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Pymnts
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DeepSeek Considers Raising Outside Funding Amid Demand for AI Chatbot
- Chinese AI startup DeepSeek is considering raising outside funding amid increasing demand for its AI chatbot app.
- The company, which was formed as a subsidiary of High-Flyer Capital Management, has not previously raised outside money.
- DeepSeek has been cautious about raising money from major Chinese investors to avoid potential limitations on business opportunities in the US.
- However, the surge in the use of its AI chatbot and the need for more AI chips and servers have prompted DeepSeek to consider outside funding.
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TheStartupMag
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As investor activity enjoys a resurgence, here are 10 breakthrough trends and companies to watch in 2025
- In Q3 of 2024, AI-related startups secured $19 billion USD, making up 28% of total venture capital dollars.
- The renewed interest in AI has revitalized the tech sector post-pandemic drop in funding.
- A diverse range of tech companies are offering solutions to new market trends as VC activity rises.
- Breakthrough trends in 2025 include startups in the pet care and mobile space, intelligent engineering, and accounting software tailored for accounting needs.
- Companies like Leap Venture Studio and Sim Local are addressing specific needs in pet care and mobile space, respectively.
- Intelligent Engineering by Ness Digital Engineering aims to increase productivity in software development cycles using data and AI.
- SkyStem and Prezent are providing innovative solutions in accounting and AI-powered communications, respectively.
- Innovations in plant care technology, sustainable ingredient production, and AI in the classroom are also notable trends.
- Viking Pure Solutions offers safe cleaning solutions, while DeepSeek and SQream are changing the narrative in AI and datasets using GPUs.
- Exciting developments and investments are expected in these areas as investor activity rebounds in 2025.
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Medium
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Surfing Nazaré Without Getting Swallowed: How Small VCs Can Compete in a New Era of Venture Capital
- Venture capital landscape dominated by large firms like Andreessen Horowitz and Lightspeed.
- Small VC firms need strategic navigation and timing to compete effectively.
- Emerging managers must adapt to the forces that define the current venture market.
- Firms that can surf the waves without getting swallowed will define the next generation of VC.
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Insider
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ScorePlay, which supports sports organizations like Major League Soccer, raises $13 million from VCs and athletes
- ScorePlay, a content management system for sports organizations, has raised $13 million in Series A funding.
- The platform is used by Major League Soccer teams, including Inter Miami, to manage and distribute content efficiently.
- ScorePlay provides an AI-powered centralized platform for uploading, tagging, and managing content, enabling quick asset delivery and multilingual distribution.
- The funding round was led by VC firms and included investment from athletes such as NBA star Giannis Antetokounmpo and soccer great Alex Morgan.
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Medium
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Food Tech’s Future Is Infrastructure: Our 2025 Food Tech Thesis
- Supply Chain Resilience & Optimization: The focus is on the entire ecosystem, from sourcing to delivery, and technologies that ensure end-to-end resilience.
- Digital Transformation of Sales & Marketing: Opportunities lie in improving how companies engage with their markets and customers, aiming for efficiency and effectiveness.
- Intelligent Automation & Robotics: Labor challenges in the food system are being intensified, leading to a need for practical solutions that deliver clear ROI.
- Bread and Butter welcomes innovations that address the challenges of the food industry and is eager to connect and learn about future visions.
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Medium
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Exploring Learning Japanese with AI as a Language Tutor
- Exploring Learning Japanese with AI as a Language Tutor
- OpenAI's ChatGPT was used to practice speaking Japanese and simulate common scenarios.
- ChatGPT offered flexibility, while Duolingo had a structured, gamified approach for language learning.
- The AI tool served well for everyday encounters, but couldn't replace the dynamics of human interaction in language learning.
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Medium
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Catalyzing Tomorrow: Unveiling Agentic AI’s Role in Shaping the Future of Business
- AI is transitioning from being just a tool to an independent operator, driving real-world adoption and accelerating the shift towards autonomy in decision-making and task execution.
- Agentic AI is playing a crucial role in making real-time business decisions, performing tasks, and optimizing workflows without the need for human intervention, marking a significant evolution in AI capabilities.
- The shift towards agentic AI mirrors the transformative journey seen in technologies like SaaS, cloud computing, and mobile internet, which have started as niche products and gradually become mainstream ways of operating.
- Companies are consolidating fragmented AI use cases into end-to-end workflow automation, with the emergence of tailored AI agents for customer support, enterprise solutions, and industry-specific tasks.
- Businesses are transitioning towards AI-native architectures where agentic AI drives entire workflows, replacing point solutions and enhancing both software efficiency and human intervention.
- The trend towards hyper-personalization in AI is leading to models tailored for specific industries, datasets, and workflows, enabling deep training on industry-specific data and enhancing situational adaptability.
- At the core of most AI agents are Large Language Models (LLMs), which, despite their limits, are being complemented by advancements in model architecture to push AI capabilities beyond current boundaries.
- Investors are focusing on the trajectory of Agentic AI, leading to a competitive marketplace ecosystem for innovation, with AI becoming a competitive essential for B2B SaaS companies.
- The future of AI lies in tailored solutions for every function, deeply trained on industry-specific data, with the next five years expected to be characterized by AI running businesses rather than just assisting them.
- Companies are urged to invest in architectures supporting agentic AI systems, implement real-time analytics for sharper automation, and strike a balance between AI autonomy and human oversight to build evolving workflows.
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Saastr
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5 Interesting Learnings from Similarweb at $260,000,000 ARR
- Similarweb, a company that IPO'd at $190m ARR, is now at $260m ARR with a growth rate of 16% and 112% NRR.
- The company is trading at $841m, or about 3.2x ARR, and its value has halved since the IPO in 2021.
