Why the Anthropic Decision Is a Turning Point for AI Training Data As generative AI continues to disrupt industries, one question has loomed large: Can AI legally train on copyrighted data? A recent U.S. court decision involving Anthropic, the maker of the Claude AI model, has delivered the first major legal precedent. In short: Training AI models on copyrighted content may be allowed under fair use—but storing pirated copies is not. This ruling reshapes the landscape for AI startups, enterprise LLM builders, and venture capitalists evaluating AI companies. What Happened: Claude AI, Copyright Law, and Fair Use Anthropic trained its Claude model using millions of copyrighted books. The court ruled that: AI model training can qualify as fair use in certain contexts Storing pirated or unlicensed copyrighted works is illegal This ruling is significant because it carves out a potential legal path for AI development under U.S. copyright law—but draws a strict line around data acquisition and storage compliance. For startups building LLMs or other generative models, this precedent highlights the legal risks of using unverified datasets. AI Startups: Data Compliance Is No Longer Optional Many AI startups use scraped datasets containing copyrighted works—books, lyrics, articles, or multimedia—often assuming that “public” equals “permissible.” But this ruling makes clear: how you obtain and store training data matters. Key takeaways for startups: Verify all training data sources Document data collection and licensing practices Avoid storing or redistributing copyrighted content without rights Anticipate legal discovery on data sourcing in future fundraising or M&A What Investors Need to Know: New Due Diligence for AI For venture capitalists, this ruling introduces a new layer of AI investment diligence. Legal exposure related to AI training data could materially impact a company’s valuation or risk profile. Investors should now ask: Where did the training data originate? Was any copyrighted content used without proper rights? Has the startup built a defensible compliance framework? Are there legal audits or internal documentation of data use? Backing companies with opaque or risky data pipelines could bring reputational and financial downside. Investors who prioritize lawful AI development will future-proof their portfolios. The Future of Generative AI and Copyright Law This ruling doesn’t end the debate—it ignites it. Other lawsuits involving OpenAI, Meta, and Google are still pending. But the Anthropic case sets a tone: courts are willing to recognize fair use in AI training, while penalizing illegal data practices. What’s next: Growth in AI data licensing platforms More transparent, auditable training pipelines Case-by-case legal guidance on fair use boundaries Cross-border data governance frameworks for global AI markets Build Responsibly: Legal, Ethical, and Scalable AI At VinVentures, we champion founders who build responsibly, where innovation meets intention. In AI, that means complying with copyright law, respecting data ownership, and preparing for a world of regulatory clarity. Whether you're developing a foundation model, fine-tuning an industry-specific LLM, or investing in AI infrastructure, data legality is now a strategic differentiator. References list: Capoot, A. (2025, June 24). Judge rules Anthropic did not violate authors’ copyrights with AI book training . CNBC. https://www.cnbc.com/2025/06/24/ai-training-books-anthropic.html

Why "Best Practices" Aren’t Enough in Startup Innovation In the fast-paced world of entrepreneurship, founders are often encouraged to follow best practices and proven strategies. But the startups that truly change industries rarely follow inherited playbooks. Instead, they challenge assumptions, rebuild from scratch, and discover new opportunities others overlooked. This approach, First Principles Thinking , is the foundation behind some of the most successful and disruptive companies in tech. It empowers startup founders to innovate at a deeper level by rethinking everything from customer behavior to pricing models and operational structures. What Is First Principles Thinking? First Principles Thinking is a problem-solving framework that breaks down complex challenges into their most basic, undeniable truths. Instead of reasoning by analogy (doing what others have done), this method starts with core facts and rebuilds upward from there. Popularized by Elon Musk and rooted in physics and philosophy, this approach asks: What do we know to be scientifically or logically true? What assumptions are we carrying without realizing it? If we stripped this down to zero, what solution would emerge? For startups, this method encourages original thinking and differentiated strategy in markets that are often saturated with imitation. Startup Examples That Prove the Power of First Principles Some of the most iconic companies today applied First Principles Thinking at their inception. Their founders reimagined markets by questioning assumptions others accepted as facts: SpaceX rebuilt the economics of spaceflight by asking why rockets had to be single-use. From physics, they concluded reusability was viable—if designed correctly. Airbnb challenged the global hospitality industry by asking: what if travelers stayed in people’s homes instead of hotels? They created a new supply model with zero infrastructure overhead. Impossible Foods deconstructed meat to the molecular level and rebuilt it without animals—by starting with the chemical properties that make meat taste like meat. These companies didn't just improve existing models. They reinvented their industries from first principles. Why First Principles Thinking Matters More Than Ever in 2025 With rapid technological shifts, AI acceleration, and changing global dynamics, many traditional business models are becoming obsolete. For founders building in uncertain times, incremental improvement is no longer enough. Breakthroughs come from foundational clarity. In 2025, First Principles Thinking is especially relevant because: Venture capital has become more selective, prioritizing novel insights over safe bets Generative AI has commoditized superficial innovation, creating a need for deeper differentiation Global markets demand locally-grounded, agile business models that can adapt to volatility Startups that think from the ground up—not from history, will outperform in the long run. How Founders Can Apply First Principles Thinking Today Founders can begin applying this mindset by revisiting core areas of their business and asking fundamental questions: Product development : Are we building what users truly need or what competitors already offer? Monetization : Are our pricing structures based on customer value, or just industry norms? Go-to-market strategy : Have we tailored our GTM to user behavior, or are we copying common SaaS tactics? Cost structure : Could we redesign operations for efficiency rather than inheriting legacy models? Teams should develop a culture of curiosity. Regularly challenge assumptions, ask “why” multiple times, and experiment with rebuilding core systems from first principles. Implications for Investors and the Broader Ecosystem For venture capital investors, founders who use First Principles Thinking signal higher potential. These entrepreneurs typically demonstrate deeper market insight, stronger conviction, and more resilient strategies. Their solutions are not just incrementally better—they are categorically different. At VinVentures, we prioritize founders who: Understand the core dynamics of the problems they solve Have original views that deviate from market consensus Are willing to question “how things are done” in pursuit of superior solutions In a saturated funding environment, the ability to think independently and build from zero is a defining competitive advantage. Final Thoughts: Build From Truth, Not Tradition First Principles Thinking is more than a mental model, it’s a strategic advantage. As markets shift and technologies evolve, founders who adopt this approach will find new categories to lead, new products to create, and new ways to solve timeless problems. If you're building with deep conviction and rethinking the fundamentals of your market, VinVentures wants to hear from you . We back founders who challenge assumptions and create bold, scalable solutions. References list: Clear, J. (2020, February 3). First principles: Elon Musk on the power of thinking for yourself . James Clear. https://jamesclear.com/first-principles#:~:text=First%20principles%20thinking%20helps%20you,exploring%20widely%20for%20better%20substitutes .

