MARKETS

Perplexity, OpenAI voted flop risks at AI conference

Perplexity and OpenAI may have gotten the worst superlatives AI companies can get.

At the Cerebral Valley AI Conference this weekend, an informal survey of more than 300 attendees organized by journalist Eric Newcomer found that Perplexity, followed by OpenAI, was voted most likely to flop. 

These companies, along with the rest of the AI market, seek to cement strong enough revenue streams to break even. But the sentiment that Perplexity and OpenAI may struggle with this is “spot on,” Scott Bickley, advisory fellow at Info-Tech Research Group, told The Deep View. However, these firms may topple for different reasons.

Perplexity’s success hinges on getting consumers to actually buy into its services, said Bickley. But the startup also relies on other foundational models, including those from OpenAI, Anthropic and Google. 

And with so many other options clouding the consumer market, namely the ever-popular ChatGPT, “the question is, what makes me want to go to Perplexity and pay to have the benefits of their deep search capabilities versus what is provided in Google with Gemini for free?”  

While OpenAI’s situation is far more complex, given the $1.4 trillion in AI infrastructure commitments it’s signed onto, the company still faces the “conundrum on the consumer side,” said Bickley. “There's going to be limited dollars that someone's willing to spend on a subscription,” he said. 

The value add for a consumer has to be significant, said Bickley, especially in OpenAI’s case. In order to emerge from the red, OpenAI may need to become a “super app,” he added, offering multiple functionalities in one ecosystem, such as ecommerce, communication and entertainment. 

And this may be part of its grand plan: In an interview with Wired published Monday, OpenAI’s CEO of Applications, Fidji Simo, said ChatGPT could “give everyone a team,” including a personal shopper, a travel agent, a financial adviser, a health coach and more, wrapped into one. “If we build that, I assume that people are going to want to pay a lot of money for that, and that revenue is going to come.” 

OpenAI and Perplexity could take the route well trodden by its tech forefathers in making money off consumers: Advertising. Digital ads are how Meta and Google have made the billions upon billions of dollars that support their own expensive AI endeavors. Ramping up those efforts could help these firms gain returns far more quickly than they would from subscription revenue. 

But OpenAI in particular faces a far more daunting task. Given the sheer amount of money it’s signed on to spend, even with its efforts spread across consumer and enterprise plans, Bickley noted, “how many hundreds of millions of users do you need to bring those revenues up to a point where (its investments) make sense?”

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RESEARCH

AI starts to think smaller

Bigger isn’t always better. 

As large language models continue to add zeros to their parameter counts (and their costs), some developers may be setting their sights smaller. Small models are gaining traction as companies seek to cut costs and focus on niche AI applications. 

While large language models have the benefit of general intelligence, a jack of all trades is a master of none. Small models, or those with parameter counts in the millions to low billions rather than the trillions, may outperform larger ones at niche tasks. 

• These models have become a focus area for both Big Tech companies and startups, with Meta, Microsoft, Google and OpenAI all debuting their own mini offerings. 

• Investors have taken a liking to the space, too: Companies like Mistral AI, Liquid AI and Sakana (which closed a $135 million Series B round on Monday) have all hit unicorn status as they rake in funding for lightweight and niche models. 

And research from IBM released in recent weeks November, shows that these models may also be better at staying focused. 

When comparing how small language models performed against their larger counterparts on a number of financial-related tasks, the small models were able to avoid drift, achieving “100% output consistency,” challenging the common belief “that larger models are universally superior for production deployment.” 

The number one reason that these models are so popular is cost, Dan Herbatschek, CEO and founder of Ramsey Theory Group, told The Deep View. These models are both cheaper to build and run, often requiring fewer GPUs and the capability to operate with CPUs or mobile hardware. “This fits well with a small AI budget,” he said. 

These models also tend to be more secure and less resource-intensive, making them easier to deploy in agents and edge cases where compute power is limited, Chuan Qiu, founder of Velda, told The Deep View. “Being real-time (and) protecting privacy are crucial for these cases,” Qiu noted.

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MARKETS

Investors turn on Nvidia

Major investors may be backing away from Nvidia. 

Peter Thiel’s hedge fund, Thiel Macro, zeroed out its stake in Nvidia in the third quarter, selling off more than 537,000 shares, a pot worth $94 million, according to regulatory filings reported on Monday. 

The move follows similar selloffs in the past week: 

• SoftBank sold off its entire stake in Nvidia, around 32.1 million shares worth more than $5.8 billion. The funds will reportedly be used to support a $22.5 billion investment in OpenAI, according to CNBC. 

• And Scion Asset Management, the fund run by Michael Burry, who gained his fame predicting and profiting from the subprime mortgage crisis in 2008, revealed $1.1 billion in put options for Nvidia and Palantir stocks. 

Nvidia has largely been the biggest winner of the AI race thus far. Its hotly demanded chips have boosted its revenue to astronomical heights, with Jensen Huang revealing in October that the company has $500 billion in orders combined for 2025 and 2026 at the company’s GTC conference in Washington, and briefly surpassed a record-breaking $5 trillion market cap in late October. 

But as discussions of an AI bubble grow, these sell-offs could be a sign that faith is beginning to waver. Investors have raised concerns that the circular financing deals between Nvidia, OpenAI and other tech firms are artificially propping up the industry.  

And tech leaders themselves have been flagging concerns of Nvidia’s involvement in the burgeoning bubble: On Monday, Klarna CEO Sebastian Siemiatkowski told The Financial Times that Nvidia’s stock boom makes him “nervous” due to “the amount of wealth that is currently automatically allocated into this trend, without some more thoughtful thinking.”

LINKS

• Fintech firm Ramp hits $32 billion valuation after $300 million funding round

• DeepMind unveils faster, more accurate AI weather model, targeting energy traders

• Cloudflare acquires AI platform Replicate for an undisclosed sum

• Nvidia, ARM partner on AI chip, custom infrastructure integration

• CISA will increase hiring efforts in 2026 in preparation for China conflict

• AI Super PAC targets New York Democrat Alex Bores over AI safety legislation

• Grok 4.1: A new frontier model from xAI, pushing standards for conversational intelligence, emotional understanding and “real-world helpfulness.”

• Google AI Flight Deals: Google has embedded AI into its flight booking platform and added the ability to organize travel plans using its Canvas AI tool.

• Flora V2: A new model to power an AI creative canvas for workflows, designs and campaigns

• Castari: An AI agent builder that manages your infrastructure to deploy agents from sandbox to production.

• Blimp: A no-code workflow automation tool, allowing non-technical users to leverage AI for their busywork.

• Anthropic: Privacy Research Engineer, Safeguards

• Capital One: Applied Researcher I (AI Foundations)

• SoFi: Senior AI Specialist - Gen AI, NLP

• Amazon: Sr AI Language Engineer, Amazon Rufus

GAMES

POLL RESULTS

Will AI coding tools like Cursor and Lovable ultimately create more coding jobs than they eliminate?

• Yes (20%)

• No (30%)

• Break even (22%)

• Too early to tell (28%)