietnam report Delving into Generative AI (GenAI) and New Technologies A remarkable 82% of Vietnamese NextGen leaders show a deep interest in exploring Generative AI (GenAI), recognizing its potential to fundamentally transform business operations and customer experiences. This widespread interest highlights their awareness of GenAI's capacity to reshape the competitive landscape and foster innovation. Additionally, 67% view AI as a crucial opportunity for leadership in the ethical use of technology, illustrating their readiness to embrace responsible innovation. This perspective is shared by 58% who believe that leading AI initiatives will not only progress their businesses but also establish their personal reputations as visionary leaders.  Source: PwC’s Global NextGen Survey 2024 Vietnam report Despite the eagerness to adopt AI, 63% of family businesses in Vietnam are still in the early stages of this technological integration. However, positive signs are showing, with 27% experimenting with AI in pilot projects, and 9% having fully integrated AI solutions into their operations, signaling proactive steps towards embracing this advanced technology.  Enhancing NextGen's Impact in Family Enterprises Navigating the digital landscape presents significant challenges, particularly when it comes to aligning the strategies of current and upcoming generations within family businesses. NextGen leaders, keen on pushing forward with new technologies, often find themselves at odds with more traditionally inclined current leaders. This underscores the critical need for effective communication and collaboration to harmonize these differing perspectives and secure the business's future success. Moreover, building robust governance and establishing trust are top priorities for NextGen leaders, with a significant majority recognizing the importance of clear ethical guidelines for AI usage. Despite this awareness, only a fraction have put such governance structures into place, revealing a substantial gap between intent and execution. The survey further highlights the importance of involving NextGen leaders in low-risk, high-return AI projects. This strategic approach allows family businesses not only to stay competitive but also to lead the charge in technological advancements, capitalizing on the innovative mindset and technological savvy of the younger generation Read more at:  PwC’s NextGen Survey 2024 - Vietnam report | Succeeding in an AI-driven world

What has happened?  According to DealStreetAsia , CROWDE, an agri-focused P2P lending platform backed by Monk’s Hill Ventures and Mandiri Capital Indonesia, has been hit by a major governance crisis. Internal whistleblowers revealed that between 2021 and 2024, approximately IDR 800 billion (~$49 million) in loan funds was diverted into fake farming projects, rather than reaching smallholder farmers.   Only about IDR 500 billion ($30 million) of the IDR 1.3 trillion ($80 million) disbursed during that period went to legitimate agricultural activity. The rest allegedly passed through shell vendor companies linked to insiders. Company background  CROWDE was founded in 2016 by Yohanes Sugihtononugroho. It was initially hailed as a "fintech enabler for smallholder farmers," aiming to facilitate financial access for this underserved group. Its core business model was that of an Agri-focused Peer-to-Peer (P2P) lender.  Initially, CROWDE's business model involved connecting farmers with individual (retail) lenders. However, by the end of 2019, CROWDE had completely shifted its model to work exclusively with institutional lenders, primarily banks. Major institutional lenders included Bank Mandiri, Bank BJB, J Trust Bank, and some local BPRs (rural banks). CROWDE had also secured funding from major venture capital firms. Its last known funding round was a $9 million Series B in 2021, which was led by Monk’s Hill Ventures, Mandiri Capital Indonesia, Great Giant Foods, and Panasean Group. Previous investors included Strive (formerly Gree Ventures) and Crevisse. Notably, Bank Mandiri’s corporate venture capital arm, Mandiri Capital, was the third-largest shareholder in CROWDE.  How was CROWDE's fraud executed?  CROWDE's alleged fraud was a multi-faceted scheme that involved diverting loan capital through fake projects, shell vendor networks, and manipulated borrower data, exploiting vulnerabilities in the P2P lending model and the limited financial literacy of farmers. The fraud is be broken down as: Diversion of loan capital through fake projects and shell networks Out of 1.3 trillion rupiah (around $80.1 million) in total disbursed loans between 2021 and 2024, an estimated 800 billion rupiah ($49.3 million) were funneled through fictitious transactions, while only about 500 billion rupiah ($30.8 million) were linked to legitimate farming activity.  The alleged wrongdoings are believed to have originated during the COVID-19 pandemic, when CROWDE, under pressure for growth from investors, began executing what insiders called "suspicious projects" that deviated from its standard lending model. What started as a single pilot project escalated into over 10,000 such initiatives.    Under-disbursement to farmers and use of fake suppliers Farmers who applied for loans, some for as much as 45 million rupiah ($2,774), reportedly received only a fraction of their approved amounts, typically 5-10 million rupiah ($308-616) in actual cash.  