Sources: AI Training Startup Mercor Aims for $10B+ Valuation with $450 Million Revenue Run Rate

Mercor Eyes $10 Billion Valuation in Upcoming Series C Funding Round

Mercor, a pioneering startup facilitating connections between companies like OpenAI and Meta with domain professionals for AI model training, is reportedly in talks with investors for a Series C funding round, according to sources familiar with the negotiations and a marketing document obtained by TechCrunch.

Felicis Considers Increasing Investment

Felicis, a previous investor, is contemplating a deeper investment for the Series C round. However, Felicis has chosen not to comment on the matter.

Targeting a $10 Billion Valuation

Mercor is eyeing a valuation exceeding $10 billion, up from an earlier target of $8 billion discussed just months prior. Final deal terms may still fluctuate as negotiations progress.

A Surge of Preemptive Offers

Potential investors have been informed that Mercor has received multiple offer letters, with valuations reaching as high as $10 billion, as previously covered by The Information.

New Investors on Board

Reports indicate that Mercor has successfully onboarded at least two new investors to assist in raising funds for the impending deal via special purpose vehicles (SPVs).

Previous Funding Success

The company’s last funding round occurred in February, securing $100 million in Series B financing at a valuation of $2 billion, led by Felicis.

Impressive Revenue Growth

Founded in 2022, Mercor is nearing an annualized run-rate revenue (ARR) of $450 million. Earlier this year, the company reported revenues soaring to $75 million, later confirmed by CEO Brendan Foody to reach $100 million in March.

Projected Growth Outpacing Competitors

Mercor is on track to surpass the $500 million ARR milestone quicker than Anysphere, which achieved this goal approximately a year post-launch. Notably, Mercor has already generated $6 million in profit during the first half of the year, contrasting with its competitors.

Revenue Model and Clientele

Mercor’s revenue stream is primarily generated by connecting businesses with specialized experts in various domains—such as scientists and lawyers—charging for their training and consultation services. The startup claims to supply data labeling contractors for leading AI innovators including Amazon, Google, Meta, Microsoft, OpenAI, Tesla, and Nvidia, with notable income derived from collaborations with OpenAI.

Diversifying with Software Infrastructure

To expand its operational model, Mercor is exploring the implementation of software infrastructure for reinforcement learning (RL), a training approach that enhances decision-making processes in AI models. The company also aims to develop an AI-driven recruiting marketplace.

Facing Competitive Challenges

Mercor’s journey isn’t without competition; firms like Surge AI are also seeking funding to bolster their valuation significantly. Additionally, OpenAI’s newly launched hiring platform poses potential competitive pressures in the realm of human-expert-powered RL training services.

Co-Founder Insights

In response to inquiries, CEO Brendan Foody stated, “We haven’t been trying to raise at all,” and noted that the company regularly declines funding offers. He confirmed that the ARR is indeed above $450 million, clarifying that reported revenues encompass total customer payments before contractor distributions, a common accounting practice in the industry.

Leadership and Growth Strategy

Mercor was co-founded in 2023 by Thiel Fellows and Harvard dropouts Brendan Foody (CEO), Adarsh Hiremath (CTO), and Surya Midha (COO), all in their early twenties. To help drive the company forward, they recently appointed Sundeep Jain, a former chief product officer at Uber, as the first president.

Legal Challenges from Scale AI

Mercor is currently facing a lawsuit from rival Scale AI, which accuses the startup of misappropriating trade secrets through a former employee who allegedly took over 100 confidential documents related to Scale’s customer strategies and proprietary information.

Maxwell Zeff contributed reporting

Sure! Here are five frequently asked questions (FAQs) based on the topic of Mercor’s valuation and financial performance:

FAQs

1. What is Mercor’s current valuation?

  • Mercor is targeting a valuation of over $10 billion as it continues to grow in the AI training startup sector.

2. What is Mercor’s current revenue run rate?

  • The company has a revenue run rate of approximately $450 million, indicating strong financial performance and growth potential.

3. What does a $10 billion valuation mean for Mercor?

  • A $10 billion valuation suggests that investors believe in Mercor’s potential for significant future growth and its strong position in the AI training market.

