班加罗尔的宣言:印度AI创业的“第二波”叙事与全球竞逐的现实
在班加罗尔面对两千名未来创始人的演讲中,一幅印度AI征服世界的蓝图被清晰勾勒:不是作为外包中心,而是作为世界级AI原生产品的策源地。演讲中充满了对印度工程人才的自信、对“后发优势”的笃信,以及对年轻创始人极速学习能力的推崇。这无疑是激动人心的宣言。但在这股乐观的浪潮之下,我们需要冷静审视:从“卓越的执行者”到“颠覆性的定义者”,印度AI创业生态需要跨越的,远不止是技术能力的鸿沟。全球市场的竞争、本土生态的局限与独特的机遇,正共同塑造着这场雄心之旅的复杂地貌。
核心观点:印度科技领袖所宣扬的“第二波”AI创业浪潮,其核心叙事在于凭借顶尖工程人才、卓越执行力和对技术前沿的快速学习,实现从服务全球到为全球打造原生AI产品的跃迁;然而,这一雄心勃勃的叙事面临着能否突破基础设施依赖、资本深度、基础研究短板以及国内市场特殊性等多重结构性考验,其成功与否将定义印度在全球AI价值链中的最终位置。
在印度硅谷班加罗尔,一场面向两千名未来创业者的演讲,为我们观察全球AI竞赛提供了一个来自南亚的鲜明注脚。演讲者勾勒的,不是关于印度作为传统IT服务巨头的陈旧故事,而是一个充满进攻性的新叙事:印度正站在“第二波”浪潮的起点,这波浪潮的核心是建立面向全球市场的世界级AI原生公司。Emergent和Giga被奉为典范,Zepto作为“后来者居上”的电商案例被反复引用。整个叙事建立在几个关键支柱之上:世界级的工程人才密度、将已有创意执行到极致的“后发优势”、年轻创始人利用AI工具将开发效率提升数个量级的能力,以及一种紧跟全球技术前沿的“捣鼓”文化。这不仅仅是一场鼓舞士气的动员,更是一份试图重新定义印度在全球科技版图中角色的宣言书——从“为世界建造”转向“为世界创造”。
这一叙事的力量首先在于其现实基础。印度拥有庞大且仍在快速增长的工程师群体,其中顶尖人才的素质确实得到了全球科技公司的广泛认可。在AI时代,编码和工程实现能力是产品化的基石,印度在这方面具备显著的人力资源优势。其次,“后发优势”的论点在消费互联网领域已被验证多次。在AI应用层,许多方向的确尚未形成牢不可破的垄断,快速迭代和卓越的用户体验仍能杀出重围。印度创业者对全球(尤其是美国)市场趋势的敏锐捕捉和快速本地化执行,是其重要武器。再者,AI编码工具的爆发,理论上极大降低了个体或小团队打造复杂产品的门槛,这与印度擅长轻资产、敏捷创业的文化不谋而合。年轻创始人对新技术的无畏和快速学习能力,在这个变化极快的领域可能比经验更重要。所有这些因素叠加,使得“第二波”叙事听起来并非空中楼阁。
然而,当我们从激动人心的演讲现场回到全球AI产业残酷的竞争现实中,便会发现这条跃迁之路布满了荆棘。第一个挑战是“基础设施依赖”。当前全球AI创新的核心引擎,如最先进的大语言模型、算力集群、关键框架和数据集,仍然高度集中在美国和中国。印度创业者大多是在这些基础模型之上进行应用开发。这意味着他们的产品创新受制于上游技术的演进路线、API政策和使用成本。要成为真正的“世界级AI原生”公司,而不仅仅是优秀的应用开发者,印度生态需要孕育出自己在基础模型、AI芯片或核心算法层面的突破。这需要长期、巨量的资本投入和深厚的基础研究积累,而这恰恰是当前印度生态相对薄弱的环节。
第二个挑战关乎“市场与资本的悖论”。演讲者强调为“全球市场”打造产品,这无疑是正确的战略,因为单一的印度市场虽然庞大,但其付费能力、数字基础设施的均匀度以及针对AI产品的特定需求,与欧美成熟市场存在差异。瞄准全球市场意味着从一开始就要与硅谷最顶尖的团队同台竞技。这要求印度初创公司不仅能吸引印度的风险投资,更需要获得全球顶级资本的支持和信任,以支撑其烧钱扩张、招募全球人才。虽然印度裔投资人在硅谷影响力日增,但能否系统性、大规模地将全球资本导向印度的早期AI原生创业,仍是一个未知数。资本往往追逐已经验证的模式和生态,印度能否在AI领域复制其在SaaS赛道上的成功,形成强大的正向循环,至关重要。
第三个挑战是“生态系统的完整性”。世界级的AI创新不仅需要工程师和创业者,还需要顶尖的研究机构、活跃的开源社区、成熟的数据市场、完善的法律法规(特别是数据隐私和AI伦理),以及能够承担高风险、支持长期研发的资本环境。印度的理工学院(IITs等)培养了优秀的工程人才,但在引领全球AI基础研究方面,声音仍相对微弱。构建一个从研究到产业化的完整良性循环,非一日之功。此外,印度国内复杂的多语言环境、数字鸿沟以及独特的监管环境,既可能催生针对本土痛点的创新(例如多语言AI助手、农业科技AI),也可能使产品在全球化过程中面临额外的适配和合规成本。