From Inspiration to Industry Influence In 2016, Alexandr Wang walked out of a movie theater after watching The Social Network . His reaction was not about emulation but ignition. He didn’t want to copy Facebook. He wanted to build something enduring. That spark became Scale AI. Nearly a decade later, the company he founded is no longer a behind-the-scenes player. It is a defining force in one of the most strategic sectors in artificial intelligence—data infrastructure. Meta’s $14.3 billion acquisition of a 49 percent stake in Scale AI is not only a vote of confidence in Wang. It is a strategic pivot for Meta and a clear signal of where long-term value in the AI economy is being built. Understanding Scale AI’s Strategic Role Scale AI began with a clear but underestimated thesis: machine learning models are only as effective as the quality of the data they are trained on. As companies raced to build larger models, Scale focused on building smarter pipelines. Its core strength lies in delivering labeled, structured, and compliant data at enterprise scale. From powering autonomous vehicles to supporting government intelligence and defense initiatives, Scale has established itself as a critical node in the artificial intelligence supply chain. Its offerings include large language model training data, synthetic data generation, and tools for human-in-the-loop review. Unlike many startups chasing the spotlight, Scale positioned itself quietly within the infrastructure layer, serving foundational needs of clients who demand precision, reliability, and scale. That positioning is now being rewarded with one of the largest strategic investments in the space. Why Meta’s Investment Signals a Strategic Shift Meta’s decision to acquire nearly half of Scale AI is not merely financial. It reflects a broader strategic calculus. As generative AI reshapes the internet, owning high-quality data infrastructure is becoming a competitive necessity. Meta recognizes that scaling large models efficiently requires more than GPU clusters. It requires trust, repeatability, and data precision. This investment marks a new phase in the platform wars where access to trusted data becomes as valuable as model performance. With this move, Meta gains an in-house advantage for building safer, enterprise-grade AI systems at a time when public scrutiny and regulatory demands around AI ethics and provenance are rapidly increasing. Lessons for Founders: Value is Built Below the Surface Alexandr Wang’s journey is a masterclass in strategic clarity. Rather than building flashy front-end tools, Scale focused on what the market would eventually depend on. The infrastructure layer may not earn headlines as often, but it earns contracts, compounding influence, and strategic leverage. Founders should pay attention to four clear lessons from this trajectory: Infrastructure creates enduring value because it becomes essential, not optional Trust and security are monetizable, especially in regulated and enterprise contexts Focused execution in unsexy markets often leads to defensible positions Long-term success requires building where the strategic bottlenecks are forming, not where attention is temporarily gathered Scale AI has followed the principles that underpin the most successful companies in history, solve a fundamental problem, own the critical layer, and stay ahead of where the market is going. Implications for Investors and the AI Ecosystem For investors, this moment affirms that the next wave of artificial intelligence returns will not come solely from consumer applications or chat interfaces. They will come from the platforms, protocols, and infrastructure that make generative systems reliable and trustworthy at scale. The Scale AI and Meta deal offers three key insights: Strategic value in AI is shifting toward infrastructure that ensures data quality, compliance, and trust Long-term enterprise AI adoption will depend on verifiable, well-structured data pipelines Investors should deepen diligence into how startups source, structure, and govern their training data This is a reminder that as AI becomes mainstream, the real leverage lies in the foundational components that are difficult to replicate and essential to performance. Closing Perspective: The New Edge in AI Innovation Scale AI’s trajectory reflects a broader pattern emerging in the artificial intelligence ecosystem. The highest value companies are not necessarily the most visible—they are the most indispensable. While many chase virality or short-term engagement, builders like Wang and his team chose the hard path of building trust, tools, and infrastructure that future innovation depends on. At VinVentures, we are constantly seeking founders with the discipline, foresight, and originality to create those systems. If you are building a company that rethinks core assumptions, supports next-generation AI, or builds the foundations others will stand on, we want to hear from you. References list: ( Founder, Alexandr Wang, Joins Meta to Work on AI Efforts | Scale , n.d.) https://scale.com/blog/scale-ai-announces-next-phase-of-company-evolution

The Strategic Pivot In a recent report by DealStreetAsia, Temasek Holdings, Singapore’s sovereign wealth fund, is reported to be pulling back from early-stage startup investing across Southeast Asia. For years, Temasek has been one of the region’s most influential capital allocators, backing everything from fintech platforms to consumer marketplaces. Its move to reduce exposure in this part of the venture capital landscape is significant and deserves careful analysis. This decision is not an exit. Rather, it reflects a strategic recalibration driven by evolving market signals, tighter global liquidity, and a reassessment of risk-reward profiles. For founders, investors, and ecosystem builders in Southeast Asia, this marks a pivotal inflection point in how innovation will be financed and scaled going forward. What Is Driving the Pullback Temasek’s shift can be understood through three core lenses. Each reflects both regional realities and global investment patterns. First is macroeconomic volatility. The past two years have seen rising interest rates, inflation shocks, and geopolitical fragmentation. These factors have impacted risk appetite across institutional capital. Early-stage ventures, by nature uncertain and illiquid, are often the first to be reevaluated in such climates. Second is portfolio correction. Several high-profile startup failures in Southeast Asia—especially in consumer-facing and fintech sectors—have led to visible markdowns. This has prompted a shift toward capital preservation and a deeper focus on sustainable, capital-efficient growth. Third is market discipline. As the startup ecosystem matures, there is a natural correction away from the hyper-growth, valuation-chasing era. Investors like Temasek are adjusting their frameworks to reflect the need for clearer monetization, stronger governance, and real traction before writing early checks. What This Means for Founders For early-stage founders across Southeast Asia, Temasek’s pivot is both a challenge and a clarifying force. The challenge lies in the reality that fundraising will take longer and require deeper proof points. The capital environment will demand more than a compelling story. It will expect functional products, validated market demand, and operational rigor. However, this shift is also a powerful filter. Founders with strong conviction, long-term vision, and lean operating models will be more likely to attract meaningful backing. Scarcity forces clarity. And in this new cycle, clarity around the problem being solved, the customer being served, and the economics being engineered will define which ventures survive and scale. Founders should not interpret Temasek’s move as a withdrawal of belief in the region. Rather, it is an invitation to build with greater intentionality and sharper discipline. Implications for Local Capital and Regional VCs Temasek’s repositioning opens the door for emerging managers, family offices, and regional funds to step into a leadership role at the earliest stages. Local funds that understand cultural nuance, market fragmentation, and hyperlocal user behavior now have more space to shape the next generation of startups. This could lead to a healthier, more diverse capital stack in Southeast Asia. Instead of relying on top-down capital from global institutions, the region may see a rise in bottom-up support from specialized funds, university-linked ecosystems, and founder-led angel networks. Additionally, this moment could inspire a new era of co-investment and syndication across regional VCs who are aligned in thesis and approach. In the absence of large institutional anchors, capital collaboration becomes more important than capital concentration. The Future of Venture in Southeast Asia Southeast Asia remains one of the world’s most dynamic growth markets. Its expanding middle class, digital infrastructure, and mobile-first populations continue to present enormous opportunities for innovation. However, the days of momentum investing are being replaced by a return to long-term fundamentals. This means a few things. Startups will be evaluated more on business models than buzz. Regional scale will matter less than unit economics. And capital will increasingly flow to companies that can articulate not just how they grow, but why their solution is resilient in the face of competition and volatility. Temasek’s pullback should be viewed not as pessimism, but as a signal that the region is evolving. As the ecosystem matures, so too must its expectations—around what quality looks like, what capital is for, and what founders are truly ready to lead durable businesses. Final Reflection Every ecosystem experiences cycles. Southeast Asia is not losing momentum. It is gaining clarity. As global capital flows adjust, this is a chance for founders to focus, for local capital to rise, and for truly differentiated ideas to find their path forward. At VinVentures, we remain committed to supporting founders who build thoughtfully and with conviction. If you are building in Southeast Asia with discipline, purpose, and a clear sense of long-term value, we invite you to start a conversation. References list: Teo, M. (2025, June 6). Beyond the Buyout: How Temasek’s pullback from early-stage investing matters. DealStreetAsia . https://share.google/nuaNXeswcli7mIUFA

