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Spott’s AI-native recruiting platform scores $3.2M to end hiring software chaos

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Spott has raised $3.2 million in seed funding to build an AI-native platform that promises to transform how recruitment agencies operate. The San Francisco-based startup announced the funding round today, led by Base10 Partners with participation […]

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Spott has raised $3.2 million in seed funding to build an AI-native platform that promises to transform how recruitment agencies operate. The San Francisco-based startup announced the funding round today, led by Base10 Partners with participation from Y Combinator, Fortino, True Equity, and several angel investors. The capital injection follows Spott’s recent completion of Y Combinator’s Winter 2025 accelerator program.

“For too long, recruitment firms have relied on outdated software to manage daily operations,” said Lander Degreve, co-founder and CEO at Spott, in an exclusive interview with VentureBeat. “Spott solves the problem of outdated, passive, and fragmented recruitment software by offering an all-in-one AI-native platform that actively automates entire workflows, enabling recruiters to focus on what matters most and make more placements.”

The recruitment technology space has seen a flood of point solutions in recent years, especially since generative AI tools began proliferating in 2022. However, most of these tools address single pain points rather than reimagining the entire recruitment workflow.

How Spott’s all-in-one AI platform challenges recruitment tech fragmentation

Unlike narrowly-focused AI tools that create more tech fragmentation, Spott is building a comprehensive operating system that manages everything from candidate sourcing and screening to placement. The platform already claims to have generated over 1,000 candidate reports for its customers, which include Stanton Chase, a global executive search firm.

“Existing recruitment software is largely a passive system of record, often requiring multiple integrations just to take notes, search & match candidates, run outbound campaigns or reformat CVs,” said Degreve. “As AI enters the space, the number of disconnected point solutions is only increasing. Spott brings all these capabilities, including cutting-edge AI, together in one end-to-end recruitment platform by default.”

The company’s approach addresses a pain point many recruitment firms face: the necessity to cobble together multiple systems to handle different aspects of their workflow — separate tools for candidate tracking, client management, email automation, resume parsing, and formatting.

This technical integration challenge represents a significant market opportunity. As recruitment agencies increasingly adopt AI tools, the pain of managing disconnected solutions grows more acute. Spott’s integrated approach could potentially capture significant market share if it successfully delivers on its promise of seamless workflow automation.

“We’ve been using the report writer a lot, and the results are already very good thanks to the way the system handles the data,” Degreve cited from customer feedback. “It’s also helpful that we can review the output and make small changes with AI ourselves and instantly see how that improves the results.”

From McKinsey to startup: The strategic vision behind Spott’s AI platform

The founders’ background brings a unique perspective to the recruitment technology space. Degreve and his co-founder Manu Vanderveeren previously worked at elite consulting firms McKinsey, BCG, and Bain, where they gained strategic insight into the recruitment industry’s challenges from a business perspective rather than just a technical one.

“The idea for Spott emerged during our time at McKinsey and BCG, where we worked on strategy and due diligence projects for recruitment agencies focused on growth,” explained Degreve. “It became clear to us that AI would be the key growth driver and differentiator in the industry. To truly unlock AI’s full potential, the entire recruitment platform needs to be rebuilt from the ground up.”

This consulting background appears to have influenced Spott’s ambitious approach. Rather than creating yet another point solution, the team is pursuing what venture capitalists often call a “platform play” — a higher-risk, higher-reward strategy that aims to become the central operating system for an entire industry.

“Building the system of record is much more complex than launching a single point solution. It requires greater effort to build and sell, as clients need to migrate to a new platform,” Degreve acknowledged. “However, we believe the only way to fully unlock AI’s potential in recruitment is to rebuild the entire system from the ground up.”

This strategic vision may explain why investors like Base10 Partners and Y Combinator have backed the company despite the crowded recruitment technology market — they’re betting on a comprehensive reimagining of the space rather than an incremental improvement.

Why AI adoption in recruitment has failed to deliver on its promise

Industry analysts have long identified the recruitment sector as ideal for AI transformation. With its heavy reliance on pattern matching, data processing, and repetitive communications, many recruitment tasks align perfectly with AI’s strengths. Yet the industry has been surprisingly slow to see widespread transformation from AI technologies.

The problem isn’t a lack of AI tools — it’s their fragmentation and failure to integrate deeply into recruiters’ existing workflows. Many recruiters report “tool fatigue” from juggling multiple AI solutions alongside their core applicant tracking systems. This complexity often negates the time savings these tools promise.

