This AI-Driven Startup Studio Aims to Launch 100,000 Companies Annually—No Kidding!

How Henrik Werdelin’s Audos is Revolutionizing Entrepreneurship with AI

Henrik Werdelin has dedicated the past 15 years to empowering entrepreneurs in creating major brands like Barkbox through his startup studio Prehype. Now, he aims to scale that momentum with his latest New York-based venture, Audos, which leverages AI to support “hundreds of thousands” of aspiring business owners each year.

The Perfect Timing for a New Venture

Given the current climate of mass layoffs across multiple industries, many professionals are reconsidering their career paths. Coupled with advancements in AI that significantly lower the barriers to developing digital products, Werdelin’s latest venture positions itself at a pivotal intersection. Audos aims to empower “everyday entrepreneurs” to forge million-dollar AI companies without needing technical skills.

A Shift in Entrepreneurial Focus

Werdelin’s transition from Prehype to Audos illustrates the evolving landscape of entrepreneurship. At Prehype, the emphasis was on collaborating with tech founders to create traditional startups that attract significant investment. Now, Werdelin articulates a broader mission: “We’re trying to democratize the knowledge and methodologies we’ve developed over the years for building successful companies.”

Empowering Everyday Entrepreneurs

Recognizing that many aspiring entrepreneurs may feel uncertain about entering the AI space or lack access to customers, Audos is eager to offer support. By providing cutting-edge AI tools that facilitate product development via natural language and capitalizing on social media algorithms, Audos helps users identify their target market.

Leveraging Algorithms for Customer Reach

“Platforms like Facebook are incredible at optimizing customer outreach if you clearly define your target audience,” explains Werdelin, co-founder of Audos alongside Nicholas Thorne. Audos employs sophisticated methods to rapidly assess whether a business idea has viable customer acquisition costs.

Successful Launches and Real-World Applications

Since its beta launch, Audos has successfully facilitated the creation of “low hundreds” of businesses. Founders have discovered Audos through targeted Instagram ads asking, “Have you ever thought about starting something, but don’t know where to go?” Their diverse projects range from car mechanics helping clients evaluate repair quotes to AI nutritionists and funeral logistics services. Werdelin affectionately labels these innovative small teams as “donkeycorns,” a nod to the unicorn model.

How Audos Operates: A Unique Revenue Model

Operating under a distinctive model, Audos does not take equity from the businesses it assists. Instead, it takes a 15% revenue share from the ventures it launches, while providing founders with up to $25,000 in funding, access to AI-driven business tools, and promotional support through paid social media ads.

Long-Term Considerations for Entrepreneurs

Werdelin states, “We’re not taking any equity in their business,” highlighting the focus on fostering grassroots entrepreneurship. However, some may view the continuous revenue cut as a significant trade-off, potentially costing entrepreneurs substantial amounts over time. The long-term implications of this model are ripe for debate.

Future Outlook and Investment Confidence

Despite potential concerns, Audos’s investors, including True Ventures—who led an $11.5 million seed round—are optimistic. Partner Tony Conrad observes that simple entrepreneurial ideas executed well, similar to Instagram, can lead to substantial success, even when the company is not pursuing billion-dollar exits.

The Vision for a Million $1M Businesses

Werdelin envisions a future where “we create a million companies generating million-dollar revenues.” This ambition translates into a trillion-dollar business landscape, reinforcing the importance of providing resources and support to individuals who previously lacked access to startup capital or expertise.

Emphasizing the Value of Small Businesses

Werdelin emphasizes the need for support tailored to smaller entrepreneurs who may not be on the radar of traditional venture capital. “The world benefits from increased entrepreneurship,” he asserts, echoing the sentiments of Audos’s diverse group of investors, including Offline Venture, Bungalow Capital, and prominent angel investors.

Pictured above, left to right, are Audos co-founders Nicholas Thorne and Henrik Werdelin.

Here are five FAQs regarding an AI-powered startup studio that plans to launch 100,000 companies a year:

FAQ 1: What is an AI-powered startup studio?

