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Okta plans to weave AI across its entire identity platform using multiple models

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One thing is clear this year: generative AI is having a tremendous impact on the software industry, and a week doesn’t pass without software companies announcing their plans to incorporate the seemingly game changing technology into their platforms.

This week, it’s Okta’s turn. The identity company is making a slew of AI-related announcements at the company’s Oktane customer conference, taking place this week in San Francisco.

“I think AI is the next big wave in technology. I think it’s as important and big and impactful as the internet, as cloud, as mobile,” Okta CEO Todd McKinnon told TechCrunch.

For Okta, that means training a model on all of the data it’s been collecting about identity and putting that to work to help make customers safer. “We have a new set of capabilities we’re launching called Okta AI,” he said. “It’s taking all of the really, really valuable data we have from risk signals and usage patterns and customers and policies, and combining them with the latest and greatest AI technology.”

As McKinnon points out, Okta AI isn’t a product per se, so much as a set of capabilities that will be added over time across the platform, some of which will incorporate predictive AI to help security teams understand possible threats, and some of which will allow users to interact with the data to pinpoint problems more easily using generative AI.

While this will involve many different pieces, he highlighted three in particular. The first, Identity Threat Protection looks at traditional identity protection, checking for things like the computer, network, location and other clues to ensure the person signing in, is who they say they are — but it doesn’t stop there.

“With Identity Threat Protection with Okta, AI, the evaluation of your security posture doesn’t stop. It’s continuously getting signals from the entire ecosystem — from CrowdStrike or Palo Alto Networks or Zscalar or any anyone else — and this is integrated with identity fraud protection. The second that there’s any kind of risk anywhere, whether it’s malware on the device or a bad network security posture, [it can identify it],” McKinnon said.

And if it finds something that passes a particular threat threshold, regardless of which system it came from, Okta or one of its partners, it will undertake what McKinnon is calling a ‘universal logout,’ logging the user out from every system until security can resolve the problem.

Next, Policy Recommender proposes an application security configuration based on similar use cases across Okta’s 18,000+ customer base. “You want to get the right balance between ease of use, without checking too much, and still making it secure, especially when the application is sensitive. So Policy Recommender is trained on the policies of thousands and thousands of customers and how they set up these apps,” he said. It uses that data to recommend a policy for each customer, based on their requirements and security posture.

Finally, Log Investigator is a more pure generative AI play, letting users query the Okta logs using natural language to find information. “The basis of this technology is a generative model that looks at all of the queries that people are issuing against the Okta logs to ask questions, and it trains the model on those queries,” he said. “So then the result of it is a natural language interface so customers can just ask questions and the Okta system will respond with answers based on what’s in their logs.”

McKinnon says the company is using a combination of models, depending on the task, including Google, OpenAI and Amazon. The company could also develop its own model in the future, one that will likely be based on open source offerings, he said.

Ray Wang, founder and principal analyst at Constellation Research, says in the future there will be an ongoing security battle with both sides using AI to gain an advantage, and security and identity companies like Okta have little choice but to get on board.

“Customers know that in the future, AI will be battling AI. So this is just the beginning, and they are expecting their identity providers to be able to handle attacks from other AI systems, as well as proactively preparing,” Wang told TechCrunch.

With these and other announcements, Okta is clearly moving toward this world, but it will be judged on how well it executes on these ideas, while protecting customer privacy. For now, these and other AI features being announced this week will go into beta in the coming months, and be generally available sometime next year.



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Affirm launches in the UK, as ‘buy now, pay later’ market faces regulatory overhaul

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Buy now, pay later (BNPL) giant Affirm is launching in the U.K., its first market outside North America.

Its long-anticipated arrival comes as U.K. lawmakers mull new rules to bring BNPL firms into line with other traditional consumer credit services, though such laws aren’t expected to come into effect until at least 2026 — long enough for Affirm to build traction, and curry favor with consumers and regulators alike.

Founded in 2012, Affirm emerged from a startup incubator called HVF, setup by PayPal co-founder Max Levchin (pictured above) who eventually took the reins at Affirm in 2014 to drive its commercial push. The company expanded beyond the U.S. and into Canada in 2022, and it has struck lucrative partnerships with major ecommerce companies through the years — Affirm has been Shopify’s major financing partner for close to a decade, not to mention Walmart, and Amazon, which tapped Affirm as Amazon Pay’s first BNPL partner in the U.S. last year. More recently, Affirm also secured the mighty Apple as a customer.

‘Normalizing debt’

The BNPL model is simple: customers are invited to purchase goods on credit, repaying the debt in several interest-free instalments, with the BNPL provider monetizing through merchant fees. Or, where the customer may require a longer repayment period, the loan may include interest, too.