“The most exciting AI feature in Spott is its advanced candidate and vacancy matching. By deeply understanding all your data — vacancies, AI-generated notes, emails, LinkedIn & WhatsApp messages and CVs — it can instantly surface the most relevant candidates,” Degreve said. “This boosts the value of your database and can significantly shorten time to hire, especially when combined with automated outreach.”

What’s notable about Spott’s approach is that it doesn’t treat AI as an add-on feature but as the core architecture of the platform. This “AI-native” design philosophy potentially allows for deeper integration of machine learning throughout the entire recruitment process rather than just at specific touchpoints.

Early customer Pauwels Solutions Group reports that the platform “automatically formats CVs and generates tailor-made presentations in our own template, which saves a lot of time and manual work.” With its 100% contract renewal rate to date, Spott appears to be delivering on its promises — though the sample size remains small at this early stage.

The future of recruitment: Agentic workflows and the changing recruiter role

With the new funding, Spott plans to accelerate development of what may be its most ambitious feature: “agentic workflows” — AI agents that can take over entire processes within the recruitment lifecycle with minimal human supervision.

“Over the next 6 to 12 months, our main focus is on building intuitive agentic workflows. These AI agents will support recruiters by gradually taking over workflows like sourcing, outreach, scheduling, and presenting candidates,” Degreve explained. “Human input will be used where it matters most and will help the agents learn and improve over time.”

This vision represents a significant shift in how recruitment professionals might work in the future. Rather than spending hours on administrative tasks, formatting resumes, or searching databases, recruiters could potentially focus almost exclusively on relationship-building and closing placements.

“Over the next few years, Spott envisions a major shift in recruitment that will redefine the role of the recruiter,” said Degreve. “As AI automates repetitive tasks, straightforward roles will increasingly be handled fully autonomously. In more complex searches, the recruiter’s focus will shift toward building strong relationships with candidates and clients, understanding culture fit, navigating nuanced negotiations, and addressing challenges that require human judgment.”

This vision aligns with broader trends in AI adoption across professional services. The most successful implementations don’t replace professionals entirely but instead elevate their work by handling routine tasks while enabling humans to focus on high-value judgment and relationship activities. For recruitment firms feeling the squeeze of tight margins and increasing competition, this proposition is particularly compelling.

The true test will come as Spott scales its platform and attempts to displace entrenched competitors in an industry known for its resistance to technological change. But with fresh funding and a clear vision, Spott has positioned itself as a contender to watch in the evolving recruitment technology landscape.

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North Atlantic in Talks with ExxonMobil to Buy French Refinery Complex

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OPEC+ Ratifies Group-Wide Quotas Before July Output Decision

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AI and microgrids: Moving towards seamless, resilient energy solutions

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HPE Aruba unveils raft of new switches for data center, campus modernization

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Indian startup Refroid launches India’s first data center CDUs

They use heat exchangers and pumps to regulate the flow and temperature of fluid delivered to equipment for cooling, while isolating the technology cooling system loop from facility systems. The technology addresses limitations of traditional air cooling, which industry experts say cannot adequately handle the heat generated by modern AI processors and high-density computing applications. Strategic significance for India Industry analysts view the development as a critical milestone for India’s data center ecosystem. “India generates 20% of global data, yet contributes only 3% to global data center capacity. This imbalance is not merely spatial — it’s systemic,” said Sanchit Vir Gogia, chief analyst and CEO at Greyhound Research. “The emergence of indigenously developed CDUs signals a strategic pivot. Domestic CDU innovation is a defining moment in India’s transition from data centre host to technology co-creator.” Neil Shah, VP for research and partner at Counterpoint Research, noted that major international players like Schneider, Vertiv, Asetek, Liquidstack, and Zutacore have been driving most CDU deployments in Indian enterprises and data centers. “Having a local indigenous CDU tech and supplier designed with Indian weather, infrastructure and costs in mind expands options for domestic data center demand,” he said. AI driving data center cooling revolution India’s data center capacity reached approximately 1,255 MW between January and September 2024 and was projected to expand to around 1,600 MW by the end of 2024, according to CBRE India’s 2024 Data Center Market Update. Multiple market research firms have projected the India data center market to grow from about $5.7 billion in 2024 to $12 billion by 2030. Bhavaraju cited aggressive projections for the sector’s expansion, with AI workloads expected to account for 30% of total workloads by 2030. “All of them need liquid cooling,” he said, noting that “today’s latest GPU servers – GB200 from Nvidia

