When Google and venture firm Accel reviewed more than 4,000 applications for their joint India-focused AI accelerator, a clear pattern emerged: many submissions were little more than surface-level \u201cwrappers\u201d built on top of existing models. The Atoms program ultimately selected five startups for its latest cohort \u2014 none of them wrapper-style products \u2014 signaling investor preference for deeper, workflow-changing AI solutions. \ud83d\udd0d<\/p>\n
According to Accel partner Prayank Swaroop, roughly 70% of rejected applications were \u201cwrappers\u201d \u2014 companies that added chatbots or AI features to existing software without reimagining the underlying workflow. Investors are increasingly wary of such startups because as model providers add features, these thin layers can quickly become redundant.<\/p>\n
Many other rejected applications landed in crowded verticals like marketing automation and recruitment tools, where differentiation is difficult and novelty is limited.<\/p>\n
The Atoms program, announced in November, backs early-stage Indian startups building AI products tied to India. Selected startups receive up to $2 million in funding from Accel and Google\u2019s AI Futures Fund, plus up to $350,000 in Google cloud and AI compute credits. The program is designed to help startups scale while feeding real-world insights back to model developers.<\/p>\n
The submissions skewed heavily toward enterprise use cases: about 62% targeted productivity tools and another 13% targeted software development and coding \u2014 meaning roughly three-quarters focused on enterprise software rather than consumer products. Organizers had hoped for more entries in healthcare and education, sectors that remain underrepresented.<\/p>\n
Jonathan Silber of Google\u2019s AI Futures Fund said the program doesn\u2019t require startups to use Google models exclusively. Many teams combine multiple models depending on the workflow. The goal is to learn how models perform in production \u2014 and to create a feedback loop where startup experimentation helps improve future models at Google DeepMind. Silber framed this relationship as a \u201cflywheel\u201d between real-world use and model development. \u2699\ufe0f<\/p>\n
The Atoms cohort and selection criteria offer a useful signal for the broader AI startup landscape: depth, defensibility, and domain-specific value still matter. For Indian founders, the message is clear \u2014 build beyond wrappers and show how AI fundamentally improves workflows and outcomes. \ud83d\ude80<\/p>\n","protected":false},"excerpt":{"rendered":"
Google and Accel\u2019s Atoms program chose five Indian AI startups focused on deep, practical AI \u2014 rejecting superficial \u201cwrapper\u201d apps. Read who made the cut and why investors are pushing for genuine AI-driven workflows. \ud83d\ude80\ud83e\udd16\ud83c\uddee\ud83c\uddf3<\/p>\n","protected":false},"author":1,"featured_media":329,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"tdm_status":"","tdm_grid_status":"","footnotes":""},"categories":[1],"tags":[],"class_list":{"0":"post-326","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-tech-news"},"yoast_head":"\n