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For enterprise companies exploring opportunities in conversational artificial intelligence (AI), a new development promises greater access and affordability. Anthropic, a leading AI model lab, today dropped the per-token pricing of its conversational model Claude 2.1 release in response to growing competition from other large players, but perhaps more importantly, open source options.
“As more players have entered the market, it’s just accelerated things,” notes Matt Shumer, CEO and co-founder of OthersideAI. Shumer points out that other entrants like DeepMind put pressure on the closed-source large language model (LLM) firms like OpenAI and Anthropic to continually lower costs.
However, Shumer also believes the true challenge lies in the “proliferation of open source.” As open development of models like Mistral and Poro makes sophisticated AI more widely available, companies can avoid dependency on any single vendor. They gain the flexibility to choose from a growing landscape of options, each with their own advantages.
This changing landscape was likely top-of-mind for Anthropic when lowering Claude’s per-token rates. “If they want to keep the businesses that are growing on their platform, which is how they’re going to succeed, they definitely need to make it cheap enough that they will retain [their client base]. That’s a big problem for them,” Shumer asserts.
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Anthropic clearly sees value in retaining growing enterprise customers invested in conversational AI applications. By making Claude more affordable relative to competitors like OpenAI, Anthropic aims to solidify its position in a maturing market with rising standards of value.
Open source enables customization beyond closed APIs
While affordable pricing expands access, open alternatives offer even greater customization potential. As Shumer notes, “With open AI, they are optimizing for the average use case…we could think about constructing our systems and our servers differently so that they’re much more optimized for that.”
Open source models empower companies to tune infrastructure precisely for their unique needs. This yields exponentially lower costs compared to generalized closed APIs. Shumer estimates open source tools serve intelligence “orders of magnitude cheaper” through such custom optimization.
For enterprises already investing engineering resources in AI, open source represents an attractive value proposition. As Shumer explains, “If you can cut those costs by 10-20x, and you’re a larger company…you’re going to do so.” The ability to fully own one’s AI stack presents a compelling case for ambitious firms seeking competitive advantages.
As a result, open source proliferation poses a distinct future challenge for closed vendors. As Shumer frames it, “I actually see open source proliferation as the more imminent threat to these companies.” Pioneering firms risk losing not just customers but also technical talent drawn to open opportunities unlocking greater potential.
This dynamic informed Anthropic’s decision to competitively lower Claude’s per-token rates. Retaining a foothold in increasingly price-sensitive and tech-savvy markets demands ongoing responsiveness to the open alternative. Closed vendors must walk a fine line balancing affordability with necessity of proprietary business models.
AI leadership requires adaptability in fast-changing landscape
Looking ahead, maintaining leadership in conversational AI will require agility navigating market shifts. Early incumbents like OpenAI pioneered the field but now face disruptions from newer generations of companies. As Shumer observes, “A year ago, two years ago…it was just OpenAI. Now there are many players competing.”
Greater competition accelerates innovation as multiple stakeholders push progress. According to Shumer, “That’s going to drive the prices down…drive the capabilities up because more people are trying to build the same thing.” This rapid evolution promises both challenges and opportunities for enterprise AI adoption plans.
However, OpenAI’s struggles also reveal advantages of the growing open source landscape. Unlike proprietary systems dependent on single vendors, open models distribute responsibility across wider communities. Despite Altman’s saga ending with his return to the company, questions certainly remain.
Success will favor companies demonstrating flexibility responding to emerging alternatives. As the conversation illustrated, even established firms are testing both proprietary and open source approaches depending on tasks. Finding optimal balance positions for organizations for ongoing affordability and technical leadership.
Most importantly, AI leadership increasingly demands actively monitoring a diversifying landscape of options. Taking cues from a maturing industry, enterprises must cultivate perceptiveness for disruptions empowering new models of value. With the promise of open source unleashing further innovations, those embracing change stand to gain decisive long-term advantages.
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