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? 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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 .

Read the original Forbes article here: https://www.forbes.com/sites/bradtempleton/2025/08/13/robocar-startup-tensor-unveils-luxury-self-drive-car-for-2026/ As reported by Forbes in August 2025, Silicon Valley-based AI company Tensor introduced the world’s first personal Level 4 autonomous vehicle built for private ownership: the Tensor Robocar. While many industry players continue to focus on centralized robotaxi fleets, Tensor is exploring a different path —one where autonomy is not merely accessed via platforms, but is integrated directly into individually owned vehicles.  Tensor's repositioning, from its earlier days as AutoX in China to its current form as a U.S.-based builder of personalized autonomous agents, reflects a broader trend toward embedded, agentic AI systems that serve individual users. The Robocar is not just a car; it is being framed as a mobile AI companion designed to enhance autonomy, privacy, and user control.     Tensor's Robocar - Source: The Verge About Tensor  Founded in Silicon Valley in 2016, Tensor is an American AI company focused on building agentic technologies, products that act on behalf of their users with intelligence and autonomy. Originally known as AutoX, the company launched one of the earliest robotaxi fleets in China before divesting its operations there to concentrate on a new vision: the personal Robocar. Tensor’s flagship product embodies this shift. With its dual-mode design and advanced AI stack, the Tensor Robocar positions itself not just as an autonomous vehicle but as a mobile AI agent built for private ownership. Headquartered in San Jose, with offices in Barcelona, Dubai, and Singapore, Tensor aims to redefine how individuals engage with autonomous mobility.  The Tensor Robocar: Redefining the technical architecture of autonomy  The Tensor Robocar was engineered from the ground up for autonomy. With over 100 integrated sensors, including 37 cameras, 5 custom lidars, and 11 radars, the vehicle offers comprehensive situational awareness. A foldable steering wheel and retractable pedals enable seamless transitions between human and autonomous operation.  Central to this platform is the Tensor Foundation Model, a dual-system AI framework. One part relies on imitation learning to handle real-time reflexive driving tasks, while the other uses a transformer-based visual-language model to interpret edge cases and environmental complexity. Together, they represent an effort to balance fast decision-making with broader contextual reasoning, a necessary step for consumer-grade autonomy.  The Robocar was conceived from the start for private ownership, with production handled by Vietnamese automaker VinFast. Engineering for full-stack redundancy and user autonomy  The Robocar was designed with self-reliance at its core. Full redundancy across power, communications, and control systems, alongside sensor cleaning, diagnostics, and over-the-air updates, enables the vehicle to function with minimal human intervention.  This level of resilience is rarely seen in consumer products. The vehicle is engineered to anticipate maintenance needs, park autonomously, and operate even in suboptimal environments, such as low-signal garages or bad weather. These capabilities hint at a shift from AVs as fleet-managed assets to self-sustaining, user-oriented machines.    The call for visionary founders and ecosystem collaboration  At VinVentures, we seek out founders who not only bring technical expertise but can also align with complex, multi-stakeholder environments. The Tensor case illustrates the kind of global partnership model that may become increasingly common, and necessary, as AI-native systems move into the physical world.  As we think about the next wave of founders and frontier technologies, we are especially interested in those who can contribute to building long-term, high-trust collaborations across the global tech-industrial landscape. 👉 Interested in driving innovation with VinVentures? Share your venture with us   HERE .

