investment opportunities Finland, Greece, Croatia, Israel
Finland
IQM, ICEYE, Oura, Nanoform Finland Oyj, Endomines Finland Oyj, Snowfox.AI
Greece
Blueground, Harbor Lab, InstaShop
Croatia
GlycanAge, Infobip
Israel
monday.com, Finout
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Scale AI Faces Pay Cuts and Client Losses After Meta Takes 49% Stake
A $14 billion investment from Meta should have been a triumph for Scale AI, but the aftermath has been far more turbulent. Since Meta acquired a 49% stake, the once‑dominant AI training startup has faced pay cuts, talent poaching, internal pivots and growing unease among contractors.
Scale AI Pay Cuts and Client Losses
Some Big Tech clients have even paused work with Scale, wary of partnering…
Scale AI Faces Pay Cuts and Client Losses After Meta Takes 49% Stake
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Just finished a four-round interview at Scale AI – here’s my interview experience.
Round 1 — BQ + Backend Practical
Behavioral questions covered previous projects. For the backend practical the stack chosen was Python. The task was similar to implementing a lightweight load balancer; you had to clone a specified project from GitHub. Required implementation points included:
a. Worker state management (e.g. active/overloaded/unreachable)
b. Task queue and priority scheduling mechanism
c. How to support scalability (for example, dynamic joining of worker nodes)
You needed to design from perspectives such as task-dispatch logic, worker heartbeat mechanism, failover strategy, etc., and produce code within a limited time — the bar was fairly high.
Round 2 — Coding: Clock-hand angle
The coding round was done live on CoderPad and you had to explain your approach. The problem itself is not hard but you must handle edge cases and clearly explain the logic. Given a time string like “3:45”, compute the angle between the hour hand and the minute hand.
You can apply a direct formula, but the interviewer wanted you to explain where each part of the angle calculation comes from. The idea: the minute hand moves 6° per minute; the hour hand moves 30° per hour plus 0.5° per minute. So compute each hand’s angle relative to 12 o’clock from the input hour and minute, take the absolute difference, and if it’s greater than 180° subtract it from 360° to get the smaller angle.
Follow-up: how would you adjust the formula if the input also includes seconds or milliseconds?
Round 3 — System Design
This round was a training session: a smiling Japanese interviewer shadowed a Chinese candidate. The task was to design a Ticketmaster-like system. You can follow Alex Xu’s methodology; however, the candidate said: “Let’s not waste time on back-of-the-envelope calculations — forget about distributed systems. Let’s map the user flow clearly and draw a diagram with all components.”
Requirements included:
a. How to handle flash-sale scenarios where many users try to buy tickets in a short time
b. How to implement a timeout on the purchase page and what to do if payment is not completed within the timeout
c. How to handle the situation when tickets are sold out
d. How to guarantee that users who completed payment will definitely receive tickets
e. How to implement a waitlist that notifies the next-in-line people when tickets are returned
Round 4 — BQ + Coding
Behavioral questions were standard:
- “Tell me about a time you had to learn something quickly.” Show structured learning ability — e.g., using API docs, codebase walkthroughs, shadowing colleagues, building a proof of concept (POC).
- “Tell me about a time you disagreed with a teammate or manager.” Support your stance with data-driven or customer-impact reasons, show you can accept valid feedback, and turn the result into a win-win.
- “Tell me about a challenging project you worked on.” Use the STAR structure to tell a business-impactful story; in the Action section quantify your measures (for example, “improved performance by 40%,” “reduced latency to P99 < 200ms”).
The coding problem: given a tree where you only know each node’s list of children, find the lowest common ancestor (LCA) of two nodes. Several approaches were discussed: first, run DFS to compute each node’s parent and depth, then raise the deeper node upward until depths match and move both up until they meet. Another approach: run a DFS from the root where each node returns a pair of booleans indicating whether it can reach the two target nodes; the first node that returns (true, true) is the LCA. Ideas, clarifications, comments, and test cases were all explained in detail.
Other Scale AI interview experiences you can refer to ： https://programhelp.net/vo/scale-ai-interview-process-explained-questions-rounds-amp-tips/
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Transforming Enterprise CRM with Salesforce AI Agents
Enterprises today are reshaping how they engage customers, streamline operations, and scale business outcomes. As Generative AI accelerates digital transformation, organizations are increasingly turning into intelligent automation. Integrating Salesforce with AI Agents allows enterprises to combine speed, precision, and governance while modernizing customer-facing and operational workflows.