In late June 2025, the Management Board of Saigon Hi-Tech Park (SHTP) officially approved CMC Technology Group’s investment in the CMC Hyperscale Data Center project, valued at over USD 250 million . With 30MW initial capacity (scalable to 120MW), a GPU supercluster, and 25+ proprietary AI models, this facility is purpose-built for AI, cloud, and cybersecurity.     Vietnam’s rise as SEA's digital hub  Over the past three decades, Vietnam has evolved from a digitally underserved economy to a rising tech hub. In the late 1990s, fewer than 1 million Vietnamese had internet access. Today, the country hosts R&D operations for global giants like Google, NVIDIA, and Apple.  Vietnam's data infrastructure is growing rapidly. With approximately 33 data centres primarily in Hanoi and Ho Chi Minh City, the domestic market is currently valued at $1.57 billion (2024) and projected to reach $3.53 billion by 2030. Despite Southeast Asia's underbuilt digital capacity, forecasts suggest regional demand will triple by 2027.   Image 1: Vietnam Data Center Market Forecast, Research and Markets (2024)   CMC's facility marks Vietnam's largest digital infrastructure investment to date and establishes it as a serious player alongside Singapore, Indonesia, and Malaysia. More importantly, it reduces reliance on foreign-hosted services and positions Vietnam as a rising AI and data hub.    Vietnam reforms for infrastructure acceleration   Vietnam’s transformation isn’t accidental, it’s increasingly policy-enabled and private-sector-led.  Key developments include:  A new government decree (effective July 2025) that simplifies licensing for data centers and delegates approval authority to Ho Chi Minh City.  Relaxation of foreign ownership limits for digital infrastructure, encouraging cross-border capital.  Improvements in land acquisition, zoning, and tax incentives for tech projects.    The Project: CMC’s hyperscale data center  CMC’s new facility is more than a physical asset; it’s set to be the “AI Heart” of Vietnam’s digital economy.  Location: Situated in Saigon Hi-Tech Park, the strategic tech hub of Ho Chi Minh City.  Power capacity: Launching with 30MW, scalable up to 120MW to support future AI and cloud demand.  AI infrastructure: Equipped with 1,000+ NVIDIA GH200 GPUs, optimized for high-performance AI workloads.  Integrated stack: Runs on CMC Cloud, built to host and deploy over 25 proprietary AI models like SmartDocs (OCR) and CATI-VLM (vision-language model).  Sustainability design: Features water-efficient cooling systems is renewable energy-ready and uses digital twin technology for real-time monitoring and optimization.  Core use Cases: Supports generative AI, cloud computing, big data analytics, cybersecurity services, and smart city infrastructure.    Built to Tier III international standards, this project is expected to put Vietnam on the global digital map, not just as a consumer of technology, but as a creator and exporter.    Strategic impacts for Vietnam  Digital sovereignty & economic security  CMC’s infrastructure reduces Vietnam’s reliance on foreign-hosted data services, allowing greater control over national digital assets and aligning with rising global concerns about data localization and sovereignty.  AI innovation at home  By enabling local training and deployment of large AI models, this facility lowers latency, reduces costs, and opens new possibilities for Vietnamese startups and researchers.  CATI-VLM, for example, has outperformed GPT-4 Vision and Amazon Textract in recent benchmarks.  Workforce upskilling & talent magnet  CMC aims to grow to 15,000 engineers, including 6,000 AI-focused roles, by 2028.  This initiative will:  Fuel STEM education and GenAI adoption  Attract overseas Vietnamese and global tech talent  Create a national sandbox for applied production-grade AI development    Regional & global significance  Southeast Asia and geopolitical framing  CMC’s project positions Vietnam alongside Singapore, Indonesia, and Malaysia, but with a unique value proposition:  Tech talent: Young, abundant, and increasingly AI-trained  Policy agility: Faster execution compared to peers  Cost advantage: Competitive TCO for global cloud and AI clients  Green edge: Built with sustainability at the core  Vietnam is rising not just as a market, but as a platform for regional AI innovation and infrastructure export.    Final thoughts  CMC's $250 million data centre is more than just a facility. It’s the launchpad for Vietnam’s broader tech ambitions, serving startups, attracting global players, and accelerating AI innovation.  At VinVentures, we see firsthand how Vietnamese founders are building world-class products. This infrastructure leap will empower a new generation of startups with the tools, compute, and talent they need to scale globally.  References list: Data Centre Magazine. (2024, July 15). Will CMC’s US$250m data centre become Vietnam’s AI heart?   https://datacentremagazine.com/news/will-cmcs-250m-data-centre-become-vietnams-ai-heart   Tech in Asia. (2024, July 16). Vietnamese tech firm CMC to build $250m hyperscale data center.   https://www.techinasia.com/news/vietnamese-tech-firm-cmc-build-250m-hyperscale-data-center

When Figma first launched, the idea of designing in a browser sounded almost absurd. Design software had always lived on heavy desktop programs, with files passed back and forth and feedback trickling in through email threads. But two college students, Dylan Field and Evan Wallace, believed there was a better way, one where creativity could happen together, in real time, from anywhere.  Fast forward to 2025, and Figma isn’t just a design tool, it’s a $60 billion public company that has completely reshaped how teams build software, websites, and digital experiences. What made that possible wasn’t just clever engineering. It was a powerful mix of vision, community, product obsession, and long-term thinking.  In this blog, we take you through the full journey, from Figma’s earliest days in stealth mode to its blockbuster IPO. Along the way, you’ll see how it built a fiercely loyal user base, turned design into a team sport, and proved that community-led growth isn’t just a nice-to-have, it’s a strategy. Whether you're a founder, investor, or just someone curious about what makes great products succeed, Figma’s story has something to teach us all.    What is Figma?  Figma is well-known as a web-based design tool primarily used for user interface (UI) and user experience (UX) design, with strong emphasis on real-time collaboration. It allows teams to design, prototype, and test digital products like websites and mobile apps. Figma is known for its collaborative features, making it a popular choice for designers, product managers, and developers working together.  Figma’s business model mirrors that of many SaaS companies, charging based on user seats and roles, but what sets it apart is how aggressively it has expanded its functionality. With the 2023 launch of Dev Mode, Figma bridged a long-standing gap between design and development, allowing engineers to inspect, translate, and implement design specs more seamlessly.     Image 1: Key features of figma - Source: aloa - https://aloa.co/blog/what-is-figma     A decade of design: How Figma built a $60B+ company 2012–2013: A browser-based Bet   The idea for Figma was born at Brown University in 2012, where co-founders Dylan Field and Evan Wallace began exploring how the browser could transform digital design. Inspired by the collaborative nature of tools like Google Docs and virtual online worlds, they envisioned a multiplayer design platform, one that would replace desktop software with something more open, accessible, and collaborative.  In 2013, that bold idea attracted its first believers. Index Ventures, alongside Terrence Rohan, led a $3.8 million seed round, giving Figma the runway to begin building a browser-native design platform from scratch.    2015–2016: From stealth to launch   Figma’s early development happened quietly. By late 2015, the company launched its private beta, introducing a new way of working to a small group of designers. The concept was radical, real-time editing, version control, and no downloads required, and not everyone was ready. Many traditional designers, used to local files and solo workflows, were skeptical of Figma’s multiplayer model.  Still, the team stayed committed. In December 2015, Greylock Partners led a $14 million Series A, with partner John Lilly joining the board. Just a few months later, in 2016, Figma officially launched to the public. With its real-time collaboration, cross-platform accessibility, and clean interface, the product quickly gained traction among early adopters, particularly remote teams and startups.    