4. How does Mercor plan to achieve its ambitious valuation?

  • Mercor is focusing on scaling its AI training solutions, attracting top talent, and potentially expanding its market reach to enhance its product offerings and customer base.

5. What factors contribute to the high valuation in the AI startup sector?

  • High valuations in the AI sector typically result from rapid advancements in technology, increasing demand for AI solutions across various industries, and investor confidence in the profitability of such innovations.

If you have more specific inquiries or need further information, feel free to ask!

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Koah Secures $5 Million to Integrate Advertising into AI Applications

Monetizing AI: How Startups like Koah Are Paving the Way with Advertising

How can startups and developers effectively monetize their AI products? A promising startup, Koah, has recently secured $5 million in seed funding and is betting on advertising as a key revenue stream.

The Current Landscape of AI Advertising

If you’re active online, you might have encountered many unattractive AI-generated ads. However, interactions with AI chatbots have largely remained advertisement-free. Koah’s co-founder and CEO, Nic Baird, predicts that this is about to change.

Ads: The Future of AI Monetization?

“Once these technologies expand beyond San Francisco, the only viable path to profitability on a global scale is through advertising,” Baird shared in an interview with TechCrunch. “History has shown this repeatedly.”

It’s important to note that Koah isn’t targeting ChatGPT for advertising integration. Instead, they are concentrating on the broader ecosystem of apps built on top of existing AI models, especially those aimed at user bases outside the U.S.

Ending the Subscription Conundrum

Initially, consumer AI products targeted “wealthier, prosumer” users, monetizing through paid subscriptions. However, as Baird points out, an AI app could now potentially reach millions of users in regions like Latin America, where subscription costs of $20 per month are unrealistic. This shift poses challenges for developers in generating subscription-based revenue while still incurring the same operational costs as their counterparts.

A sample Koah ad for acne wash
Image Credits: Koah

Unlocking New Opportunities in AI

Baird believes that successfully integrating advertising into AI chats could unlock the potential of “vibe coded” apps that might otherwise be too costly to maintain without significant venture capital investment.

Current Applications and Advertisers

Koah has already started serving ads within applications such as AI assistant Luzia, parenting app Heal, student research platform Liner, and creative tool DeepAI. Advertisers include well-known names like UpWork, General Medicine, and Skillshare.

These sponsored ads are designed to appear contextually within chats. For instance, if a user seeks advice on startup strategies, the app might display an UpWork ad connecting them to relevant freelancers.

Proving Effectiveness in Advertising

While many publishers express skepticism about the effectiveness of ads in AI chats, some have seen minimal success with existing ad tech solutions. Baird asserts that Koah’s platform is delivering click-through rates of 7.5%, which is four to five times more effective than competition, with early partners earning $10,000 within their first month using Koah.

Image Credits: Koah

Key Investment Support

Koah’s seed funding round was led by Forerunner, with additional participation from South Park Commons and AppLovin co-founder Andrew Karam.

Consistent Revenue Models in Consumer AI

Forerunner partner Nicole Johnson noted in her investment commentary that monetization issues in AI are a pressing concern for developers and investors alike. While subscriptions have been the standard for monetizing AI services, relying solely on them may lead to user fatigue.

Johnson argues for diversified revenue models in consumer AI, stating that ads will play a significant role in future monetization strategies. She believes Koah is establishing the essential foundation for this new monetization layer.

The Role of AI Chats in Advertising

According to Baird and his team, AI chat interactions fit between raising awareness through social media ads and final purchases via search engine ads. He emphasizes the importance of capturing users’ “commercial intent” as they explore options through AI.

“People aren’t making purchases via AI; they generally transition to Google for that,” Baird commented. Thus, the challenge for Koah lies in determining how best to fulfill users’ needs during their interactions.

“It’s not about merely placing display ads in AI,” Baird concluded. “I want to focus on understanding what users are seeking and ensuring we provide it effectively.”

Sure! Here are five FAQs regarding Koah’s recent $5M funding to integrate ads into AI applications:

FAQ 1: What is Koah planning to do with the $5M raised?