Answer: An AI-powered startup studio is a venture that utilizes artificial intelligence to research, develop, and launch new business ideas at scale. By leveraging AI technologies, the studio aims to streamline the startup process, from ideation to execution, making it possible to launch multiple companies simultaneously.

FAQ 2: How does the startup studio plan to launch 100,000 companies a year?

Answer: The studio plans to use advanced AI algorithms to identify market trends, consumer needs, and viable business models. By automating significant parts of the startup creation process, including market research, product development, and marketing, they aim to rapidly prototype and launch numerous companies each year.

FAQ 3: What types of businesses will the studio focus on?

Answer: The startup studio intends to diversify its portfolio by exploring various industries and domains, including tech, e-commerce, health, and more. Their AI systems will identify sectors with the highest potential for growth and innovation, allowing for a wide range of business opportunities.

FAQ 4: How will this impact traditional entrepreneurship?

Answer: This startup studio could democratize entrepreneurship by lowering barriers to entry and reducing startup costs. While it may create competition, it also offers traditional entrepreneurs access to innovations, mentoring, and potential collaboration opportunities, fostering an evolving ecosystem.

FAQ 5: How can aspiring entrepreneurs get involved?

Answer: Aspiring entrepreneurs can get involved by applying to work within the studio, pitching their business ideas, or collaborating on specific projects. The studio may offer resources, mentorship, and funding opportunities for those selected to partner with them, enabling them to harness the power of AI while driving their own ventures forward.

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Evogene and Google Cloud Launch Groundbreaking Foundation Model for Generative Molecule Design, Ushering in a New Era of AI in Life Sciences

<h2>Evogene Unveils Revolutionary AI Model for Small-Molecule Design</h2>

<p>On June 10, 2025, Evogene Ltd. announced a groundbreaking generative AI foundation model for small-molecule design, developed in partnership with Google Cloud. This innovative model marks a significant leap forward in the discovery of new compounds, answering a long-standing challenge in pharmaceuticals and agriculture—identifying novel molecules that fulfill multiple complex criteria simultaneously.</p>

<h3>Transforming Drug Discovery and Crop Protection</h3>

<p>The new model enhances Evogene’s ChemPass AI platform, aiming to expedite research and development (R&D) in drug discovery and crop protection. By optimizing factors such as efficacy, toxicity, and stability within a single design cycle, this development has the potential to reduce failures and accelerate timelines significantly.</p>

<h3>From Sequential Screening to Simultaneous Design</h3>

<p>Traditionally, researchers have followed a step-by-step approach, evaluating one factor at a time—first efficacy, then safety, and finally stability. This method not only prolongs the discovery process but also contributes to a staggering 90% failure rate for drug candidates before they reach the market. Evogene's generative AI changes this model, enabling multi-parameter optimization from the outset.</p>

<h3>How ChemPass AI Works: A Deep Dive</h3>

<p>At the core of the ChemPass AI platform lies an advanced foundation model trained on an extensive dataset of approximately 40 billion molecular structures. This curated database allows the AI to learn the "language" of molecules, leveraging Google Cloud’s Vertex AI infrastructure for supercomputing capabilities.</p>

<p>The model, known as ChemPass-GPT, employs a transformer neural network architecture—similar to popular natural language processing models. It interprets molecular structures as sequences of characters, enabling it to generate novel SMILES strings that represent chemically valid, drug-like structures.</p>

<h3>Overcoming Previous Limitations in AI Models</h3>

<p>The performance of ChemPass AI surpasses standard AI models, achieving up to 90% precision in generating novel molecules that meet all specified design criteria. This level of accuracy significantly reduces reliance on traditional models, which historically struggled with bias and redundancy.</p>

<h3>Multi-Objective Optimization: All Criteria at Once</h3>

<p>A standout feature of ChemPass AI is its capacity for simultaneous multi-objective optimization. Unlike traditional methods that optimize individual properties one at a time, this AI can account for various criteria—from potency to safety—thereby streamlining the design process.</p>