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Platform approach gains steam among network teams

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Oracle to spend $40B on Nvidia chips for OpenAI data center in Texas

OpenAI has also expanded Stargate internationally, with plans for a UAE data center announced during Trump’s recent Gulf tour. The Abu Dhabi facility is planned as a 10-square-mile campus with 5 gigawatts of power. Gogia said OpenAI’s selection of Oracle “is not just about raw compute, but about access to geographically distributed, enterprise-grade infrastructure that complements its ambition to serve diverse regulatory environments and availability zones.” Power demands create infrastructure dilemma The facility’s power requirements raise serious questions about AI’s sustainability. Gogia noted that the 1.2-gigawatt demand — “on par with a nuclear facility” — highlights “the energy unsustainability of today’s hyperscale AI ambitions.” Shah warned that the power envelope keeps expanding. “As AI scales up and so does the necessary compute infrastructure needs exponentially, the power envelope is also consistently rising,” he said. “The key question is how much is enough? Today it’s 1.2GW, tomorrow it would need even more.” This escalating demand could burden Texas’s infrastructure, potentially requiring billions in new power grid investments that “will eventually put burden on the tax-paying residents,” Shah noted. Alternatively, projects like Stargate may need to “build their own separate scalable power plant.” What this means for enterprises The scale of these facilities explains why many organizations are shifting toward leased AI computing rather than building their own capabilities. The capital requirements and operational complexity are beyond what most enterprises can handle independently.

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New Intel Xeon 6 CPUs unveiled; one powers rival Nvidia’s DGX B300

He added that his read is that “Intel recognizes that Nvidia is far and away the leader in the market for AI GPUs and is seeking to hitch itself to that wagon.” Roberts said, “basically, Intel, which has struggled tremendously and has turned over its CEO amidst a stock slide, needs to refocus to where it thinks it can win. That’s not competing directly with Nvidia but trying to use this partnership to re-secure its foothold in the data center and squeeze out rivals like AMD for the data center x86 market. In other words, I see this announcement as confirmation that Intel is looking to regroup, and pick fights it thinks it can win. “ He also predicted, “we can expect competition to heat up in this space as Intel takes on AMD’s Epyc lineup in a push to simplify and get back to basics.” Matt Kimball, vice president and principal analyst, who focuses on datacenter compute and storage at Moor Insights & Strategy, had a much different view about the announcement. The selection of the Intel sixth generation Xeon CPU, the 6776P, to support Nvidia’s DGX B300 is, he said, “important, as it validates Intel as a strong choice for the AI market. In the big picture, this isn’t about volumes or revenue, rather it’s about validating a strategy Intel has had for the last couple of generations — delivering accelerated performance across critical workloads.”  Kimball said that, In particular, there are a “couple things that I would think helped make Xeon the chosen CPU.”

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AWS clamping down on cloud capacity swapping; here’s what IT buyers need to know

As of June 1, AWS will no longer allow sub-account transfers or new commitments to be pooled and reallocated across customers. Barrow says the shift is happening because AWS is investing billions in new data centers to meet demand from AI and hyperscale workloads. “That infrastructure requires long-term planning and capital discipline,” he said. Phil Brunkard, executive counselor at Info-Tech Research Group UK, emphasized that AWS isn’t killing RIs or SPs, “it’s just closing a loophole.” “This stops MSPs from bulk‑buying a giant commitment, carving it up across dozens of tenants, and effectively reselling discounted EC2 hours,” he said. “Basically, AWS just tilted the field toward direct negotiations and cleaner billing.” What IT buyers should do now For enterprises that sourced discounted cloud resources through a broker or value-added reseller (VAR), the arbitrage window shuts, Brunkard noted. Enterprises should expect a “modest price bump” on steady‑state workloads and a “brief scramble” to unwind pooled commitments.  If original discounts were broker‑sourced, “budget for a small uptick,” he said. On the other hand, companies that buy their own RIs or SPs, or negotiate volume deals through AWS’s Enterprise Discount Program (EDP), shouldn’t be impacted, he said. Nothing changes except that pricing is now baselined.

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Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs).  In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

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John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

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2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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