AI has become the driving force of venture capital in 2025, but the surge in capital isn’t as broad as the headlines might suggest. According to  EY report on Venture capital investment trends (2025) , startups raised over $80 billion in the first quarter alone, nearly 30% higher than the end of last year. Yet peel back the numbers and you see a different story: without one $40 billion AI deal, total funding would have dropped by more than a third. In other words, capital is piling up, but it’s pooling around a few frontrunners rather than flowing evenly across the market. That dynamic creates as many risks as it does opportunities, and it puts real pressure on both founders and investors to stay grounded in fundamentals.  In 2025, venture capital is moving to a new rhythm, and AI is the conductor. The most eye-catching rounds tell us that money is no longer something founders must chase, at least not the ones at the very top. Instead, investors are lining up with preemptive offers, perks, and even the occasional surprise term sheet.    A Market Where Investors Do the Chasing  Decagon AI illustrates this shift. Barely two years old, the company, building AI systems for enterprise decision-making, has raised more than $230 million across four preemptive rounds. Three months after closing a $1.5 billion round, it was fielding unsolicited offers at valuations as high as $5 billion. Investors weren’t just offering capital, they were offering access, experiences, and even hand-delivered “gifts” hiding term sheets.  Other names tell the same story. Anysphere, which develops AI tools for software engineers, doubled its valuation from $9.9 billion to $18 billion within weeks and may rise to $40 billion if growth holds. Anthropic, focused on frontier large language models, secured $13 billion, more than double its original target. Perplexity, the AI-powered search and Q&A platform, closed three rounds in a single year, climbing to $20 billion.  The common thread? The best-positioned AI startups aren’t asking for money; money is chasing them.  The Data Confirms the Trend  Anecdotes aside, the numbers are striking. According to the EY , it shows that VC-backed companies raised $80.1 billion in Q1 2025 , the strongest quarter since 2022. But here’s the crucial detail: without a single $40 billion AI transaction , total funding would have fallen 36%  compared to the prior quarter.  Other signals from the report reinforce the picture:  More than 70% of all VC activity was AI-related .     The number of deals fell , even as capital volumes surged.   Mega-rounds above $100 million declined slightly  from Q4 2024.    In short, this isn’t broad-based venture growth. It’s a wave centered almost entirely on AI, carried by a small set of outsized transactions.  Why This Cycle Feels Different, and Familiar  For seasoned observers, the pattern recalls both the dot-com boom of the late ’90s and the zero-rate surge of 2021: compressed fundraising timelines, valuations doubling in weeks, and investors competing fiercely for allocation.  Yet there are key differences. Today’s pools of capital are larger, and AI is not a niche, it is a general-purpose technology cutting across sectors, from enterprise productivity to healthcare and industrial automation. This breadth gives the cycle more structural weight than previous hype waves. Still, the risk remains the same: when valuations leap ahead of business fundamentals, companies can find themselves boxed into unrealistic growth targets or future down rounds.  Implications for Founders and Investors  For founders, the opportunity is obvious: larger checks, longer runways, and freedom to build. But every valuation is also a promise. Raising at $10 billion means convincing the market you can grow into it. If that trajectory falters, strategic options narrow.  For investors, the challenge is discipline. The pressure to gain exposure to leading AI companies is intense, yet paying any price is rarely a winning strategy. Declining deal volumes show that capital is not indiscriminate, it’s being funneled toward firms with clear product-market fit, credible technology, and paths to liquidity.  What does this mean for the rest of 2025? Three themes stand out:  AI remains the epicenter  of venture funding, driving both infrastructure and early applications.  Selectivity increases : capital will keep concentrating in the frontrunners, while weaker legacy companies face challenges raising.  Valuation discipline matters : the companies that endure will be those that use capital as a tool, not a trophy.  Conclusion  The message is clear. AI has become the defining theme of venture capital in 2025, lifting overall numbers to multi-year highs. But this surge is not evenly distributed, it rests heavily on a handful of megadeals and frontrunner companies.  For founders, the environment offers unprecedented opportunity, but it comes with heightened expectations. For investors, the temptation to chase access must be weighed against fundamentals.  Capital may be moving faster, but building durable businesses still takes time. And in this AI-driven cycle, it will be discipline, not just dollars, that separates long-term winners from short-lived stories.  References list:   Bloomberg News. (2025, September 23). Private jets, box seats and big checks. Investors are doing whatever it takes to get into top AI deals . Bloomberg. https://www.bloomberg.com/news/articles/2025-09-23/vcs-are-scrambling-for-a-piece-of-ai-darlings-like-anthropic-cursor-cognition   Ernst & Young. (2025). Venture capital investment trends . EY. https://www.ey.com/en_us/insights/growth/venture-capital-investment-trends   👉 Interested in driving innovation with VinVentures? Share your venture with us   HERE .

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

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

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