Image 2: Figma UI from 2025 - Source: Web Design Museum https://www.webdesignmuseum.org/gallery/figma-in-2015      2018–2020: Capital, Confidence, and Community   As Figma’s product matured, so did investor interest. The company raised several major rounds to fuel its growth:  2018 – Series B: $25M led by Kleiner Perkins  2019 – Series C: $40M led by Sequoia Capital  2020 – Series D: $50M led by Andreessen Horowitz  Beyond funding, Figma’s community began to take shape. In October 2019, the company launched the Figma Community, a platform for users to publish, remix, and share design assets. This move transformed Figma from a productivity tool into a vibrant creative ecosystem, one where learning, sharing, and open-source design could thrive.    2021: From tool to workflow hub   In April 2021, Figma launched FigJam, a digital whiteboard for brainstorming, diagramming, and early-stage ideation. FigJam extended Figma’s reach beyond designers, bringing in product managers, engineers, and marketers, essentially any team involved in building digital products. The company also closed its Series E in June 2021, raising $200M from Durable Capital Partners, and reaching a $10 billion valuation.      Image 3: Figma introduced FigJam - Source: TechCrunch     2022: Going mainstream, and drawing adobe’s attention   Figma’s momentum continued in 2022. The company partnered with Google for Education, integrating its design principles into classrooms worldwide. That same year, Adobe announced plans to acquire Figma for $20 billion, a landmark moment signalling just how far the company had come. While regulators ultimately blocked the deal in 2023, it cemented Figma’s status as a category-defining company.    2023–2024: Expanding the platform   Rather than slowing down, Figma doubled down on innovation. In 2023, it released Dev Mode, a specialized interface for developers to inspect designs and translate them directly into code. The feature helped streamline handoff between design and engineering teams, one of the most common friction points in software development.  In 2024, Figma entered the AI era with the launch of Figma Make, a tool that allows users to generate functional prototypes from natural language prompts. It marked Figma’s boldest step yet in pushing design tooling toward AI-native workflows.    Image 4: Figma DevMode  - Source: Figma https://help.figma.com/hc/en-us/articles/15023124644247-Guide-to-Dev-Mode      2025: IPO and market validation   On July 31, 2025, Figma went public in a blockbuster IPO. Shares were priced at $32, but demand far outpaced supply, the offering was reportedly 40 times oversubscribed, and more than half of institutional orders received no allocation.  By the end of its first trading day, Figma stock closed at $115.50, up nearly 250%, giving the company a market cap of $56.3 billion based on outstanding shares, and a fully diluted valuation north of $65 billion. That figure not only dwarfed Adobe’s original offer but also made Figma one of the most valuable software IPOs of the decade.    Image 5: Initial public offering of the collaborative design application Figma, on Thursday, July 31, 2025 - Source: Business Insider     According to Business Insider, the IPO also delivered massive returns to its early backers:    Entity   Approx. Stake Post‑IPO   Value After Pricing (~$115.50)   Index Ventures  ~13%  $7.2B  Greylock Partners  ~12%  $6.7B  Dylan Field  ~11%  $6.3B  Kleiner Perkins  ~11%  $6.0B  Sequoia Capital  ~7%  $3.8B  Evan Wallace  ~5.5%  $3.1B  Praveer Melwani (CFO)  ~0.3%  $171M  Shaunt Voskanian (CRO)  ~0.2%  $136M  Lynn V. Radakovich  ~0.1%  $73M  Kelly Kramer  ~0.01%  $6.5M      Image 6: Figma shares more than triple – Source: Bloomberg    Why Figma became a breakout success?  3. 1 Rethinking design from the browser   Figma’s rise from a quiet startup to one of the most influential design tools of the decade didn’t happen overnight. It succeeded not just because of strong product execution, but because it solved real problems in a thoughtful way, and stayed true to a clear set of values.  When Dylan Field and Evan Wallace first imagined Figma, the design world looked very different. Most design software was expensive, hard to learn, and only ran on powerful machines. Collaboration was painful designers had to send files back and forth, manage dozens of versions, and rely on clunky handoffs to developers.  Field and Wallace saw an opportunity to build something better: a modern design tool that lived in the browser, worked on any device, and let people create together in real time. They believed that great design shouldn’t be locked behind paywalls or limited to experts. Instead, they wanted to make design more accessible, collaborative, and creative, for everyone.    Image 7: Figma view from browser - Source: Markup.io https://www.markup.io/blog/how-to-use-figma/      3.2 Product simplicity with collaborative power   That vision shaped every part of Figma’s product. From day one, it was built to be fast, intuitive, and multiplayer. You didn’t need to download anything. You didn’t need a powerful laptop. Anyone with a browser could jump in, co-edit, and contribute.  This made it easier for teams, designers, developers, product managers, marketers, to work together without barriers. Figma wasn’t just a better tool; it reflected how modern teams wanted to work: live, together, and in the open.    3.3 A community-first launch strategy   But Figma’s success wasn’t just about the product. It was also about how they introduced it to the world. Rather than launching with a big PR splash, Figma spent four years in quiet development, talking to real users and improving the product based on feedback. Claire Butler, the company’s first marketing leader, helped build early relationships by showing the tool to design teams, listening closely, and creating a sense of excitement before it even launched. This strategy-built trust and created a strong base of early adopters, people who felt personally invested in the product’s evolution.    3.4 Design evangelists and playful advocacy   Figma’s community-driven growth took off because of how naturally it encouraged sharing. The company invited well-known designers to try Figma early and let them speak publicly about their experiences.  It also hired a full-time “design advocate” who hosted live, informal events like Pixel Pong, weekly design battles streamed online. These moments showcased the tool’s capabilities while building a fun and inclusive creative culture around it. People didn’t just use Figma, they talked about it, tweeted about it, and brought others in.   3.5 Investing in scalable, human-centered community   As the user base expanded, Figma doubled down on community infrastructure. It launched the Figma Community, where designers could share templates and discover others’ work. It built Friends of Figma, a global network of volunteer-run meetups. It hosted Config, an annual conference for designers, developers, and product teams.  All these touchpoints helped Figma stay close to its users, learn from them, and celebrate their creativity.    3.6 Values that scale with the product   What tied everything together was a clear and consistent philosophy. Figma was designed to be open, collaborative, and intuitive, and those same values shaped how the company operated and grew.  It didn’t rely on flashy ads or aggressive sales. It built a tool that worked beautifully, listened to its users, and let word of mouth do the rest. That’s why Figma was able to earn the trust of everyone from independent freelancers to teams at Slack, Dropbox, and Twitter.    Could you be the next figma?   Figma didn’t win by being everywhere, it won by being indispensable to a specific group and then expanding from there. It’s proof that modern product success is equal parts innovation, execution, and community. If you’re a founder, it’s worth asking: Are you just building a tool, or are you building something people want to gather around?  And if you’re an investor, it’s worth looking beyond spreadsheets to ask: Is this company creating something people care  about? Figma’s rise reminds us that great products don’t just solve problems, they create movements. And that’s where real value is built.    References  list: Bloomberg. (2025, July 31). Figma IPO brings value near $20 billion from failed Adobe deal . Bloomberg.com . https://www.bloomberg.com/news/articles/2025-07-31/figma-ipo-brings-value-near-20-billion-from-failed-adobe-deal?fromMostRead=true   Business Insider. (2025, July). Who got rich on Figma’s IPO . https://www.businessinsider.com/who-got-rich-on-figma-ipo-2025-7   CNBC. (2025, July 31). Figma’s top VCs sitting on $20 billion in stock after IPO pop . https://www.cnbc.com/2025/07/31/figmas-top-vcs-sitting-on-20-billion-in-stock-after-ipo-pop.html   Figma. (n.d.). Meet us in the browser . Figma Blog. https://www.figma.com/blog/meet-us-in-the-browser/   Social+. (n.d.). Figma’s community-driven path to success . https://www.social.plus/blog/figmas-community-driven-path-to-success   Web Design Museum. (2016). Figma in 2016 . https://www.webdesignmuseum.org/gallery/figma-in-2016