演讲中提到的“捣鼓”文化是一把双刃剑。它鼓励快速实验和灵活应变,这无疑是应用创新的催化剂。但AI领域的突破性创新,往往也需要“坐冷板凳”的长期主义,需要对底层技术原理的深刻理解,而不仅仅是快速组合现有的API。如何在鼓励“捣鼓”的同时,培养和留住那些愿意投身于更底层、更长期、不确定性更高的技术探索的人才,是印度AI生态能否实现阶层跃升的关键。
因此,“第二波”印度AI创业潮的真正考验,在于其能否完成从“卓越执行”到“范式定义”的惊险一跃。他们有可能在多个垂直应用领域(如教育科技、金融科技、开发者工具、企业软件)诞生出一批成功的全球性公司,凭借出色的工程实现、成本优势和产品化能力占据重要市场份额。这本身已是巨大的成功。但要诞生像OpenAI、Anthropic这样定义技术范式的公司,或者像英伟达这样定义硬件生态的公司,印度还需要在基础层进行更雄心勃勃的布局和投资。这需要政府、学术界、产业界和资本形成更紧密的战略合力。
班加罗尔的宣言是一个强烈的信号,表明印度科技精英不再满足于产业链的特定环节,而是渴望在AI时代争夺话语权。这场竞逐将异常激烈。美国的先发优势、中国的市场与数据规模、欧洲的监管与伦理话语权,都是强大的对手。印度的机会在于其独特的人才基数、日益增长的国内市场、与英语世界的天然联系以及在全球 diaspora 的网络。最终,“第二波”的成功将不仅由诞生多少家独角兽来衡量,更将由印度能否在全球AI的价值链、知识链和创新链上,占据更核心、更不可替代的位置来决定。这场始于班加罗尔的远征,注定是一场关于技术、资本、生态和野心的综合较量。
参考来源
- RT by @paulg: What I told 2,000 future founders in Bengaluru today:
- 1/ We believe we are at the start of a second wave of Indian companies that will build world-class AI native products for the global market. Emergent and Giga are the model of the future.
- 2/ Just because a space seems crowded doesn't mean it's too late. Zepto, Emergent, Giga - none were first movers. Second mover advantage is real.
- 3/ In fact, a good formula for finding startup ideas is to look at ideas that are showing some promise and just execute them better. Execution is everything: if you're an exceptional engineer, and you can build and move faster than your competitors, you'll win.
- 4/ There is every reason to believe Indian teams can beat US teams building global products. The level of engineering talent here is on a whole different level, and that's the key input.
- 5/ In the AI era, the best founders are the ones building at the edge of what's technically possible. You need to be experimenting wth the latest models, the latest open source projects.
- 6/ Stay in the flow of information. Watch the right podcasts, follow the right people on X. With AI changing this fast, you need to know what the smartest builders are thinking.
