Three companies in the autonomous vehicle space are raising money for their efforts.
Waymo closed an oversubscribed investment round of $5.6 billion, WeRide launched its initial public offering (IPO) and Pony AI filed for an IPO on Nasdaq, according to press releases and report.
Waymo said in a Friday (Oct. 25) press release that its $5.6 billion investment round was led by Alphabet, with continued participation from Andreessen Horowitz, Fidelity, Perry Creek, Silver Lake, Tiger Global and T. Rowe Price.
The company will use the new funding to welcome more riders in Atlanta, Austin, Los Angeles, Phoenix and San Francisco, and to continue advancing its AI-powered autonomous driving system, Waymo Driver, according to the release.
Waymo is now providing 100,000 paid weekly trips, up tenfold from last year, per the release.
WeRide announced the pricing of its IPO in a Friday press release, saying its underwriters for the offering include Morgan Stanley Asia Limited, J.P. Morgan Securities, China International Capital Corporation Hong Kong Securities Limited, ABCI Security Company Limited, BNP Paribas Securities (Asia) and Tiger Brokers (NZ).
Seeking Alpha reported Friday that WeRide raised about $440.5 million from the IPO, offering about 7.74 million American depository shares priced between $15.50 and $18.50 apiece.
WeRide operates in about 30 cities in seven countries, according to the report.
Pony AI, which filed for an IPO on Nasdaq, has operations in Silicon Valley, Beijing and Guangzhou and runs a fleet of more than 250 robotaxis, per the Seeking Alpha report.
These reports come about two weeks after Tesla unveiled its Cybercab, an autonomous vehicle that is slated for production by 2027 and is aimed at reshaping urban transportation with its sub-$30,000 price tag and $0.40 per mile operating cost.
The driverless taxi was the centerpiece of a Tesla event that showcased various aspects of the company’s vision for the future.
Weeks earlier, Apple made official the cancellation of its self-driving car, months after announcing that it would do so. The tech giant contacted the California Department of Motor Vehicles to cancel its Autonomous Vehicle Program Manufacturer’s Testing Permit, which had been active until April 30 of next year.
Agentic artificial intelligence (AI) promises to improve operational efficiencies and the customer experience offered by enterprises.
The advanced technology is finding applications in loan underwriting and fraud detection, and now it’s moving across borders.
TerraPay Co-Founder and Chief Operating Officer Ram Sundaram told PYMNTS as part of the “What’s Next in Payments” series focused on exploring AI’s use in banking and by FinTechs that automated decision making and streamlined processes will continue to transform global money movement, especially as faster payments gain ground in cross-border transactions. That’s the inexorable trend, but as Sundaram put it, there’s still room, and a necessity, to have some human interaction in the mix.
In terms of global fund flows, TerraPay’s single connection ties more than 3.7 billion mobile wallets together across 200 sending and 144 receiving countries, touching 7.5 billion bank accounts. As one might imagine, coordinating and enabling the transactions is complex.
“Obviously, in the best-case scenario, everything goes smoothly, but when things are not going smoothly, that’s when the customer queries come in,” Sundaram said.
It’s no easy task to find out straight away where a transaction is, as analysts and representatives at the company have to look at logs and query partner systems.
“A lot of that work is done manually,” said Sundaram, who added that the agents “know the corridors and the markets that they are working in, but it still takes some time.”
TerraPay is using AI models with machine learning to bolster customer support and automate tasks as financial institutions (TerraPay’s client base) send payments in real time, and those payments are processed into local markets’ beneficiary banks.
“We still don’t trust [AI models] to let them respond to the customer straight away, but we can do the analysis, and then that gets reviewed by an agent who decides if [information] is accurate or not and then sends it off,” Sundaram said.
The same principles are guiding AI models and company practices to improve technical and security operations, analyzing and categorizing anomalous transactions and automating integrations with partner firms.
“Compliance is an issue where there is a lot of review needed of the alerts, and we are using [AI models] to speed up those processes,” Sundaram said.
Asked by PYMNTS about how agentic AI can be harnessed, he said: “In financial services, you can’t take chances on technology like this, which has the freedom to go wrong. You have to be careful about making sure that it’s 100% reliable before we can let things run entirely by automation.”
Agentic AI also remains pricey. For example, OpenAI is charging $20,000 a month for its specialized agents. However, Sundaram said the industry will become commoditized quickly, which will lower prices, and some open-source offerings are capable.
“There’s a fire hose of news about breakthroughs and new ideas and new ways of doing things that are coming out on a daily basis,” he said.
Data underpins it all, and Sundaram told PYMNTS that no matter what the application, the information fed into the models must be clean. Most organizations have a range of data sitting in different intra-company silos, and those silos need to come down.
In addition, the data must be structured so that it is accessible and can be synthesized by the models. Many firms may have more than 1,000 software-as-a-service (SaaS) resources to which they are subscribed but are not accurately tracked or monitored.
“Every database is separated, each one sitting somewhere else,” he said.
The days of stitching together those separate SaaS offerings to run an enterprise are ending, he said, and we’re headed to a future when data is collected in one place.
AI models and agentic AI “are extensions of what we’ve always valued at TerraPay, which means building the most efficient infrastructure possible in order to make sure that transactions are processed safely, quickly and affordably,” Sundaram told PYMNTS. “We see AI and [AI models] as powerful tools that help us scale all this very quickly while making sure we build more and more efficiency into the system.”