After a sharp contraction in 2023, enterprise software investment rebounded in 2024. Venture funding climbed 7 percent over the previous year and reached $371 billion as mega‑rounds for AI‑native companies returned. Analysts at Sapphire Ventures noted that the year was marked by an AI‑driven rebound in VC funding  and “ultra‑round” financings concentrated in AI research labs. In the last quarter alone, investors poured $113 billion into the sector. This capital surge signals that the AI SaaS market is healing, and it’s shifting attention to companies that embed AI deeply into operations rather than grafting it on as a feature.    Why vertical SaaS is outpacing horizontal platforms  Enterprise software once promised unlimited scale through one‑size‑fits‑all products, but this model is showing its limits. A 2024 study found that more than 40 percent of software companies are increasing specialization in existing industries and almost a third are expanding into new verticals. Vertical SaaS, software built for a specific industry, now outgrows horizontal enterprise software.   While traditional enterprise software grew 11.1 percent in 2023 and is expected to compound around 9.6 percent through 2032, vertical SaaS is forecast to grow 12–15 percent annually through 2034. Customers increasingly demand tools that are preconfigured for industry‑specific workflows; firms with fewer than 50 employees juggle an average of 16 different SaaS apps, creating complexity that vertical solutions simplify. It’s no surprise that 89 percent of executives and IT leaders surveyed in 2023 said vertical SaaS “is the way of the future”.  Several structural advantages drive this momentum:  Higher revenue and retention: Verticalization correlates with a 10–20 percent increase in year‑over‑year revenue growth for B2B companies. Platforms that embed payments and other fintech services often see 2–5x increases in revenue and retention. That makes sense because sector‑specific workflows enable natural cross‑sell and upsell opportunities, strengthening customer stickiness.  Lower customer‑acquisition costs: Research shows that vertical SaaS vendors maintain a sales‑and‑marketing (S&M) expense ratio around 17 percent of revenue, roughly half the 34 percent spent by horizontal vendors. Focused teams, industry‑specific marketing, and efficient product adoption reduce cost to acquire each customer.  Market penetration: Specialists trade addressable market size for market share. For example, Mindbody, a fitness management platform, controls 61.5 percent of its niche. Shopify powers 29 percent of ecommerce websites. Deep domain knowledge allows these players to become the operating system of their industry.  AI unlocking new niches: Andreessen Horowitz observed that there are already over 5 000 vertical SaaS companies in the US spanning industries from trucking to real estate. AI is widening the opportunity: by automating sales, marketing and back‑office labor, AI can increase revenue per customer by up to 10x, turning previously “too small” markets into large opportunities. The same report notes that more than 600 NAICS industries still lack modern vertical software.  Ramp’s trajectory as evidence of the rebound  Ramp illustrates how an AI‑first vertical platform can ride this recovery. Founded in 2019 as a corporate card for startups, Ramp has evolved into an AI‑native finance operating system. It automates expense policies, procurement workflows and compliance tasks using domain‑aware agents. The company’s metrics highlight both market recovery and the advantages of specialization:  Metric   Aug 2023   Apr 2024   Mar 2025   Jun 2025   Jul 2025   Growth   Valuation (USD)   $5.8 B   $7.65 B   $13 B   $16 B   $22.5 B   + 290 %   Customer count   ~15 k   –  –  –  40 k+   >2.5×  Annualized revenue   $300 M   –  $700 M   –  –  >2×  Cash flow   Negative  –  Positive (2025)  Positive  Positive  Turned positive  Table 1: Key Performance Metrics and Growth Timeline (Reuters)   Valuation and revenue data from company disclosures show a nearly four‑fold increase over two years. Customer count grew from about 15 000 in 2023 to more than 40 000 by mid‑2025.  By early 2025 Ramp reached positive cash flow, a rare achievement for a late‑stage fintech, while still investing heavily in product development. The chart below visualizes Ramp’s valuation growth over this period. It illustrates how quickly an AI‑first vertical SaaS company can compound its value.    Image 1: Ramp’s Funding Round in USD (PM Insights)   Ramp’s success is rooted in its AI‑first architecture. In beta tests, its finance agents processed 10 000 transactions while reducing manual reviews by 85 percent and detecting 15x more policy violations. Customers are achieving three times the productivity they had in 2023, and Ramp expects a 30x increase by 2027 through parallel agent operations. By embedding AI into core workflows rather than offering bolt‑on copilots, Ramp turns routine finance tasks into autonomous processes. This capability is precisely what many CFOs seek as they navigate a market where efficiency and compliance are paramount.    Image 2: Ramp product improvements over past year. (Ramp)   Trends shaping the AI‑SaaS landscape  Concentrated investment in AI‑native platforms. The 2024 rebound was driven by mega‑financings for AI labs and AI‑native software companies. Though deal count hit a 10‑year low, funding volume recovered, signalling that investors are prioritizing fewer, higher‑conviction bets.    Shift to verticalization and specialization. More than two‑fifths of software companies are doubling down on industry specialization, and analysts forecast vertical SaaS growth of 12–15 percent annually, well ahead of horizontal platforms. Customers want solutions configured for their regulatory environment, data models and workflows.    Integration of fintech and embedded payments. Embedded finance is becoming table stakes. Among vertical SaaS providers surveyed, nearly 40 percent already offer a fintech product, and those who integrate payments often experience 2–5× revenue lifts. Fintech not only drives revenue but also increases retention by making the software indispensable to daily operations.    AI as leverage, not garnish. AI is transforming vertical SaaS beyond chatbots. Andreessen Horowitz notes that AI can replace labor across sales, marketing, customer service and finance, expanding the revenue potential per customer by up to tenfold. It also reduces customer‑acquisition costs by automating outreach and support.    Market penetration and operational efficiency. Vertical SaaS vendors achieve higher penetration rates and lower churn. Some platforms command 50 percent or more of their market. Their sales and marketing spend is half that of generalist software, leading to better margins and faster payback periods. This structural efficiency appeals to investors seeking resilient growth.  How to stand out in a competitive AI SaaS market  The recovery in AI‑native SaaS is real, but it doesn’t lift all boats. Here are strategies for builders and investors looking to differentiate:  Choose a niche with high manual labor or regulatory complexity. Look for industries with repetitive tasks and unique compliance requirements. According to a16z, many markets such as laundry services, chiropractic offices and veterinary clinics, spend billions on labor each year but remain underserved by software. AI can capture this labor spend by automating back‑office workflows.    Embed AI in the workflow, not on top of it. Avoid generic copilots. Build agents that understand domain rules, data structures and approval hierarchies. Ramp’s success stems from embedding AI into expense policy enforcement and procurement, reducing manual touchpoints by 85 percent.    Build payment and lending rails. Integrated fintech deepens stickiness and opens new revenue streams. Embedded payment solutions have been shown to drive 2–5× increases in revenue and retention for vertical platforms. The Tidemark benchmark survey found that about 40 percent of vertical SaaS companies already offer fintech products, if your competitors don’t, you have a first‑mover advantage.  Leverage specialized data. Vertical platforms collect granular, proprietary datasets that can feed better AI models. High‑quality training data leads to more accurate predictions and automation. This becomes a moat that generic software cannot match.  Focus on customer success and support. In niche markets, reputation and word‑of‑mouth carry disproportionate weight. HiringThing’s report notes that impeccable service prevents churn and converts users into advocates. Invest in implementation, training and continuous education to ensure customers realize the value of your software.  Conclusion: the future is vertical and AI‑native  The AI‑SaaS market is recovering, but not uniformly. Investors are favouring companies that combine AI‑native architectures with deep vertical focus. Vertical SaaS platforms benefit from higher growth rates, lower go‑to‑market costs, and stronger customer retention than their horizontal peers. They embed payments and AI into the core workflow, creating compounding advantages. Ramp’s rapid ascent, from a $5.8 billion valuation in 2023 to $22.5 billion in 2025, illustrates how powerful this model can be. For builders and investors, the message is clear: pick a vertical, understand its workflows intimately, and architect the product around automation. The next generation of software winners will not be those who tack AI onto dashboards, but those who make AI the operating system of an industry.      References list:    The State of the SaaS Capital Markets: 2024 in Review, 2025 in Focus.    https://sapphireventures.com/blog/the-state-of-the-saas-capital-markets-2024-in-review-2025-in-focus    2024 Vertical SaaS Trends.   https://blog.hiringthing.com/2024-vertical-saas-trends     2024 Vertical & SMB SaaS Benchmarking Report.    https://www.tidemarkcap.com/post/2024-vertical-smb-saas-benchmark-report     “AI Inside” Opens New Markets for Vertical SaaS.    https://a16z.com/2024/04/05/ai-inside-opens-new-markets-for-vertical-saas      Vertical Software Is Having A Moment.    https://activantcapital.com/research/vertical-software-is-having-a-moment     AI Finance App Ramp Is Valued at $22.5 Billion in Funding Round.    https://www.wsj.com/articles/ai-finance-app-ramp-is-valued-at-22-5-billion-in-funding-round-5a4269cb   👉 Interested in driving innovation with VinVentures? Share your venture with us   HERE .