Intelligent Automation for Modern Enterprises
Traditional automation handles repetitive tasks but struggles with dynamic decisions and shifting customer expectations. Salesforce enhanced with AI agents overcomes this limitation by embedding cognitive intelligence into CRM processes. These agents interpret data, surface opportunities, and automate decisions that once required human input—evolving Salesforce from a passive system of record into a proactive system of action.
Accelerating Speed and Operational Efficiency
In an enterprise environment where every second counts, AI-driven automation helps teams operate at business speed. AI agents inside Salesforce automate lead qualification, service routing, and case handling, enabling faster execution across teams. Predictive forecasting and real-time prioritization further empower organizations to respond with accuracy, agility, and compliance.
Scaling with Unified and Intelligent Data
Sustainable transformation depends on scalability. By connecting AI agents with the Salesforce Data Cloud, enterprises can unify data across sales, marketing, and service. This integrated foundation provides AI agents with complete context, enabling smarter, more consistent automation as volumes grow. Combined with Scale AI capabilities, organizations handle higher transaction loads while continuously improving through learning-driven optimization.
Governance and Compliance Built-In
Operating in regulated environments demands strict oversight. AI agents enhance Salesforce with built-in governance—comprehensive audit trails, policy enforcement, and compliant workflows. Sensitive data remains protected through controlled access and continuous monitoring, ensuring transparency and accountability across all automated business processes.
Agentforce: The New Layer of CRM Intelligence
Agentforce, Salesforce’s next-generation AI layer, introduces agents capable of interacting with customers and employees to automate end-to-end processes. With natural language understanding and contextual reasoning, Agentforce accelerates response times and strengthens decision-making across departments.
Advantage of Salesforce with AI Agents for Enterprises
- Simplifies complex workflows through intelligent automation.
- Delivers personalized experiences at enterprise scale.
- Empowers teams with real-time, actionable insights.
- Strengthens speed, governance, and long-term scalability.
Conclusion
Salesforce with AI Agents enables enterprises to meet modern demands for speed, compliance, and superior customer experience. By combining intelligence, scalability, and built-in governance, it supports precise, agile operations. This marks the future of CRM, where intelligent automation enhances interactions, strengthens decisions, and drives sustainable enterprise growth.
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Agentic AI is here — are you ready?
Ash Dhupar joins us to unpack the real-world challenges of bringing AI—especially agentic AI—into business at scale. Agentic AI: Challenges and strategies for implementation.
1-line Summary: Agentic AI: Challenges and strategies for implementation.
Meta Description: Explore the complexities of deploying agentic AI in your organization. Learn how Ash Dhupar discusses scaling AI solutions with…
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Scale AI Founder Alexandr Wang Joins Meta in Landmark $14.3 Billion Deal
Alexandr Wang, the 27-year-old founder and CEO of Scale AI, has officially confirmed his departure from the company to join Meta, following a blockbuster $14.3 billion investment by the tech giant. The move was announced in a memo shared with employees and later posted on Wang’s X (formerly Twitter) account.
The deal marks one of the largest AI-related investments to date, giving Meta a 49% stake in the fast-growing artificial intelligence data company. Despite its sizeable financial commitment, Meta will not receive voting rights, ensuring Scale AI’s operational independence, according to a spokesperson from the startup.
“Opportunities of this magnitude often come at a cost,” Wang wrote in his memo. “In this instance, that cost is my departure. It has been the absolute greatest pleasure of my life to serve as your CEO.”
ALTLeadership Transition
With Wang’s exit, Jason Droege, Scale AI’s current Chief Strategy Officer, will take over as CEO. Droege brings deep Silicon Valley experience, having previously served as a Vice President at Uber and a venture partner at Benchmark.
A select number of Scale AI employees will also transition to Meta under the terms of the agreement.
Meta’s Superintelligence Play
Meta confirmed the strategic partnership, stating that Wang will lead efforts around superintelligence, a core focus of CEO Mark Zuckerberg’s 2025 AI vision.
“As part of this, we will deepen the work we do together producing data for AI models, and Alexandr Wang will join Meta to work on our superintelligence efforts,” a Meta spokesperson said. “We will share more about this effort and the great people joining this team in the coming weeks.”
Zuckerberg’s AI ambitions have intensified in recent months, especially amid mounting competition from OpenAI and Alphabet (Google). Sources say he has grown frustrated with the lukewarm reception of Meta’s latest Llama model, prompting a more aggressive strategy to acquire external talent and innovation.
In a departure from his usual preference for promoting internally, Zuckerberg reportedly handpicked Wang to lead Meta’s next-generation AI initiatives.
Impact on Industry and Customers
Scale AI plays a pivotal role in the AI ecosystem by supplying high-quality training data to leading tech firms, including Google, Microsoft, and OpenAI—all of whom compete directly with Meta. Despite Meta’s minority stake, Scale AI emphasized that the company’s existing customer relationships and proprietary data will remain unaffected.
