Silicon Valley kingmakers

Meanwhile, the investor lineup reads like a who’s who of Silicon Valley’s kingmakers. Sequoia’s Roelof Botha and “solo GP” Elad Gil represent the kind of money that moves markets and shapes entire industries. Dramatic as it may sound, their funding decisions often preview which technologies will dominate enterprise conversations within two years, making their perspectives essential intelligence for anyone planning technology strategy.

The programming extends well beyond AI and public markets. The CEO of Waymo will showcase how autonomous systems are reshaping transportation, while Netflix’s CTO will provide a rare glimpse into the streaming infrastructure that powers global entertainment. Perhaps most intriguingly, Kevin Rose—who founded Digg, sold it, then recently rescued it from corporate ownership—will discuss the art of platform resurrection in an era of constant digital disruption.

Disrupt takes place as both TechCrunch and San Francisco reassert their respective primacies — the publication as tech journalism’s defining voice, the city as technology’s undisputed capital. It also promises to be entertaining, as these events always are.

Artificial intelligence (AI) could be a useful tool to find ‘questionable’ open-access journals, by analysing features such as website design and content, new research has found.

The researchers set out to evaluate the extent to which AI techniques could replicate the expertise of human reviewers in identifying questionable journals and determining key predictive factors. ‘Questionable’ journals were defined as journals violating the best practices outlined in the Directory of Open Access Journals (DOAJ) – an index of open access journals managed by the DOAF foundation based in Denmark – and showing indicators of low editorial standards. Legitimate journals were those that followed DOAJ best practice standards and classed as ‘whitelisted’.

The AI model was designed to transform journal websites into machine-readable information, according to DOAJ criteria, such as editorial board expertise and publication ethics. To train the questionable journal classifier, they compiled a list of around 12,800 whitelisted journals and 2500 unwhitelisted, and then extracted three kinds of features to help distinguish them from each other: website content, website design and bibliometrics-based classifiers.

The model was then used to predict questionable journals from a list of just over 15,000 open-access journals housed by the open database, Unpaywall. Overall, it flagged 1437 suspect journals of which about 1092 were expected to be genuinely questionable. The researchers said these journals had hundreds of thousands of articles, millions of citations, acknowledged funding from major agencies and attracted authors from developing countries.

There were around 345 false positives among those identified, which the researchers said shared a few patterns, for example they had sites that were unreachable or had been formally discontinued, or referred to a book series or conference with titles similar to that of a journal. They also said there was likely around 1780 problematic journals that had remained undetected.

Overall, they concluded that AI could accurately discern questionable journals with high agreement with expert human assessments, although they pointed out that existing AI models would need to be continuously updated to track evolving trends.

‘Future work should explore ways to incorporate real-time web crawling and community feedback into AI-driven screening tools to create a dynamic and adaptable system for monitoring research integrity,’ they said.

There’s little doubt that Palantir Technologies (PLTR -0.19%) is one of the most significant stock market stories of the decade, so far. The data mining company unveiled its Artificial Intelligence Platform (AIP) in 2023 and since has been climbing fast.

Palantir jumped 340% in 2024, making it the best-performing stock in the S&P 500, and its 118% gain so far this year puts it at a close second to Seagate Technology for 2025. An investment in Palantir of just $1,000 three years ago would have given you $21,000 today.

PLTR data by YCharts

Undoubtedly, people are looking for the next Palantir, and for many, BigBear.ai (BBAI 0.59%) is a contender. Like Palantir, BigBear.ai is a government contractor that is using artificial intelligence (AI) to develop solutions for defense and intelligence agencies.

Image source: Getty Images.

But if you’re hoping BigBear.ai can match Palantir, I think you’ll be mistaken. There are three other names you should consider instead to play the AI space.

BigBear.ai isn’t another Palantir

Palantir is growing so fast because it’s reeling in contracts hand over fist. It closed $2.27 billion in total contract value sales in the second quarter, up 140% from last year. Its customer count grew 43% for the quarter. That’s why the company’s revenue growth is so steep — it’s gone from about $460 million per quarter to $1 billion a quarter in just three years.

BigBear.ai, however, had revenue of just $32.4 million in the second quarter, down 18% from a year ago. Management said the drop was because of lower volume of U.S. Army programs, but that also shines a spotlight on the company’s biggest problem. BigBear.ai’s biggest contract is with the Army, a $165 million deal to modernize and incorporate AI into its platforms. If the Army slows down its work for any reason, then BigBear.ai and its stock suffer.

So, what AI companies are a better play than BigBear.ai now?

Palantir Technologies

I completely understand wanting to get in on the next Palantir, but I also see a lot of value in investing in the original. While BigBear.ai has to create new platforms and new products for each of its clients, Palantir’s AIP is designed to work with multiple government agencies and commercial businesses.

Palantir rolls out AIP in boot camps so potential customers can try it out, and the results speak for themselves — the company closed 157 deals in the second quarter that were valued at $1 million or more. Sixty-six of those were more than $5 million in value and 42 were more than $10 million. BigBear.ai can’t do that.

International Business Machines

International Business Machines (IBM 1.15%) wins my vote in the AI space because of a bet that Big Blue made six years ago. The venerable computing company that was perhaps best known for its work in personal computing spent $34 billion in 2019 to purchase Red Hat, an open-source enterprise software company, in order to develop its hybrid cloud offerings. The hybrid cloud combines public cloud, private cloud, and on-premises infrastructure, which gives customers flexibility to keep parts of their data secure while utilizing cloud services.

IBM layers its hybrid cloud with its Watsonx, which is its portfolio of artificial intelligence products, which includes a studio to build AI solutions, virtual agents, and code assistants powered by generative AI.

IBM saw software revenue of $7.4 billion in its second quarter, with the hybrid cloud revenue up 16% from a year ago.

“Our strategy remains focused: hybrid cloud and artificial intelligence,” CEO Arvind Krishna said on the Q2 earnings call. “This strategy is built on five reinforcing elements — client trust, flexible and open platforms, sustained innovation, deep domain expertise, and a broad ecosystem.”

Amazon

I love Amazon (AMZN 1.44%) — not because I get packages delivered to my house every week (its e-commerce division makes shopping incredibly convenient), but because of Amazon Web Services (AWS).

AWS holds first place in global market share for cloud computing, with a 30% share. Its Amazon Bedrock platform allows customers to use generative AI to build and experiment with AI-powered products. And because it operates on Amazon’s powerful cloud, users don’t need to invest in expensive graphics processing units (GPUs) or data centers of their own.

AWS was responsible for $30.87 billion in revenue and $10.16 billion in operating income. That profit margin is hugely important, as Amazon’s net income for the quarter was just $18.16 billion — AWS accounts for more than half of the company’s profit despite being responsible for just 18% of the company’s revenue.

In addition, Amazon’s advertising business is growing in importance. It’s using machine learning to deliver targeted product ads, making it one of Amazon’s most profitable efforts. Advertising services revenue jumped to $15.6 billion in the second quarter, up 22% from a year ago.

E-commerce is where Amazon made its mark, but AI  is where Amazon will carve its future.

The bottom line

AI is going to shape our future for years to come. While BigBear.ai is making efforts, not everyone can be a winner. Pass on BigBear.ai for now and focus on established companies that are not only proven winners, but also have a broad runway for growth.