The BNPL market has long been on the U.K. regulatory radar, with incumbents such as Klarna and Clearpay often criticized for encouraging impulse buying and normalizing debt. The U.K.’s Financial Conduct Authority (FCA) has hitherto had some power to keep BNPL providers in check, but there are key exemptions, such as services that involve interest-free credit, where fixed-sum agreements stipulate that debts be repaid within 12 months.

But new rules in the works could bring BNPL companies fully in line with other consumer credit companies. The Labour government last month announced a fresh BNPL consultation, with plans to introduce regulation to “ensure people using BNPL products receive clear information, avoid unaffordable borrowing, and have strong rights when issues arise.”

It’s clear that Affirm is already pushing to position itself favorably both with patrons and the the powers-that be. Indeed, the company notes for the U.K. launch that its interest-bearing payment options won’t involve compound interest — instead, interest will be fixed, and calculated entirely on the original amount borrowed.

It’s also worth noting that Klarna started charging late fees in the U.K. last year, and this is one area where Affirm is setting out to differentiate — it says it won’t be charging late fees or any other “hidden charges.”

Head-to-head

It has been a bumpy few years for the BNPL sector. Klarna was valued at more than $45 billion in 2021, a figure that swiftly plummeted by 85% to $6.5 billion following the great post-pandemic “correction” many companies endured — however, news emerged last week that Klarna’s valuation has risen again to $14.6 billion. It has been a similar turbulent time for Affirm, whose ups and downs have followed a trajectory reminiscent of its European rival.

Following its 2021 IPO, Affirm saw its market cap hit the giddy heights of $47 billion, but its stock took a giant hit, with its market capitalization dropping below $3 billion last year. However, Affirm’s shares have surged to more than $13 billion in 2024, with the NASDAQ-listed company recently reporting a Q4 year-on-year revenue jump of 48%, and losses dropping from $206 million to $45 million. Levchin also predicted profitability in 2025.

We’ve known for some time that the U.K. was likely going to be Affirm’s next port-of-call outside the U.S. and Canada, with the firm’s chief revenue officer Wayne Pommen going on record to say that it would be targeting markets where some of its largest existing partners already have a presence.

For its U.K. launch, there aren’t any of the same big-name brands it has domestically, but the fact that it counts the likes of Amazon, Shopify, and Apple as customers in the U.S. means that it wouldn’t be a huge stretch to expand such commercial partnerships to the U.K. For now, though, Affirm is going to market with the like of flight booking site Alternative Airlines and payments processor Fexco, with “additional UK and international brands expected to follow.”

In the build up to today’s launch, Affirm told TechCrunch that it has already hired in the region of 30 employees, including Ruth Spratt who’s leading the local charge, while it’s also looking to add to its headcount through the remainder of the year. And similar to its remote-first ethos elsewhere, workers aren’t tethered to a particular physical hub.

The company wouldn’t confirm its next plans for growth in Europe or elsewhere, though it said that it would be “taking the same disciplined approach” that it has always done to any future expansion.



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OpenAI has hired the co-founder of Twitter challenger Pebble

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Gabor Cselle, the former CEO and co-founder of X challenger Pebble, has joined OpenAI to work on a secretive project.

Cselle, who according to LinkedIn has been employed at OpenAI since October, announced the news in a post on X yesterday. “Will share more about what I’m working on in due time,” he wrote. “Learning a lot already.”

Cselle is a repeat founder who sold his first company, the Y Combinator-based mobile email startup reMail, to Google. His second company, the native advertising startup Namo Media, he sold to Twitter before Elon Musk purchased the social network and rebranded it to X.

Nearly a decade ago, Cselle worked at Twitter as a group product manager, focusing on the home timeline, user onboarding, and logged-out experiences. Cselle left Twitter in 2016 for Google, where he was director at the tech giant’s Area 120 incubator for spin-offs.

Cselle began working on Pebble, originally called T2, in 2022 with Michael Greer, Discord’s ex-engineering head. Pebble, whose microblogging service emphasized safety and moderation, grew to a small but engaged community and raised funding from angles including Android co-founder Rich Miner.

Ultimately, though, Pebble struggled to maintain meaningful growth. The company shut down last October, reemerging as a Mastodon instance in November.

In May, Cselle joined the accelerator South Park Commons, where he worked on a range of generative AI prototypes including an homage to the viral HQ Trivia.

Csell’s hiring reveal comes the same weekend as OpenAI rival Anthropic gains its own high-profile recruit: Embark founder Alex Rodrigues. Rodrigues, who led autonomous trucking firm Embark through a SPAC merger in 2021 (and subsequent fire sale to Applied Intuition in 2023), said on Friday that he’d be joining Anthropic as an AI safety researcher.