- 7/ Most of the best startups don't come from someone explicitly trying to start a company. They start from someone building a project just for fun, or tinkering with a new technology because they are curious. India needs more of this "tinkering" culture - this is how you have novel ideas when technology is shifting quickly.
- 8/ Founders are getting younger. Aadit was 18 when he started Zepto. The Giga founders were 20 when they came to SF. Young people who can learn very fast have the advantage right now.
- 9/ The best founders are pushing AI coding to the max. You can now write 20K lines of code / day. One person can do the work that just a year ago would take a 100 person team. The best builders are taking advantage and building at Garry Tan speeds. - https://nitter.net/snowmaker/status/2045506195415535872#m
- 《重返未来:1999》三周年特别版本·3.7版本PV:他者的悲哀 - https://www.bilibili.com/video/BV1MPdBB8EEN
- RT by @paulg: A new paper in @Nature from David Reich, @aliakbari23 and colleagues breaks the conventional understanding of recent human evolution. The field believed that strong selection in the recent past (~10,000 years) was rare, with few exceptions like the lactase persistence locus. In this paper, the authors challenge that belief, showing that we weren't looking at the problem right.
- Previous studies that looked for evidence of selection using ancient DNA addressed the problem cross-sectionally, asking if allele frequencies differed across populations more than what one would expect based on genetic drift and migration. Most arrived at the conclusion that population structure primarily explained the observed differences. Here, the authors addressed the problem longitudinally, accounting for when ancient individuals lived by explicitly modeling time as a variable in the analysis. It turns out doing it this way dramatically increases power, increasing the number of genome-wide significant selection signals by 20-fold!
- Looking at why accounting for the time variable led to such dramatic changes in results, the authors find that previous studies missed so much because selection often happened not on new variants leading to dramatic sweeps (the conventional model: new variant -> selection -> increase in frequency) but on already existing variants driven by transient environmental pressures. Many of these variants underwent reversals, selected up when a pressure existed, then purged when it disappeared or the trade-off cost became dominant. A great example is the TYK2 variant, where an allele boosting immunity was selected for thousands of years because it protected against TB, then got purged as TB endemicity declined and the autoimmune cost took over.
- The scale of what they found is striking: hundreds of loci showing strong selection in the past 10,000 years with a median selection coefficient of ~0.86%. This number is pretty big in evolutionary terms, meaning allele frequencies have been shifting by ~1% per generation in a consistent direction. Previous selection scans found a maximum of 20 loci, and this one finds hundreds. That isn't an incremental change. It fundamentally reframes our understanding of how common strong selection has been in recent human history.
- Some of the most striking findings come from polygenic selection, where hundreds of small-effect alleles were pushed in the same direction simultaneously. Polygenic scores based on large-scale GWAS of today predict recent negative selection for traits like body fat, waist circumference and schizophrenia, and positive selection for others like cognitive traits. One important caveat is that GWAS phenotypes are measured in industrialized societies today, and how well they capture what was actually being selected in ancient environments is debatable.
- For me personally, these findings have direct implications for drug discovery. When using human genetics to find drug targets, we often fixate on the benefit and risk profiles of variants visible today. But we need to be aware that a variant's benefit:harm ratio might be environmentally contingent, and could reverse when the wrong environment manifests. An evolutionary understanding of a variant's association with traits is therefore essential.
- The same logic applies, perhaps even more urgently, to embryo selection. Selecting embryos based on polygenic traits is humans making permanent, heritable decisions for their offspring with a narrow view of today's environment. The ancient DNA record now shows that cost-benefit landscapes flip over time. So, an embryo carrying man-made selections is carrying those changes into an unpredictable future environment.
- The broader takeaway is that human evolution didn't freeze in the last 10,000 years. We just lacked the tools and datasets to see its movement. The current findings are based on European populations. I am curious to see these analyses extended to other populations too, like South Asian, East Asian and African populations, which might be holding more surprises to blow our minds.
- Akbari et al. Nature 2026
- https://www.nature.com/articles/s41586-026-10358-1 - https://nitter.net/doctorveera/status/2044679999450664967#m