The global artificial intelligence (AI) gold rush is not just in algorithms and applications; it’s in data centers and the physical infrastructure that powers AI. Across the world, tech giants are pouring hundreds of billions of dollars into AI infrastructure, especially data centers packed with high-end servers and networking gear.  This immense capital outlay is creating profound ripple effects far beyond Silicon Valley. It’s delivering windfalls to industries as varied as manufacturing, construction, materials, and energy, all of which are crucial in constructing and equipping these digital-age factories.   The question on everyone’s mind: How long can this breakneck investment in AI infrastructure continue, and what happens when the frenzy cools off?  Data Centers at the Core of AI Expansion  Massive data center facilities, filled with row upon row of servers, have become the backbone of AI’s rapid expansion. The scale of investment today is staggering. Industry leaders like Microsoft, Alphabet (Google), Meta, and Amazon are racing to expand their data processing capabilities, while new AI-focused startups (such as Anthropic and xAI) are also joining the fray.  Source: Morgan Stanley (2025)   Morgan Stanley forecast, AI infrastructure spending could hit $3 trillion by 2028; a scale compared to transformative waves like electricity or the Internet. AI is rapidly permeating workplaces and daily life, driving demand for cloud computing capacity, training infrastructure for ever-larger AI models, and massive data storage capabilities. Hyperscale data centers (AWS, Google Cloud, Azure, etc.) are the modern-day oil fields, producing the computational energy fueling AI innovation. This boom is already a rising tide lifting multiple industries. It’s a virtuous cycle: AI ambitions drive infrastructure investment → which boosts orders for heavy equipment, construction materials, and energy → which, in turn, strengthens the broader economy.  The Biggest Beneficiaries and Where Capital Is Flowing The AI data center boom has become a rising tide lifting many boats, far beyond Big Tech itself. Industries that don’t look like “tech” on the surface, construction firms, power equipment makers, cooling system providers, and fiber-optics suppliers,are seeing unprecedented demand. Amphenol: Acquired CommScope’s connectivity unit for $10.5B; 40% of that unit’s revenue tied to data centers. Stock has surged ~150% in two years, market value now above $130B. Vertiv Holdings: Power and cooling systems supplier; stock has quadrupled in two years, with operating profits up 36% in 1H 2025. Caterpillar & Cummins: Supplying backup generators and power equipment; Caterpillar’s sales of power generation units rose 19% in Q2 2025. SPX Technologies: Stock up 40% YTD; its new cooling systems allow operators to balance water conservation and energy efficiency. These firms are selling the “picks and shovels” of the AI gold rush, and investors are rewarding them handsomely. As SPX’s CEO Gene Lowe put it, hyperscalers prefer not to depend on a single supplier, leaving room for multiple players to thrive. Naturally, capital is following this momentum. The boom is not only enriching established industrials but also spawning opportunities for startups in cooling tech, advanced semiconductors, renewable-powered grids, and optimization software. Meanwhile, the world’s largest asset managers are taking strategic positions across the AI infrastructure value chain: Apollo Global Management ($650B AUM): Took a majority stake in Stream Data Centers, betting on long-term, utility-like AI cash flows. Blackstone ($1T AUM): Acquired QTS Realty Trust for $10B in 2021—once seen as niche real estate, now a hyperscale giant in the U.S. & Europe. Brookfield Asset Management ($900B AUM): Launched a dedicated AI infrastructure strategy (Aug 2025), leveraging strengths in renewable energy and construction to solve bottlenecks. Early innings, but clouds on the horizon  Despite the enthusiasm, industry observers caution that this boom won’t last indefinitely. Right now, companies are in a land-grab phase, spending aggressively to secure AI capacity. Two possible paths ahead:  Overshoot and pullback: AI firms may build far more capacity than the market needs in the medium term, forcing painful consolidation.  Sustained growth: demand keeps rising, and utilization eventually catches up, much like internet infrastructure in the late 1990s enabled the cloud revolution a decade later.  The truth is likely somewhere in the middle. Spending at current levels is unsustainable forever. Eventually, hyperscalers will shift from expansion to optimization, moving from building more centers to making better use of the ones they already have. Momentum is strong now, but a slowdown is inevitable.    Opportunities and Risks Ahead for Startups and Venture Capital   The AI infrastructure boom brings both extraordinary opportunities and real risks for startups and investors.  Opportunities: Startups with defensible technologies,cooling, chips, energy solutions, workload management, are well-positioned to capture value. Hyperscalers’ reluctance to rely on single suppliers opens the door for nimble, innovative players.  Risks: Companies tied too closely to hyperscaler spending may face sharp slowdowns once expansion eases. For example, construction-tech startups thriving on today’s data center wave could see demand evaporate just as quickly.  Agility will be the defining trait. Founders should design products that can pivot across markets, enterprise IT, edge computing, or slower-moving international regions. For VCs, the discipline lies in backing companies solving enduring pain points like efficiency, cost, and sustainability, rather than chasing those buoyed only by hyperscaler capex.  For VinVentures and other forward-looking funds, this is an era of immense opportunity, but also one demanding rigor and long-term perspective. The future of AI infrastructure will be built not just on ambition, but on resilience, the ability to withstand tomorrow’s inevitable cooldown while capturing today’s growth.    Sources:  ainvest. SPX Technologies (SPXC) earnings outperformance, guidance hike signal long-term buy potential as industrial sector shifts.  ainvest. https://www.ainvest.com/news/spx-technologies-spxc-earnings-outperformance-guidance-hike-signal-long-term-buy-potential-industrial-sector-shifts-2508/?utm_source=chatgpt.com   Caterpillar Inc. 2Q 2025 Caterpillar Inc. earnings call transcript.  Caterpillar Inc. https://s25.q4cdn.com/358376879/files/doc_financials/2025/q2/2Q-2025-Caterpillar-Inc-Earnings-Call-Transcript_8-5-2025.pdf   Bond, S. Apollo bets on AI data centre boom with Stream stake.   Financial Times.   https://www.ft.com/content/7052c560-4f31-4f45-bed0-cbc84453b3ce?utm_source=chatgpt.com   Hook, L. Brookfield intensifies push into AI infrastructure.   Financial Times.   https://www.ft.com/content/b5381dac-15dd-46d5-a560-0f62386a961e?utm_source=chatgpt.com   Investopedia Staff. Vertiv Holdings stock rises as data center firm raises full-year outlook.   Investopedia.   https://www.investopedia.com/vertiv-holdings-stock-rises-as-data-center-firm-raises-full-year-outlook-11781612?utm_source=chatgpt.com   Morningstar Equity Research. Amphenol–CommScope deal widens data center opportunity and comes at a fair price.   Morningstar.   https://www.morningstar.com/company-reports/1318904-amphenol-commscope-deal-widens-data-center-opportunity-and-comes-at-a-fair-price?utm_source=chatgpt.com   Morgan Stanley Research. The AI diffusion: Tech roundtable.   Morgan Stanley.   https://www.morganstanley.com/insights/articles/ai-diffusion-tech-roundtable   Roumeliotis, G., & Ranasinghe, D. Blackstone to take QTS Realty Trust private in $10 bln deal.   Reuters.   https://www.reuters.com/business/blackstone-take-qts-realty-trust-private-10-bln-deal-2021-06-07/?utm_source=chatgpt.com   Singh, K. Apollo buys majority stake in Stream Data Centers.   Reuters.   https://www.reuters.com/technology/apollo-buys-majority-stake-stream-data-centers-2025-08-06/?utm_source=chatgpt.com   Vertiv Holdings. Vertiv reports strong orders, sales and EPS growth; raises full-year guidance.  Vertiv. https://investors.vertiv.com/financial-news/news-details/2025/Vertiv-Reports-Strong-Orders-Sales-and-EPS-Growth-Raises-Full-Year-Guidance/default.aspx?utm_source=chatgpt.com   Michaels, D. Brookfield Asset Management sets sights on “in-the-box” AI investments.   The Wall Street Journal.   https://www.wsj.com/articles/brookfield-asset-management-sets-sights-on-in-the-box-ai-investments-7bba605c?utm_source=chatgpt.com   👉 Interested in driving innovation with VinVentures? Share your venture with us   HERE .