- While Similarweb's growth is modest, it is re-accelerating from 11% in Q4'23 to 16% currently.
- The company is experiencing growth in net new customer count at a rate of 20% and is focusing on $100k+ deals.
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Saastr
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The 2025 SaaS Vibe Check: What Founders Need to Know Right Now with SaaStr CEO and Founder Jason Lemkin
- SaaStr CEO and Founder Jason Lemkin discussed the state of SaaS, AI, and venture funding in 2025.
- Growth is prioritized over profitability in today's market; companies are valued based on growth multiples.
- AI-native companies are seeing easier fundraising due to their growth rates and innovation.
- SaaS companies are either experiencing rapid growth or stagnation, with little in between.
- Founders are advised to slow down the VC funding process and show thoughtful execution.
- Hiring the right first sales rep is crucial for accelerating growth from $1M to $3M+.
- AI is dominating VC funding in 2025, making it essential for startups to have an AI component.
- Efficiency matters, but growth remains the top priority for companies like Shopify and PagerDuty.
- AI-driven companies like Cursor AI and Wiz are showing exceptional growth rates.
- Raising funds without a strong AI focus is challenging; personalization and AI curiosity are key for successful fundraising.
- The sales and marketing playbook is evolving, emphasizing the importance of strategic hiring and adapting to new market dynamics.
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Medium
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Layout of a SaaS Model
- The SaaS Model follows a similar layout as previous models, with the structure: Output < Model < Schedules < Data.
- The Summary and Assumptions are placed side by side to quickly visualize the impact of input changes and stress-test the model.
- The Summary + Assumptions tab can control 95% of the entire model, allowing for easy comparison between scenarios.
- The three-statement operating model is the recipient of a lot of information, driven by assumptions and custom drivers for revenue and expenses.
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Siliconangle
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Gomboc AI secures $13M to tackle cloud security backlogs with deterministic AI
- Gomboc.AI Inc. has raised $13 million in seed funding to address cloud security backlogs with deterministic AI.
- Gomboc.AI offers a deterministic AI solution for cloud security remediations, ensuring consistent and predictable outcomes.
- The platform provides precise, automated code-level fixes for security issues, reducing the number of cloud security vulnerabilities.
- Gomboc.AI's policy-driven framework and reliance on deterministic AI differentiate it from generative AI approaches.
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Insider
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A startup has raised $3.9 million from Nat Friedman and Daniel Gross to solve AI's unstructured data bottleneck
- Pulse, a startup specializing in unstructured data preparation for machine learning models, has raised $3.9 million in a funding round led by Nat Friedman and Daniel Gross.
- Pulse offers a toolkit that helps businesses convert raw, unstructured data into formats suitable for machine learning models, addressing the demand for custom chatbots and digital agents tailored to internal data.
- The funding round also included investors such as Y Combinator, Sequoia Scout, Soma Capital, and individuals from Nvidia, OpenAI, and fintech startup Ramp.
- Pulse's technology uses computer vision techniques and fine-tuned extraction models to automate the process of converting messy data into training data, resulting in increased efficiency and accuracy.
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Medium
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Tokenization’s Role in Private Equity and Venture Capital
- Tokenization, powered by blockchain, is revolutionizing the landscape of private equity and venture capital by making investments more accessible and liquid.
- Asset tokenization in finance is transforming private market investments, increasing financial inclusion, and efficiency.
- Tokenization in private equity and venture capital converts traditional investments into digital tokens on blockchain platforms, ensuring security and transparency.
- Smart contracts in private equity enable fractional ownership, automated compliance, and reduced need for intermediaries, making investments more accessible.
- Benefits of private equity tokenization include enhanced liquidity, reduced minimum investment requirements, improved operational efficiency, and real-time settlement.
- Venture capital tokenization offers broader investor access, improved portfolio management, enhanced exit options, and transparent governance via smart contracts.
- Asset tokenization faces regulatory challenges, but jurisdictions are developing frameworks to govern tokenized securities ensuring investor protection.
- The future of tokenization in private equity and venture capital looks promising with increasing adoption among traditional firms and advancements in technology.
- STOEX is a platform facilitating tokenized real-world asset trading with a focus on regulatory compliance, security, and accessibility for investors.
- Tokenization in the financial domain reshapes how investments are managed, traded, and structured, promising increased accessibility, liquidity, security, and efficiency.
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Saastr
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Dear SaaStr: What Are The Traits That Define a Great VP?
- A Good VP Maintains Velocity in Their Functional Area
- A Great VP Increases Velocity in Their Functional Area
- A Mediocre VP Oversees Slowly Decelerating Velocity
- A Bad VP Oversees Rapidly Decelerating Velocity
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Saastr
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The Only 3 Customer Success Metrics That Actually Matter for Building a $100M ARR Business with ChurnZero’s CEO
- Customer Success metrics NRR and NPS are essential, but insufficient for building sustainable growth.
- UFR (Up For Renewal) is crucial for knowing the dollars you can retain and requires detailed calculations.
- Building a UFR machine involves data audit, process definition, and operational integration over 90 days.
- GRR (Gross Revenue Retention) is an early warning system for product-market fit and requires investment and onboarding excellence.
- CSAT (Customer Satisfaction) is a real-time customer health monitor that needs moment-based measurement and response protocols.
- Key benchmarks for GRR range from 80-95% based on company size, with red flags including variance and onboarding issues.
- CSAT segmentation strategies include user role, customer size, industry, and product module usage.
- Implementing UFR, CSAT, and GRR systems within a structured 30-60-90 day plan can lead to building a scalable CS organization.
- Start measuring these metrics now to lay the foundation for sustainable growth in Customer Success.
- Join SaaStr Annual for more insights on scaling Customer Success with industry leaders.
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