Answer: Koah intends to use the $5 million funding to enhance its technology for integrating advertisements into AI applications. This funding will help develop new features and improve user experience while ensuring a seamless integration of advertisements within AI platforms.

FAQ 2: How will ads be integrated into AI applications?

Answer: Ads will be integrated into AI applications through innovative algorithms that ensure relevance and non-intrusiveness. The goal is to provide users with tailored advertising experiences that align with their interests and usage patterns, enhancing engagement without disrupting the user experience.

FAQ 3: Who are the investors behind Koah’s funding?

Answer: The funding round saw participation from a mix of venture capital firms and private investors who specialize in technology and advertising sectors. Specific investor names may be disclosed in future announcements as Koah seeks to forge strategic partnerships.

FAQ 4: What benefits do ads bring to AI applications?

Answer: Integrating ads into AI applications can provide a monetization strategy for developers, allowing them to fund further development and improve features. Additionally, relevant ads can enhance user experience by offering tailored suggestions and promotions that users may find useful.

FAQ 5: How will this funding affect the end users of Koah’s apps?

Answer: End users can expect a more robust and feature-rich application experience as the funding allows Koah to invest in technology enhancements. While ads will be present, the company is committed to ensuring they are relevant and enhance rather than detract from the user experience.

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GitHub Copilot Surpasses 20 Million Users All-Time

<div>
    <h2>GitHub Copilot Surpasses 20 Million Users: A New Era in AI Coding Tools</h2>

    <p id="speakable-summary" class="wp-block-paragraph">Microsoft CEO Satya Nadella announced during a recent earnings call that GitHub Copilot, the AI coding assistant from GitHub, has achieved over 20 million users. A GitHub representative clarified that this figure includes “all-time users.”</p>

    <h3>Significant Growth in User Base</h3>
    <p class="wp-block-paragraph">In just the last three months, five million new users have begun utilizing GitHub Copilot. This follows the announcement in April that the tool had reached 15 million users. However, Microsoft and GitHub have not disclosed how many of these users remain active on a monthly or daily basis, suggesting those numbers may be significantly lower.</p>

    <h3>Enterprise Adoption on the Rise</h3>
    <p class="wp-block-paragraph">GitHub Copilot is becoming a favorite among large organizations, with 90% of the Fortune 100 reportedly using the tool. The adoption rate among enterprise customers has surged by approximately 75% compared to the previous quarter, indicating robust growth in this sector.</p>

    <h3>The Growing Landscape of AI Coding Solutions</h3>
    <p class="wp-block-paragraph">As AI coding tools gain traction, they are emerging as a key revenue stream. Nadella revealed that GitHub Copilot has become a more profitable venture than all of GitHub was at the time of its acquisition by Microsoft in 2018. Its growth trajectory remains strong as interest in AI tools expands.</p>

    <h3>AI Coding Tools vs. General AI Chatbots</h3>
    <p class="wp-block-paragraph">Despite their popularity, leading AI coding tools still attract far fewer users than AI chatbots like ChatGPT and Gemini, which collectively reach hundreds of millions monthly. This disparity arises from the specialized nature of software engineering compared to the broader inquiries handled by AI chatbots.</p>

    <h3>Strong Market Demand for AI Coding Tools</h3>
    <p class="wp-block-paragraph">Software developers and their organizations are increasingly willing to invest in AI coding tools. With an extensive roster of enterprise clients and a thriving ecosystem of developers, GitHub Copilot is well positioned to dominate the enterprise AI coding market.</p>

    <h3>New Entrants in the AI Coding Space</h3>
    <p class="wp-block-paragraph">Cursor, a competitor in the AI coding realm, aims to rival GitHub Copilot in the enterprise sector. The company has been acquiring talent from emerging AI startups and reportedly boasted over a million daily users as of March. Its annual recurring revenue has soared past $500 million, indicating significant growth.</p>