Fundraising is never simply a matter of having a great product or a bold vision. As Omri Drory, Ph.D., General Partner at NFX, observes, a founder’s ability to secure capital is shaped by many forces: the strength of the idea, the credibility of the team, the attractiveness of the technology, and even the personal impression the founder leaves on investors. Yet, there is also a quieter, often unspoken factor, the rhythms of venture capital itself.    Drory describes these rhythms as a kind of seasonality . However, these rhythms are not rigid rules. Great companies raise capital in every month of the year. Term sheets get signed in July, rounds close in December. What seasonality offers is not a limitation, but a pattern: moments when investors are more focused and responsive, and moments when their attention is naturally pulled elsewhere. For founders, recognizing these patterns can make fundraising a bit smoother, a bit faster, and sometimes a bit more favourable.    Why Seasonality Matters  Fundraising is less about fixed deadlines and more about momentum. Conversations tend to move faster when investors are in-market, actively sourcing, and not distracted by travel or internal reviews. Conversely, even highly promising companies can see processes drag when investor attention is divided. The ability to keep energy high across multiple conversations often determines whether a round closes quickly or stretches out.  Deal flow data reflects these rhythms. An analysis of over 42,000 venture rounds shows that the summer months of May through August account for 34.6% of annual deal activity, contradicting the idea of a “summer slowdown.” December consistently leads with 10.8%, as firms deploy capital before year-end, while January and February lag at 6.8% and 6.9% as funds focus on planning cycles (Lemkin, 2024).  The implication is clear: fundraising can happen in any month, but aligning a raise with periods of higher responsiveness can reduce friction, help maintain urgency, and support a more decisive outcome.        Image 1: 42,000+ primary rounds signed by US startups on Carta | 2018-2024 ( Lemkin, J., 2024)    Understanding the fundraising calendar  The U.S. and global fundraising year can be divided into four main seasons, with clear peaks and troughs:  January – March : A period of renewed energy as budgets are refreshed and major gatherings, such as the J.P. Morgan Healthcare Conference, set the tone for the year.  April – Early June : Still active, though slightly steadier than the first quarter. Sometimes called the “false lull.”  Summer (June – August) : Investor travel can slow processes, though founders who raise during this period may benefit from less competition for attention.  September – November:  Another strong window, short but intense, as many firms look to finalize deals before year-end.  December – New Year : Typically quieter, with more focus o n internal reviews, though some funds push to close outstanding deals.      Image 2: The fundraising season in the US - Drory, O. (2023, September 6)     How to Fundraise by Utilizing Seasonality Effectively  Being Present in the Right Place at the Right Time   Fundraising has a strong relationship component. While remote pitching has become more common and efficient, in-person interactions often enable a different kind of focus and familiarity. In his essay, Omri Drory notes an instance where he booked a transatlantic flight for a spontaneous coffee meeting, underscoring how availability can create opportunities.  In practical terms, this means that founders may benefit from planning physical presence in key hubs during periods of heightened investor activity. Being in town can make it easier to arrange meetings, participate in events, and build familiarity over multiple touchpoints.  Creating Urgency Through Seasonality   Drory also highlights that investor decision-making tends to accelerate when competitive dynamics are visible. Fundraising seasons can amplify this effect, as many firms are simultaneously reviewing opportunities. Running a structured and well-timed process allows founders to present their round as time-sensitive rather than open-ended.  This typically involves identifying the right partners within firms, individuals whose focus and investment thesis match the company, and approaching them through strong introduction channels. Warm introductions from portfolio founders or trusted co-investors tend to carry more weight than cold outreach.  Preparing in the Off-Season, Executing in the On-Season   Preparation is most effective when treated as an ongoing discipline rather than something triggered only when runway is short. Founders who define milestones for the next round, build investor relationships early, and maintain an updated data room are often better positioned to move quickly when the time comes.  Quieter months can be used to refine narratives, rehearse delivery, and ensure financial and legal materials are in order. This way, when more active seasons arrive, founders can focus on execution rather than catching up on preparation.  Understanding Fund Economics, The “Magic Numbers”   Venture funds generally operate within specific economic parameters. Ownership targets and check sizes are shaped by fund size, stage focus, and portfolio strategy. For example, seed funds may seek 10–30% ownership, Series A funds often aim for 15–25%, while later-stage funds can accept smaller percentages if the company’s growth prospects are sufficiently large.  If a proposed round does not align with these parameters, participation is less likely, regardless of company quality. Founders who research these considerations and structure their raise accordingly are better positioned to engage investors productively. Clear alignment between a company’s fundraising needs and a fund’s investment model helps create more efficient conversations.  Speed as a Signal of Execution   Finally, Drory emphasizes speed. A fast, decisive fundraising process sends a powerful signal to investors about the founder’s ability to execute. Conversely, a slow round raises doubts about traction, clarity of story, or leadership.    This means being highly responsive, anticipating investor requests, and managing conversations in parallel to maintain momentum. Speed is not only about logistics but also about perception: investors extrapolate from a fundraising process to a company’s overall execution capability.    Conclusion: Seasonality as an Additional Lever   Seasonality in fundraising should be viewed as a pattern, not a prescription. Companies with strong momentum or compelling breakthroughs can raise capital at any point in the year. At the same time, being aware of the rhythms in investor activity can provide founders with an extra degree of leverage, helping them maintain momentum, shorten timelines, or create more favourable dynamics.  At VinVentures, we partner with founders who are reshaping industries and defining what comes next. If you are building with that ambition, we want to hear from you. Let’s talk if that’s you!   References:   Drory, O. (2023, September 6). The Seasons of Fundraising. NFX. https://www.nfx.com/post/fundraising-seasons     Lemkin, J. (2024, July 10).  Every month is a good month to raise: What 42,000+ funding rounds tell us about raising capital in July . SaaStr.  https://www.saastr.com/every-month-is-a-good-month-to-raise-what-42000-funding-rounds-tell-us-about-raising-capital-in-july/     👉 Interested in driving innovation with VinVentures? Share your venture with us   HERE .

Many companies have joined the AI race. Over the past two years, we’ve seen an explosion of pilots: generative models for marketing copy, chatbots for customer service, copilots for developers. According to an MIT report, despite the momentum, only about 5 percent of organizations have successfully scaled AI pilots into measurable business impact.  What separates the few that scale from the many that stall?  An article in Harvard Business Review  sheds light on this question. Their central argument is that successful AI adoption hinges less on algorithms and more on leadership. Companies that scale AI pilots do so because they have leaders who can connect technology with strategy, embed it into workflows, and build the trust needed for adoption. They call these leaders  AI shapers.   Why Technical Expertise Alone Is Not Enough  Think of Johnson & Johnson, one of the largest healthcare companies in the world. Over the course of a few years, it ran nearly 900 AI pilots. By sheer numbers, it should have been a success story. But most of those pilots went nowhere. Instead of declaring victory on volume, J&J made a difficult but crucial pivot: it shut down the majority of pilots, moved governance closer to business units, and doubled down on the handful that actually created measurable value.  This example illustrates the heart of the issue. Talent and experimentation are necessary but not sufficient. What matters is the ability to sift through experiments, choose what aligns with business goals, and scale only the use cases that matter. That is a leadership challenge, not a coding challenge.  The Five Behaviors of AI Shapers  The findings show five leadership behaviors that consistently show up in companies that succeed with AI pilots. They call this the  SHAPE index:   strategic agility, human centricity, applied curiosity, performance drive, and ethical stewardship .    Image 1: T he SHAPE Index: Five Behaviors of AI Shapers  (Havard Business Review)   S-Strategic Agility   Leaders with strategic agility treat pilots as experiments rather than rigid roadmaps. They ask tough questions early: Does this create business value? Do we have criteria for when to pivot?  Consider a global logistics company that piloted a generative AI tool for delivery route optimization. Early results fell short of expectations. Instead of increasing investment into the tool, leadership redirected resources into predictive maintenance AI, which quickly showed cost savings across the fleet. The decision wasn’t about abandoning AI, but about adjusting the course to capture value.  Organizations that lack this agility often defend projects because they’ve already invested heavily in them. That mindset leads to inefficient use of resources and lost opportunities for value creation.   H-Human Centricity   Trust sets the pace of AI adoption. When employees feel sidelined or threatened, pilots lose momentum. Leaders with human centricity ask a different question: How can AI enhance people’s contributions?  At a European bank, leadership introduced a generative AI compliance assistant. Rather than imposing it top-down, they invited compliance officers into the design process. Employees came to see the tool as a support for their work, not a replacement, and adoption followed. By  contrast, firms that announce “AI will make us more efficient" without addressing employee concerns often encounter resistance that slows progress.  This reflects a broader workplace dynamic. Stanford psychologist Jamil Zaki has described what he calls an “empathy crisis” in today’s organizations. AI can either intensify that challenge or become a tool for empowerment, depending on how leaders choose to introduce it.  A-Applied Curiosity   Generative AI is evolving rapidly, with fresh applications appearing almost daily. The leaders who succeed are those who combine curiosity with discipline. They launch small, low-cost pilots with clear learning objectives, then double down on the few that deliver tangible value.  Applied curiosity means exploring widely but scaling selectively. It’s about testing many ideas, capturing lessons quickly, and channeling resources into the ones that create measurable business impact.  Without this discipline, organizations risk spreading themselves too thin, running pilots that never connect back to real priorities.  P-Performance Drive   Many organizations equate activity with progress, emphasizing the number of pilots launched rather than the value created. Leaders with true performance drive shift the focus toward outcomes, whether in revenue growth, cost efficiencies, or customer experience, rather than vanity metrics.  Johnson & Johnson offers a useful example. By  streamlining its portfolio of pilots and scaling only those that demonstrated clear ROI, the company avoided unnecessary experimentation and concentrated resources on measurable impact.    E-Ethical Stewardship   Generative AI introduces new risks, including hallucinations, bias, and potential intellectual property exposure. Ethical stewardship means addressing these challenges from the very beginning by embedding governance, transparency, and oversight into the workflow. For example, one media company built “red team” testing into every generative AI deployment, auditing outputs for accuracy and bias before going live. This not only reduced reputational risk but also strengthened trust with employees and stakeholders, creating the foundation for faster scaling. By contrast, organizations that postpone governance until after issues emerge often find themselves struggling to catch up. So What Leaders Can Do Differently?  Moving from pilots to scale requires deliberate choices. Four stand out:  Assess where your leadership stands against the SHAPE framework. Without diagnosis, organizations misplace talent and misjudge readiness.  Hire for the capabilities that are hardest to teach, particularly strategic agility and applied curiosity. Technical skills are abundant; shaping skills are scarce.  Develop leaders who already show shaper behaviors by giving them visibility, stretch assignments, and targeted support. Move beyond generic bootcamps and focus on the few leadership skills that matter most.  Role model adoption at the top. Employees will not embrace AI if their leaders don’t use it themselves. Leaders who integrate AI into their daily decisions send a powerful signal that adoption is a shared priority, not a delegated task.  Conclusion  AI success will not be won in research labs alone.   It will be won in workflows, in strategy rooms, and in the daily choices leaders make to guide adoption across the enterprise. The question is not how many pilots you launch, but whether you have the leadership to scale the ones that matter.  The companies that cultivate AI shapers won’t just join today’s 5 percent of winners—they will define the playbook for successful AI adoption in the years ahead.  The choice is yours: will your organization shape AI, or be shaped by it?     References:  Estrada, S. (2025, August 18). MIT report: 95% of generative AI pilots at companies are failing. Yahoo Finance. https://finance.yahoo.com/news/mit-report-95-generative-ai-105412686.html?_guc_consent_skip=1757847197   Van Den Broek, R., Hellauer, S., & Wang, D. (2025, September 12). What Companies with Successful AI Pilots Do Differently. Harvard Business Review. https://hbr.org/2025/09/what-companies-with-successful-ai-pilots-do-differently?ab=HP-hero-latest-2    .  👉 Interested in driving innovation with VinVentures? Share your venture with us   HERE .