<h3>Integrating Multiple AI Techniques</h3>

<p>The generative model integrates different machine learning methodologies, including multi-task learning and reinforcement learning. By continuously adjusting its strategy based on multiple objectives, the model learns to navigate complex chemical spaces effectively.</p>

<h3>Advantages Over Traditional Methods</h3>

<ul>
    <li><strong>Parallel Optimization:</strong> AI analyzes multiple characteristics simultaneously, enhancing the chances of success in later trials.</li>
    <li><strong>Increased Chemical Diversity:</strong> ChemPass AI can generate unprecedented structures, bypassing the limitations of existing compound libraries.</li>
    <li><strong>Speed and Efficiency:</strong> What would take human chemists a year can be accomplished in days with AI, expediting the discovery process.</li>
    <li><strong>Comprehensive Knowledge Integration:</strong> The model incorporates vast amounts of chemical and biological data, improving design accuracy and effectiveness.</li>
</ul>

<h3>A Broader AI Strategy at Evogene</h3>

<p>While ChemPass AI leads the charge in small-molecule design, it is part of a larger suite of AI engines at Evogene, including MicroBoost AI for microbes and GeneRator AI for genetic elements. Together, they represent Evogene's commitment to revolutionizing product discovery across various life science applications.</p>

<h3>The Future of AI-Driven Discovery</h3>

<p>The launch of Evogene’s generative AI model signals a transformative shift in small-molecule discovery, allowing scientists to design compounds that achieve multiple goals—like potency and safety—in one step. As future iterations become available, customization options may expand, further enhancing their utility across various sectors, including pharmaceuticals and agriculture.</p>

<p>The effectiveness of these generative models in real-world applications will be vital for their impact. As AI-generated molecules undergo testing, the loop between computational design and experimental validation will create a robust feedback cycle, paving the way for breakthroughs in not just drugs and pesticides, but also materials and sustainability innovations.</p>

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Here are five FAQs with answers regarding the collaboration between Evogene and Google Cloud for their foundation model in generative molecule design:

FAQ 1: What is the foundation model for generative molecule design developed by Evogene and Google Cloud?

Answer: The foundation model is an advanced AI framework that leverages generative modeling techniques and machine learning to design and optimize molecules for various applications in life sciences. This model enables researchers to predict molecular behaviors and interactions, significantly accelerating the drug discovery and development process.

FAQ 2: How does this collaboration between Evogene and Google Cloud enhance drug discovery?

Answer: By utilizing Google Cloud’s computational power and scalable infrastructure, Evogene’s generative model can analyze vast datasets to identify promising molecular candidates. This partnership allows for faster simulations and analyses, helping to reduce the time and cost associated with traditional drug discovery methods while increasing the likelihood of successful outcomes.

FAQ 3: What potential applications does the generative model have in the life sciences?

Answer: The generative model can be used in various applications, including drug discovery, agricultural biotechnology, and the development of innovative therapeutic agents. It helps in designing novel compounds that can act on specific biological targets, leading to more effective treatments for a range of diseases.

FAQ 4: How does the use of AI in molecule design impact the future of life sciences?

Answer: AI-driven molecule design is poised to revolutionize the life sciences by enabling faster innovation and more precise targeting in drug development. With enhanced predictive capabilities, researchers can create tailored solutions that meet specific needs, ultimately leading to more effective therapies and improved health outcomes.

FAQ 5: What are the next steps for Evogene and Google Cloud following this announcement?

Answer: Following the unveiling of the foundation model, Evogene and Google Cloud plan to further refine their technologies through ongoing research and development. They aim to collaborate with various stakeholders in the life sciences sector to explore real-world applications and expand the model’s capabilities to address diverse challenges in drug discovery and molecular design.

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What OpenAI’s o1 Model Launch Reveals About Their Evolving AI Strategy and Vision

OpenAI Unveils o1: A New Era of AI Models with Enhanced Reasoning Abilities

OpenAI has recently introduced their latest series of AI models, o1, that are designed to think more critically and deeply before responding, particularly in complex areas like science, coding, and mathematics. This article delves into the implications of this launch and what it reveals about OpenAI’s evolving strategy.