    <h3>Convergence of Features Between GitHub Copilot and Cursor</h3>
    <p class="wp-block-paragraph">While GitHub Copilot and Cursor originally targeted different aspects of the developer experience, their offerings are increasingly aligned. Both platforms are now rolling out <a target="_blank" rel="nofollow" href="https://github.blog/changelog/2025-04-04-copilot-code-review-now-generally-available/">AI agents for code reviews</a> and <a target="_blank" rel="nofollow" href="https://www.wired.com/story/cursor-releases-new-ai-tool-for-debugging-code/">bug detection</a>. They are also developing <a target="_blank" rel="nofollow" href="https://techcrunch.com/2025/06/30/cursor-launches-a-web-app-to-manage-ai-coding-agents/">AI agents to streamline programmer workflows</a>, allowing developers to offload repetitive tasks.</p>

    <h3>Competitive Landscape in AI Coding Tools</h3>
    <p class="wp-block-paragraph">Beyond Cursor, GitHub faces formidable competition from well-funded companies looking to penetrate the enterprise AI coding sector. This includes tech giants like Google, which recently acquired top talent from AI coding startup Windsurf, and Cognition, the maker of the Devin AI coding agent.</p>

    <p class="wp-block-paragraph">As the competitive landscape heats up, both established players and newcomers are vying for a share of this rapidly evolving market.</p>

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This rewrite maintains the essence of the original article while optimizing headlines for SEO and enhancing readability.

Here are five FAQs regarding GitHub Copilot reaching 20 million all-time users:

FAQ 1: What is GitHub Copilot?

Answer: GitHub Copilot is an AI-powered code completion tool developed by GitHub and OpenAI. It assists developers by offering context-aware code suggestions while they write, making coding faster and more efficient.

FAQ 2: How did GitHub Copilot reach 20 million all-time users?

Answer: GitHub Copilot reached 20 million users through widespread adoption by developers across various platforms and programming languages. Its integration into popular IDEs, along with continuous improvements and user feedback, has contributed to its growing user base.

FAQ 3: What features contribute to GitHub Copilot’s popularity?

Answer: Key features that contribute to GitHub Copilot’s popularity include real-time suggestions, code snippets for various programming languages, context understanding based on comments and existing code, and the ability to learn from the user’s coding style to improve suggestions.

FAQ 4: Is GitHub Copilot available for all developers?

Answer: Yes, GitHub Copilot is available to all developers, although it operates on a subscription model after a free trial period. Developers can sign up and access Copilot for use in popular code editors like Visual Studio Code and others.

FAQ 5: What are the benefits of using GitHub Copilot for developers?

Answer: Developers benefit from using GitHub Copilot by increasing productivity, reducing the time spent on repetitive coding tasks, improving code quality through AI-assisted suggestions, and gaining access to coding patterns and best practices that they may not be familiar with.

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AI Simulated 500 Million Years of Evolution to Create a New Protein

Revolutionizing Protein Design with the Power of AI

Introducing ESM3: The Next Evolution of Protein Engineering

Exploring the Endless Possibilities of AI-Driven Protein Design

The Future of Biology: Unleashing AI to Reshape Evolution

Ensuring Ethical and Responsible AI Development in Protein Engineering

ESM3: Pioneering the Future of Biotechnology with Rapid Evolution

  1. What is the significance of this new protein created through AI simulated evolution?

    • This new protein has the potential to revolutionize various industries, including medicine, food production, and biotechnology, by providing unique functionalities and capabilities not found in naturally occurring proteins.

  2. How does AI simulate evolution to create new proteins?

    • AI algorithms analyze vast amounts of protein sequences and structures to predict how they might evolve under different conditions. By simulating millions of years of evolution in a virtual environment, AI can generate novel protein sequences with desired properties.

  3. Will this new protein be safe for consumption?

    • Before being introduced into any application, the safety of the new protein will be rigorously tested through laboratory experiments and clinical trials. It will undergo thorough scrutiny to ensure it is safe for human consumption or use in other settings.

  4. Can this new protein be used to treat diseases or improve human health?

    • Yes, the unique properties of this new protein may hold promise for developing novel therapies or diagnostic tools for various diseases. Researchers are currently exploring its potential applications in medicine and health-related fields.
  5. How does this breakthrough in protein design impact the field of synthetic biology?
    • The successful creation of a new protein using AI-driven evolution represents a major advancement in the field of synthetic biology. It opens up exciting possibilities for designing custom proteins with specific functions and properties, thereby expanding the toolkit available to researchers in this rapidly evolving field.