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Women in AI: Sophia Velastegui believes AI is moving too fast

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As a part of TechCrunch’s ongoing Women in AI series, which seeks to give AI-focused women academics and others their well-deserved (and overdue) time in the spotlight, TechCrunch interviewed Sophia Velastegui. Velastegui is a member of the National Science Foundation’s (NSF) national AI advisory committee and the former chief AI officer at Microsoft’s business software division.

Velastegui didn’t plan on having a career in AI. She studied mechanical engineering as a Georgia Tech undergrad. But after a job at Apple in 2009, she became fascinated by apps — especially AI-powered ones.

“I started to recognize that AI-infused products resonated with customers, thanks to the feeling of personalization,” Velastegui told TechCrunch. “The possibilities seemed endless for developing AI that could make our lives better at small and large scale, and I wanted to be a part of that revolution. So I started seeking out AI-focused projects and took every opportunity to expand from there.”

AI-forward career

Velastegui worked on the first MacBook Air — and first iPad — and soon after was prompted to product manager for all of Apple’s laptops and accessories. A few years later, Velastegui moved into Apple’s special projects group, where she helped to develop CarPlay, iCloud, Apple Maps, and Apple’s data pipeline and AI systems.

In 2015, Velastegui joined Google as head of silicon architecture and director of the company’s Nest-branded product line. After a brief stint at audio tech company Doppler Labs, she accepted a job offer at Microsoft as general manager of AI products and search.

At Microsoft, where Velastegui eventually came to lead all business app-related AI initiatives, Velastegui guided teams to infuse products such as LinkedIn, Bing, PowerPoint, Outlook, and Azure with AI. She also spearheaded internal explorations and projects built with GPT-3, OpenAI’s text-generating model, to which Microsoft had recently acquired the exclusive license.

“My time at Microsoft truly stands out,” Velastegui said. “I joined the company when it was in the midst of huge changes under CEO Satya Nadella’s leadership. Mentors and peers advised me against making that jump in 2017 because they viewed Microsoft as lagging in the industry. But in a short window, Microsoft had started making real headway in AI, and I wanted in.”

Velastegui left Microsoft in 2022 to start a consulting firm and head product development at Aptiv, the automotive tech company. She joined the NSF’s AI committee, which collaborates with industry, academia, and government to support basic AI research, in 2023.

Navigating the industry

Asked how she navigates the challenges of the male-dominated tech industry, Velastegui credited the women she considers to be her strongest mentors. It’s important that women support each other, Velastegui says — and, perhaps more importantly, that men stand up for their female co-workers.

“For women in tech, if you’ve ever been part of a transformation, adoption, or change management, you have a right to be at the table, so don’t be afraid to take your seat there,” Velastegui said. “Raise your hand to take on more AI responsibilities, whether it’s part of your current job or a stretch project. The best managers will support you and encourage you to keep pushing ahead. But if that’s not feasible in your 9-5, seek out communities or university programs where you can be part of the AI team.”

A lack of diverse viewpoints in the workplace (i.e. AI teams made up mostly of men) can lead to groupthink, Velastegui notes, which is why she advocates that women share feedback as often as they can.

“I strongly encourage more women to get involved in AI so our voices, experiences, and points of view are included at this critical inception point where foundational AI technologies are being defined for now and the future,” she said. “It’s critical that women in every industry really lean into AI. When we join the conversation, we can help shape the industry and change that power imbalance.”

Velastegui says that her work now, with the NSF, focuses on tackling outstanding fundamental issues in AI, like a lack of what she calls “digital representation.” Biases and prejudices pervade today’s AI, she avers, in part due to the homogenous makeup of the companies developing it.

“AI is being trained on data from developers, but developers are mostly men with specific perspectives, and represent a very small subset of the 8 billion people in the world,” she said. “If we’re not including women as developers and if women aren’t providing feedback as users, then AI will not represent them at all.”

Balancing innovation and safety

Velastegui sees the AI industry’s breakneck pace as a “huge issue” — absent a common ethical safety framework, that is. Such a framework, were it ever to be widely embraced, could allow developers to build systems with speed without stifling innovation, she believes.

But she’s not counting on it.

“We’ve never seen technology this transformative evolve at such a relentless pace,” Velastegui said. “People, regulation, legacy systems … nothing has ever had to keep up at the current speed of AI. The challenge becomes how to stay informed, up-to-date, and forward-thinking, while also aware of the dangers if we move too fast.”

How can a company — or developer — create AI products responsibly today? Velastegui champions a “human-centered” approach with learning from past mistakes and prioritizing the well-being of users at its core.

“Companies should empower a diverse, cross-functional AI council that reviews issues and provides recommendations that reflect the current environment,” Velastegui said, “and create channels for regular feedback and oversight that will adapt as the AI system evolves. And there should be channels for regular feedback and oversight that will adapt as AI systems evolves.”



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