The remaining funds were allegedly diverted through fake agricultural suppliers. These were limited partnerships (CVs) that posed as legitimate vendors but were allegedly controlled by CROWDE insiders. These fake entities then issued invoices for non-existent seeds, fertilizers, and farming equipment that never actually reached the farmers. Large-scale cash withdrawals and lack of documentation Bank statements from one of these fake CVs, seen by DealStreetAsia, showed CROWDE making daily transfers of around 40 million rupiah to the CV account. A few days later, over 1 trillion rupiah ($61.5 million) in cash was reportedly withdrawn via cheque from this CV account. This vast sum was then routed to another bank account as instructed by management.  Crucially, no documentation recorded this massive cash outflow, necessitating a forensic audit by the whistleblower to retrace the money flow. At least 30 such fake supplier CVs were identified among hundreds of legitimate ones.  Manipulation of borrower data and project recycling The fraud also involved manipulated borrower data. In some cases, farmers who had previously taken out loans from CROWDE were allegedly recorded as new loan recipients, effectively recycling past borrowers for new, potentially fraudulent, loans.  Funds intended for legitimate agricultural inputs were also funnelled through fictitious partner stores, raising concerns that the money was diverted for other purposes.    Exploitation of system vulnerabilities and farmer trust CROWDE reportedly maintained full control over the disbursement process, managing both borrower and lender escrow accounts. This centralized control facilitated the alleged misconduct.  The absence of direct communication between institutional lenders and the end borrowers. This lack of direct oversight meant lenders couldn't verify if farmers received their full loan amounts or the promised agricultural inputs. The limited financial literacy of many farmers also made the manipulation possible, as they might not have fully understood the terms or the discrepancies in their loan disbursements.  Internal knowledge and silencing More than 50% of CROWDE’s head office employees were reportedly aware of these fraudulent practices. However, no formal complaints surfaced for years due to a pervasive fear of retaliation, a lack of internal oversight, and a culture of silencing reinforced by the company’s leadership. The internal disbursement structure, the fake vendor network, and the manipulated borrower data were all reportedly overseen by top executives.    Outlook for CROWDE  Legal and regulatory fallout The most significant immediate threat is the police report filed on February 11, 2025, by J Trust Bank against CROWDE's management team, accusing them of fraud, embezzlement, and money laundering. This legal action, initiated by an institutional lender, underscores the severity of the alleged misconduct and will likely lead to criminal proceedings against key executives, including ex-CEO Yohanes Sugihtononugroho, commissioners Adryan Hafizh and Ahmat Sahri, and president director Andrew Yeremia PL Tobing.  Furthermore, the Financial Services Authority (OJK) has halted CROWDE's business license since the allegations surfaced earlier in 2025 2 . OJK's public statement on May 19, 2025, indicates ongoing monitoring and coordination with law enforcement agencies to support legal proceedings. This regulatory intervention effectively prevents CROWDE from conducting its core business operations.  Operational collapse CROWDE's operations appear to have largely ceased. Magdalena Joyce Andries, who had recently replaced Yohanes Sugihtononugroho as CEO, has resigned. The company is reported to have laid off all its staff earlier in 2025, with many allegedly not receiving their full layoff packages. Emails sent to the company's official ID are bouncing, suggesting it has become inactive. This points to a complete shutdown of day-to-day operations.  Deceptive financial image Despite the internal collapse and legal issues, CROWDE's website, as of March 5, 2025, continued to project a misleading image of financial health with a TKB90 score of 97%.  However, this figure was artificially inflated using fake borrower data and recycled projects to mask the platform’s actual performance. Insiders also claim the FY2023 financial figures obscure the true scale of misappropriated funds and hidden liabilities. This suggests any public-facing data is unreliable and aims to obscure the grim reality.  Potential salvage efforts Despite the severe challenges, there is a glimmer of a potential future for the company, albeit in a restructured form. Some investors are reportedly exploring options to salvage CROWDE.  Monk’s Hill Ventures, a key investor, is believed to be considering acquiring a majority stake from Mandiri Capital Indonesia and other shareholders. The proposed plan involves a restructuring of operations, significant improvements in financial oversight, and potentially maintaining Bank Mandiri as a lending partner. Yohanes Sugihtononugroho has confirmed that a recovery plan is being discussed, with Mandiri Capital leading the process. However, details remain undisclosed due to non-disclosure agreements.    Broader Industry Context  Investor Sentiment and Fundraising Outlook  The fallout is likely to deepen caution among both local and foreign investors, particularly in sectors where business models rely on financial intermediation, such as lending, invoice financing, and embedded credit. Although SEA remains a long-term growth story, the short- to medium-term fundraising climate is tightening.  Venture capital (VC) fundraising in Southeast Asia took a significant hit in 2024, plunging 68 per cent against 2023 and marking a four-year low, according to a recent report from DealStreetAsia Data Vantage . The fourth quarter marked the weakest funding environment in over six years. CROWDE’s collapse could reinforce these trends, prompting funds to delay deployments or tighten deal screening criteria, especially in Indonesia, where multiple P2P platforms are under distress or have entered liquidation.  