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AMD Bolsters AI Presence with $665 Million Purchase of Silo AI

AMD Strengthens AI Position with Silo AI Acquisition

In a strategic move to bolster its presence in the AI sector, AMD has acquired Silo AI, Europe’s largest private AI lab, for $665 million. This acquisition marks a significant step in AMD’s AI expansion.

Established in 2017 and headquartered in Helsinki, Finland, Silo AI is a renowned AI research and development company specializing in creating customized AI models, platforms, and solutions for various industries, particularly focusing on cloud, embedded, and endpoint computing.

Key Details of the Acquisition

The all-cash transaction is a major investment for AMD, expected to be finalized in the second half of 2024, pending customary closing conditions and regulatory approvals.

Following the completion of the acquisition, Silo AI will become part of AMD’s Artificial Intelligence Group. Peter Sarlin, CEO, and Co-founder of Silo AI, will continue to lead the Silo AI team and report directly to Vamsi Boppana, AMD’s Senior Vice President of the Artificial Intelligence Group, ensuring the preservation of Silo AI’s culture while leveraging AMD’s global reach and resources.

Silo AI’s Expertise and Offerings

With a team of over 300 AI experts spread across 6 countries, Silo AI has a strong track record of developing tailored AI models and platforms for enterprise clients.

One of Silo AI’s notable accomplishments includes the creation of open-source multilingual large language models, Poro, and Viking, built on AMD platforms. These models exemplify Silo AI’s capability to develop AI systems capable of processing and generating human-like text in multiple languages.

Counting global leaders such as Allianz, Philips, Rolls-Royce, and Unilever among its clientele, Silo AI has also forged partnerships with top AI firms like Aleph Alpha and Mistral to solidify its position in the European AI landscape.

Impact and Future Outlook

By incorporating Silo AI’s expertise, AMD aims to expedite the development and deployment of AI solutions for its global customer base, narrowing the gap with competitors in the AI chip market, notably Nvidia. Silo AI’s multilingual language models and bespoke AI solutions enrich AMD’s product portfolio, enabling them to address a wider range of AI use cases.

Furthermore, this acquisition bolsters AMD’s presence in Europe, a pivotal AI development hub, allowing them to tap into the region’s AI talent pool and expand their footprint.

As part of a broader AI strategy, AMD has been actively enhancing its AI capabilities through strategic investments and acquisitions, positioning itself to capitalize on the growing demand for AI computing.

  1. What is AMD’s recent acquisition of Silo AI?
    AMD recently announced their acquisition of Silo AI, a Finland-based company specializing in artificial intelligence and machine learning solutions. This acquisition strengthens AMD’s position in the AI market.

  2. How much did AMD pay for the acquisition of Silo AI?
    AMD paid $665 million for the acquisition of Silo AI. This substantial investment showcases AMD’s commitment to expanding their AI capabilities.

  3. How will the acquisition of Silo AI benefit AMD?
    By acquiring Silo AI, AMD gains access to their expertise in AI and machine learning, allowing them to enhance their product offerings and better serve the growing demand for AI solutions in various industries.

  4. Will Silo AI continue to operate as a separate entity?
    While the specifics of how Silo AI will integrate into AMD are still being determined, it is expected that Silo AI’s technology and talent will be leveraged to strengthen AMD’s AI offerings.

  5. What does the acquisition of Silo AI mean for the future of AMD?
    With this acquisition, AMD is poised to become a major player in the AI market, solidifying their position as a leading provider of cutting-edge technology solutions for industries seeking AI capabilities.

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EvolutionaryScale Raises $142 Million to Enhance Generative AI in Biology

EvolutionaryScale Secures $142 Million in Seed Funding for AI-driven Biological Innovation

The cutting-edge artificial intelligence startup, EvolutionaryScale, has recently closed a successful seed funding round, raising an impressive $142 million. The company’s focus on leveraging generative AI models for biology has garnered significant industry interest and support. With this substantial investment, EvolutionaryScale is poised to revolutionize the field of biology by driving innovation and accelerating discoveries.