The problem: Misconception in AI startups - strong models or viral tools alone guarantee success.   There’s a common myth in the startup world, especially in AI, that great technology is all it takes to succeed. Build a powerful model, launch a viral tool, and the rest will follow. But the truth is far more nuanced.  As NFX (2025) points out, in the age of generative AI, a winning startup is defined not only by what it builds, but how it monetizes, retains, and grows over time. Founders who focus solely on product risk scaling a leaky bucket. The best founders obsess over business models early, and continuously.  More importantly, innovation doesn’t mean inventing something from scratch. Instead,   the most resilient companies creatively combine and layer existing business models into a stackable structure that scales with customer value and company maturity.    Current Landscape of AI Startup Business Models  The Evolution of Software Business Models  To understand where AI monetization is heading, it helps to reflect on how software business models have evolved:  On-premise software: one-time licensing  SaaS: subscription or seat-based pricing  Usage-based: pay-as-you-go, especially common in APIs and cloud services  Outcome-based (AI's frontier): charging for results, tasks completed, hours saved, outcomes delivered  AI is accelerating a shift toward models that tie pricing to value realized, not just software delivered. And within this shift, NFX identifies two dominant early-stage strategies: Price Disruption and Quality Innovation.    Two Innovation Paths in AI     Price Disruption: Lowering Costs, Expanding Access   Price Disruption uses AI to offer services at significantly lower cost, without reducing quality. It works best when:  Legacy tools are outdated or overgeneralized  Services are commoditized  Incumbents can’t match AI’s speed or scalability  But if applied blindly, it can backfire. A “race to the bottom” dynamic emerges where companies compete only on price, eroding margins and long-term value.  NFX suggests smarter versions of Price Disruption:  Upstream Monetization: Offer a low-cost service at a bottleneck and monetize later (e.g., through fintech, marketplace, or data layers).  Bundling: Use a low-cost wedge product and upsell high-value add-ons.  Performance-based pricing: Charge based on campaign success or actual results.  Usage-based upsell: Give away the core tool, then monetize usage (e.g., per output, API call, or storage).  If done strategically, Price Disruption becomes a wedge, not a trap.    Quality Innovation: Winning on Trust, Not Price   The second path, Quality Innovation, targets sectors where cost is secondary to trust and performance. Think legal, healthcare, or financial services. These customers don’t just want cheaper, they want better, faster, and more reliable. And AI, when implemented well, can deliver. The advantage here lies in gross margin expansion. AI reduces overhead, improves delivery speed, and enables consistent service, while maintaining premium pricing.  To extend Quality Innovation, founders can:  Monetize proprietary data: Offer industry benchmarks, insight dashboards, or data feeds.  Introduce DIY/self-serve tiers: Let budget-conscious users engage with a light version, then upsell to high-touch services over time.  This model tends to be more defensible. It builds on domain knowledge, brand trust, and operational leverage, which take time to replicate.    Solution: From product to platform, what stackable models look like  Truly innovative business models often emerge not from reinvention, but recombination. You don’t need to invent a new model. You can:  Fuse two well-known models (e.g., SaaS + usage-based pricing),  Subvert incumbents’ business logic (e.g., freemium over high-fee models),  Or stack new layers as your product and capabilities expand.  Some leading examples:  Amazon started with e-commerce, then added AWS (usage), then ads (data monetization).  OpenAI launched ChatGPT with subscriptions, then layered API usage for developers.  Robinhood upended trading fees, then expanded into fractional shares, cash management, and financial services.  Each of these companies didn’t just build a product, they graduated into platforms by stacking monetization models that deepened engagement, improved margins, and extended their defensibility.  Let’s take a hypothetical AI startup: it launches with a simple productivity tool, monetized by monthly subscription. That’s layer one. Later, it opens API access for enterprise clients, layer two. Then it offers fine-tuned model training for domain-specific tasks, layer three. Eventually, it builds full-stack consulting and implementation services, layer four.  Each step builds on the one before: revenue scales, churn decreases, and the product becomes increasingly embedded in the customer’s workflow.   Final Thoughts: Business Models = Long-Term Defensibility  In a crowded AI landscape, where the pace of innovation is fast and capital is abundant, the business model is often the last true moat.  Technology gets copied. Features get commoditized. But a well-architected, stackable business model compounds over time, boosting margins, increasing LTV, and embedding your product deeper into the customer’s world. As NFX emphasizes, founders must think of their business model as a core product, a structure to evolve, test, and scale just like the tech.  In this context, stackable business models are more than just a monetization tactic. They’re a long-term strategic advantage. Each layer, subscriptions, APIs, data, consulting, marketplaces, deepens your customer relationship, expands your revenue per user, and adds friction for competitors trying to replace you.    References:     AI-Driven Business Models: 4 characteristics | HBS Online . (2024, September 10). Business Insights Blog. https://online.hbs.edu/blog/post/ai-driven-business-models?utm_source=chatgpt.com     Ward, E. (2025, February 13). Stackable business models in the age of AI . NFX. https://www.nfx.com/post/stackable-business-models#The-Bare-Bones-of-Business-Model-Innovation   👉 Interested in driving innovation with VinVentures? Share your venture with us   HERE .

On May 29, 2025, Venture Forum 2025 was held in Hanoi, co-hosted by VinVentures and the National Innovation Center (NIC). Under the theme “Redefining Capital,” the forum brought together an esteemed group of 200 leaders from government agencies, financial institutions, venture funds, startups, and corporate partners. For the first time, three of Southeast Asia’s top venture debt institutions: Genesis Alternative Ventures, InnoVen Capital, and January Capital - gathered at a forum in Vietnam to unlock new capital pathways and expand funding access for local startups. Over 200 leaders gathered at Venture Forum 2025 to explore new models of capital and collaboration. Source: VinVentures Session 1 – “When Banks Think Like VCs” Moderated by Mr. Hai Nam Bui, CEO of SoBanHang, this session explored how financial institutions can evolve from traditional lenders into strategic innovation partners, highlighting the potential of venture arms, policy alignment, and tech-driven engagement models. Session 2 – “Rethinking Venture Debt in Southeast Asia” Moderated by Ms. Ngoc Nguyen, Deputy Editor of DealStreetAsia Vietnam, this discussion emphasized how venture debt can serve as a complementary financing tool, with experts underscoring the importance of sound governance, credit readiness, and long-term planning. Session 3 – “Fintech’s Role in Expanding Access to Capital” Moderated by Mr. Nam Doan, Principal at ThinkZone Ventures, the session showcased how fintech solutions are expanding access to finance, with founders sharing how trust, technology, and user-centric design can deliver inclusive services for underserved communities. We extend our gratitude to all speakers, guests, partners, and media agencies whose participation made Venture Forum 2025 more than just a dialogue — it became a milestone for collaboration in shaping a more connected, resilient, and innovation-led ecosystem in Vietnam and Southeast Asia. As part of VinVentures’ core value of partnership, the forum reaffirmed our role as a connector of capital, ideas, and innovation across the region. 👉  To explore media coverage of the forum, please visit : 🔗 VnEconomy 🔗 Baodautu 👉 Interested in driving innovation with VinVentures? Share your venture with us   HERE .