“Mythos’ significant improvement in software engineering-related tasks is a departure from the trend of incremental improvements between consecutive frontier models,” Kotak Institutional Equities said in a note. “These developments could have implications for IT services firms.”
How Mythos differs
What makes Mythos different from its predecessors is not merely better performance, but the nature of the leap. Kotak describes it as a “step-jump” in benchmark performance across software engineering tasks – a break from the incremental gains that had, until now, given the industry some breathing room to adapt.
Anthropic has not released Mythos publicly. Instead, the San Francisco-based AI company is rolling it out through a controlled programme called Project Glasswing, with a closed group of partners that includes Amazon Web Services, Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorgan Chase, the Linux Foundation, Microsoft and NVIDIA.
That limited release is itself a caveat. “Model capabilities are largely unproven in real-world scenarios due to a lack of a public release,” Kotak noted.
Beyond coding, Mythos is being positioned as a formidable cybersecurity tool that, according to Anthropic, outperforms human experts and existing tools. In some cases, it has reportedly identified software bugs that went undetected for decades despite multiple testing cycles. In some ways, it is described as superior to most human cybersecurity engineers.
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Motilal Oswal flagged the significance of this shift. “Mythos shows that model capabilities are moving ahead quickly, with AI now extending beyond coding and ERP into areas like cybersecurity,” the brokerage said.
This broadening of AI’s capability footprint enlarges the surface area of potential disruption for Indian IT firms, which have long relied on labour-intensive models across both software development and managed security services.
How vulnerable are Indian IT giants?
Not all Indian IT firms face equal risk. The critical variable is exposure to application services, also known as custom application development, where agentic software engineering capabilities could drive the sharpest productivity gains, and therefore the deepest headcount implications.
Among Tier 1 names, Infosys carries higher exposure to application services, while HCL Technologies sits at the lower end. The risk calculus is sharper in the mid-tier, where Persistent Systems leads Indian peers in apps exposure.
Kotak estimates a 3-3.5% annual growth headwind for the industry over the next three years. Mythos, if its capabilities translate to real-world deployments, could turn that estimate “from prudent to practical,” the brokerage warned, with further downside if large capability improvements continue in future frontier models.
“The Mythos model provides a firmer foundation for AI disruption-related concerns and could pressurize the valuation multiples of IT services companies,” Kotak said.
There is one structural cushion for incumbents: the complexity of enterprise IT environments. Motilal Oswal points out that large enterprises operate in “brownfield” setups—legacy systems built over 20-30 years—where deploying AI requires integration, data cleanup, and governance alignment, all of which take time.
The contrast with new-age companies is stark. Of the top 20 token users for OpenAI, 90% are new-age companies, indicating that AI deployment remains significantly easier in greenfield, cloud-first environments than in legacy enterprise settings.
Mythos also improves hallucination rates, alignment to user instructions and long-context recall. These factors could meaningfully lift AI adoption in IT services tasks beyond the narrow coding use cases markets have focused on so far.