Enhancing Problem-solving with o1: OpenAI’s Innovative Approach

The o1 model represents a new generation of AI models by OpenAI that emphasize thoughtful problem-solving. With impressive achievements in tasks like the International Mathematics Olympiad (IMO) qualifying exam and Codeforces competitions, o1 sets a new standard for cognitive processing. Future updates in the series aim to rival the capabilities of PhD students in various academic subjects.

Shifting Strategies: A New Direction for OpenAI

While scalability has been a focal point for OpenAI, recent developments, including the launch of smaller, versatile models like ChatGPT-4o mini, signal a move towards sophisticated cognitive processing. The introduction of o1 underscores a departure from solely relying on neural networks for pattern recognition to embracing deeper, more analytical thinking.

From Rapid Responses to Strategic Thinking

OpenAI’s o1 model is optimized to take more time for thoughtful consideration before responding, aligning with the principles of dual process theory, which distinguishes between fast, intuitive thinking (System 1) and deliberate, complex problem-solving (System 2). This shift reflects a broader trend in AI towards developing models capable of mimicking human cognitive processes.

Exploring the Neurosymbolic Approach: Drawing Inspiration from Google

Google’s success with neurosymbolic systems, combining neural networks and symbolic reasoning engines for advanced reasoning tasks, has inspired OpenAI to explore similar strategies. By blending intuitive pattern recognition with structured logic, these models offer a holistic approach to problem-solving, as demonstrated by AlphaGeometry and AlphaGo’s victories in competitive settings.

The Future of AI: Contextual Adaptation and Self-reflective Learning

OpenAI’s focus on contextual adaptation with o1 suggests a future where AI systems can adjust their responses based on problem complexity. The potential for self-reflective learning hints at AI models evolving to refine their problem-solving strategies autonomously, paving the way for more tailored training methods and specialized applications in various fields.

Unlocking the Potential of AI: Transforming Education and Research

The exceptional performance of the o1 model in mathematics and coding opens up possibilities for AI-driven educational tools and research assistance. From AI tutors aiding students in problem-solving to scientific research applications, the o1 series could revolutionize the way we approach learning and discovery.

The Future of AI: A Deeper Dive into Problem-solving and Cognitive Processing

OpenAI’s o1 series marks a significant advancement in AI models, showcasing a shift towards more thoughtful problem-solving and adaptive learning. As OpenAI continues to refine these models, the possibilities for AI applications in education, research, and beyond are endless.

  1. What does the launch of OpenAI’s GPT-3 model tell us about their changing AI strategy and vision?
    The launch of GPT-3 signifies OpenAI’s shift towards larger and more powerful language models, reflecting their goal of advancing towards more sophisticated AI technologies.

  2. How does OpenAI’s o1 model differ from previous AI models they’ve developed?
    The o1 model is significantly larger and capable of more complex tasks than its predecessors, indicating that OpenAI is prioritizing the development of more advanced AI technologies.

  3. What implications does the launch of OpenAI’s o1 model have for the future of AI research and development?
    The launch of the o1 model suggests that OpenAI is pushing the boundaries of what is possible with AI technology, potentially leading to groundbreaking advancements in various fields such as natural language processing and machine learning.

  4. How will the launch of the o1 model impact the AI industry as a whole?
    The introduction of the o1 model may prompt other AI research organizations to invest more heavily in developing larger and more sophisticated AI models in order to keep pace with OpenAI’s advancements.

  5. What does OpenAI’s focus on developing increasingly powerful AI models mean for the broader ethical and societal implications of AI technology?
    The development of more advanced AI models raises important questions about the ethical considerations surrounding AI technology, such as potential biases and risks associated with deploying such powerful systems. OpenAI’s evolving AI strategy underscores the importance of ongoing ethical discussions and regulations to ensure that AI technology is developed and used responsibly.

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