Governance Scrutiny and Institutional Recalibration  One immediate effect is increased scrutiny on governance practices. The allegations against CROWDE, ranging from the use of fake vendors and recycled borrower data to inflated repayment scores, highlight systemic gaps in internal controls and independent auditing.  Institutional LPs may respond by favoring VC firms with more active portfolio monitoring, board representation, and structured post-investment governance frameworks. Funds that historically prioritized speed and founder-market fit may face pressure to institutionalize oversight processes and conduct more rigorous operational diligence.  Regulatory Implications  In Indonesia, where the Financial Services Authority (OJK) has already suspended CROWDE’s license, the case is likely to serve as a catalyst for stronger enforcement and licensing standards in the fintech lending space. Policymakers are expected to pursue stricter disclosure requirements, deeper background checks on key executives, and tighter controls around escrow fund management.  While these measures may increase compliance costs, they could ultimately restore lender confidence and support the long-term health of the sector. In the near term, however, regulatory tightening could reduce the number of active platforms and raise barriers to entry for new fintech startups.  Reputational Spillover for Impact and Agri-Focused Startups  CROWDE’s early positioning as an impact-driven solution for smallholder farmers complicates the picture further. The reputational damage may extend beyond the P2P sector to affect adjacent verticals such as agri-fintech and rural financial inclusion startups.  To counter this, companies in these segments will likely need to emphasize third-party auditing, measurable impact reporting, and governance transparency—not only to secure capital, but to preserve the credibility of mission-driven innovation in the region.   Conclusion  CROWDE’s downfall is a pivotal moment for Southeast Asia’s fintech sector. Beyond the legal proceedings and investor losses, the case underscores a deeper structural challenge: building scalable financial platforms that are not only innovative, but also trustworthy and well-governed. As fundraising conditions remain fragile, the startups and funds that emerge stronger will be those that treat governance as a core competency, not a regulatory checkbox.  References: CROWDE. Official Website . Accessed July 15, 2025. https://crowde.co/ Ng, J. (2024, June 24). Indonesia’s agri-focused P2P lender Crowde faces allegations of financial misconduct . DealStreetAsia. https://www.dealstreetasia.com/stories/crowde-allegations-financial-misconduct-448950 Tech in Asia. (2024, June 25). OJK reviews J Trust Bank’s report against fintech startup Crowde . https://www.techinasia.com/news/ojk-reviews-trust-banks-report-fintech-startup-crowde

1. The Foundational Challenge Planning a wedding or event in today’s modern world can feel like navigating a maze. Multiple vendors, unpredictable budgets, and limited visibility often leave both hosts and service providers frustrated. This fragmentation creates inefficiencies and inconsistent customer experiences—a problem crying out for innovation. 2. Enter LIVEN: A Cohesive Ecosystem Founded in 2022 and headquartered in Ho Chi Minh City, LIVEN Technology PTE. LTD integrates key platforms— Your Wedding Planner , VDES.vn , and Marry.vn —into a comprehensive solution for event planning : Your Wedding Planner streamlines vendor discovery, scheduling, and budgeting through intelligent tools and personalized support. VDES.vn operates as a full-service e-commerce marketplace for venues and event professionals. Marry.vn is the leading wedding directory in Vietnam, matching hundreds of vendors with couples seamlessly . Together, these brands digitize an outdated, offline-heavy industry, driving efficiency, access, and satisfaction throughout the event lifecycle. 3. LIVEN’s Regional Outlook VinVentures’ support aligns perfectly with LIVEN’s roadmap to become Vietnam's go-to event tech engine. With solid traction in Vietnam and forward-thinking plans for cross-border expansion—including destination weddings, LIVEN is positioned to redefine how weddings are planned across major SEA markets . Closing Thoughts VinVentures’ investment in LIVEN isn’t just a funding event—it’s a strategic milestone in transforming event planning across Southeast Asia. We firmly believe that scalable platforms like LIVEN, built thoughtfully in fragmented and technology-hungry markets, are essential pieces in the future landscape of event services. If you're building thoughtfully—or investing with long-term conviction—VinVentures is always open to connecting. Reach out: contact@vinventures.net | VinVentures.net VinVentures Capital Fund Contact: contact@vinventures.netMore Info: VinVentures.net

At Vingroup Annual General Meeting, it was reaffirmed that VinVentures’ mission is continuing to identify and invest in Vietnam’s most promising technology startups. From the establishment in late 2024, VinVentures has been ramping up its engagement across the local ecosystem - actively sourcing and evaluating high-potential ventures- and combining capital with strategic guidance to give founders the resources they need to scale and drive long-term competitiveness. If you’re building a groundbreaking venture, let’s connect, send us a message or visit apply now to explore how we can support your growth.

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.