“Meta’s investment and Alexandr’s move will not impact Scale AI’s service to its clients,” the company stated. “Meta will not have access to any of our business information or data.”
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Alexandr Wang, the CEO of Scale AI, was born in 1997 in Los Alamos, New Mexico, into a family of scientists who work at the Los Alamos National Laboratory.
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Scale AI piqued Meta’s interest with a startling $15 billion investment, but what makes this relatively new firm so important to the future of artificial intelligence? Behind the scenes of AI
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Scale AI Under Investigation by U.S. Department of Labor: Implications for
the AI Industry
Scale AI Under Investigation by U.S. Department of Labor: Implications for the AI Industry
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IN THE FALL OF 2020, GIG WORKERS IN VENEZUELA POSTED A SERIES OF images to online forums where they gathered to talk shop. The photos were mundane, if sometimes intimate, household scenes captured from low angles—including some you really wouldn’t want shared on the Internet.
In one particularly revealing shot, a young woman in a lavender T-shirt sits on the toilet, her shorts pulled down to mid-thigh.
The images were not taken by a person, but by development versions of iRobot’s Roomba J7 series robot vacuum. They were then sent to Scale AI, a startup that contracts workers around the world to label audio, photo, and video data used to train artificial intelligence.
They were the sorts of scenes that internet-connected devices regularly capture and send back to the cloud—though usually with stricter storage and access controls. Yet earlier this year, MIT Technology Review obtained 15 screenshots of these private photos, which had been posted to closed social media groups.
The photos vary in type and in sensitivity. The most intimate image we saw was the series of video stills featuring the young woman on the toilet, her face blocked in the lead image but unobscured in the grainy scroll of shots below. In another image, a boy who appears to be eight or nine years old, and whose face is clearly visible, is sprawled on his stomach across a hallway floor. A triangular flop of hair spills across his forehead as he stares, with apparent amusement, at the object recording him from just below eye level.
iRobot—the world’s largest vendor of robotic vacuums, which Amazon recently acquired for $1.7 billion in a pending deal—confirmed that these images were captured by its Roombas in 2020.
Ultimately, though, this set of images represents something bigger than any one individual company’s actions. They speak to the widespread, and growing, practice of sharing potentially sensitive data to train algorithms, as well as the surprising, globe-spanning journey that a single image can take—in this case, from homes in North America, Europe, and Asia to the servers of Massachusetts-based iRobot, from there to San Francisco–based Scale AI, and finally to Scale’s contracted data workers around the world (including, in this instance, Venezuelan gig workers who posted the images to private groups on Facebook, Discord, and elsewhere).
Together, the images reveal a whole data supply chain—and new points where personal information could leak out—that few consumers are even aware of.
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Alexandr Wang : “Je ne suis jamais retourné à l’école” ; Voici le nouveau plus jeune milliardaire autodidacte au monde
Alexandr Wang : “Je ne suis jamais retourné à l’école” ; Voici le nouveau plus jeune milliardaire autodidacte au monde
Alexandr Wang a grandi dans l’ombre du Los Alamos National Lab du Nouveau-Mexique, le site top secret où les États-Unis ont développé leur première bombe atomique pendant la Seconde Guerre mondiale. Ses parents étaient des physiciens qui travaillaient sur des projets d’armement pour l’armée.
Maintenant, il le fait aussi: scale AI, la société de Wang basée à San Francisco, basée à Six ans, a déjà…
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Scale AI secures $375M Series E
- By Phil Siarri , Nuadox -
San Francisco-based Scale AI; a company known for its artificial intelligence solutions for the document processing, automotive, ecommerce and other industries; announced on April 13 it has secured a $375 million Series E funding round co-led by Dragoneer, Greenoaks Capital, and Tiger Global.
[[MORE]]Reuters reports an updated valuation of $7 billion.
“At Scale, we’re building the foundation to enable organizations to manage the entire AI lifecycle. Whether they have an AI team in-house or need a fully managed models-as-a-service approach, we partner with our customers to build their strategy from the ground up and ensure they have the infrastructure in place to systematically deliver highly-performant models.” Alexandr Wang, CEO and founder of Scale AI, stated in a blog post detailing the new funding.
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Header image: Scale AI banner. Credit: Scale AI, Fair Use.
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Alexandr Wang’s Interview in BloombergDecades ago, wondrous technology could only be foreseen in television and movies as filmmakers implanted ideas into people’s heads of what the future would look like. People have been fantasizing over space battleships, flying cars, automated human-like robots, holograms, augmented and…