Founding Team and Backers Leading the Way

EvolutionaryScale was founded by a team of former Meta AI researchers, including Alexander Rives, Tom Secru, and Sal Candido. With their expertise in machine learning and computational biology, the team has set a strong foundation for the company’s vision and approach. The seed funding round was backed by prominent investors such as Nat Friedman, Daniel Gross, and Lux Capital, along with participation from industry giants like Amazon and Nvidia’s venture capital arm, NVentures. This strong support underscores the industry’s confidence in EvolutionaryScale’s mission and potential.

ESM3: The Frontier Model for Biological Advancements

Central to EvolutionaryScale’s technology is ESM3, an advanced AI model trained on a vast dataset of 2.78 billion proteins. This groundbreaking model has the unique ability to generate novel proteins, opening up new avenues for scientific research and applications. By reasoning over protein sequence, structure, and function, ESM3 can create proteins with specific characteristics and functionalities, fostering innovative developments in various domains.

Enhancing Collaboration and Access to Innovation

To promote accessibility and collaboration, EvolutionaryScale has made ESM3 available for non-commercial use. Additionally, the company has partnered with AWS and Nvidia to provide select customers with access to the model through their platforms. This strategic move aims to empower researchers and developers to leverage ESM3’s capabilities for their projects, facilitating faster and more efficient discovery processes.

Transformative Implications Across Industries

The implications of EvolutionaryScale’s ESM3 model span across multiple industries. In the pharmaceutical sector, the model’s ability to generate novel proteins can significantly expedite drug discovery and development processes. By designing proteins with specific therapeutic properties, researchers can uncover new drug targets and create innovative treatments for various diseases. Moreover, ESM3 has the potential to drive the creation of novel therapeutics, leading to advancements in personalized medicine and targeted therapies.

Beyond healthcare, EvolutionaryScale’s technology holds promise for environmental protection efforts. The model could be instrumental in designing enzymes to degrade plastic waste, offering a sustainable solution to the pressing issue of plastic pollution. Overall, ESM3 has the potential to accelerate scientific research and foster transformative breakthroughs in diverse fields.

Leading the Charge in AI-driven Biological Innovation

EvolutionaryScale’s successful seed funding round signifies a significant milestone in the application of generative AI to biology. With its groundbreaking ESM3 model and a team of experts at the helm, the company is positioned to drive innovation in drug discovery, therapeutics, and environmental solutions. By harnessing the power of AI to design novel proteins, EvolutionaryScale aims to pave the way for scientific breakthroughs and transformative innovations. As the company continues to expand its capabilities and navigate challenges, it has the potential to shape the future of AI-driven biological research and development.
1. How will EvolutionaryScale use the $142 million in funding?
EvolutionaryScale plans to advance generative AI technology in the field of biology by further developing and scaling its platform to drive innovation in drug discovery, personalized medicine, and biological research.

2. What is generative AI and how does it apply to biology?
Generative AI is a form of artificial intelligence that is capable of creating new data, images, or other content based on patterns observed in existing data. In the field of biology, generative AI can be used to model complex biological processes, simulate drug interactions, and predict potential outcomes of genetic mutations.

3. How will EvolutionaryScale’s platform contribute to drug discovery?
EvolutionaryScale’s generative AI platform can be used to identify novel drug candidates, design custom molecules for specific biological targets, and predict drug-drug interactions. By accelerating the drug discovery process, EvolutionaryScale aims to bring new treatments to market faster and more efficiently.

4. How will EvolutionaryScale ensure the ethical use of AI in biology?
EvolutionaryScale is committed to upholding ethical standards in the use of AI technology in biology. The company adheres to guidelines set forth by regulatory bodies and industry best practices to ensure the responsible and transparent application of generative AI in biological research and drug development.

5. What are the potential implications of EvolutionaryScale’s advancements in generative AI for the field of biology?
EvolutionaryScale’s work in advancing generative AI technology has the potential to revolutionize the field of biology by enabling researchers to explore complex biological systems in new ways, discover novel therapeutic interventions, and personalize medical treatments based on individual genetic profiles.
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