Tools & Platforms

UpWest: ‘AI can accelerate the prep, but it can’t replace human judgment’

Published

3 weeks ago

August 12, 2025

“We invest at the earliest stages. The most important work we do is still human and in-person: sitting across from founders, seeing how they think on their feet, how they react to challenges, and whether there’s real chemistry and trust,” said Gil Ben-Artzy, Founding Partner at UpWest. “AI can accelerate the prep, but it can’t replace the nuance of those interactions.”

The VC firm joined CTech for its VC AI Survey, and outlined some of the ways the technology will integrate with human interactions. “Over time, I expect we’ll keep weaving AI deeper into our process, but always as a tool to enhance judgment, not substitute for it,” he added.

You can learn more in the interview below.

Fund ID
Name and Title: Gil Ben-Artzy – Founding Partner
Fund Name: UpWest
Founding Team: Shuly Galili, Gil Ben-Artzy
Founding Year: 2012
Investment Stage: Growth
Investment Sectors: We are a generalist fund and invest as early as it gets in multiple verticals, including cybersecurity, fintech, healthcare, etc. We are focused on helping Israeli founders better access the US market, as we ourselves are based in the US

On a scale of 1 to 10, how has AI impacted your fund’s operations over the past year – specifically in terms of the day-to-day work of the fund’s partners and team members?

I’d put it at about a 7. We’ve meaningfully integrated AI into parts of our workflow, from streamlining due diligence research, to quickly mapping competitive landscapes, to summarizing technical documentation so we can get to the heart of the matter faster. It’s also become a useful companion for portfolio support, including helping draft go-to-market experiments, analyzing customer feedback, and preparing for investor updates.

That said, we invest at the earliest stages. The most important work we do is still human and in-person: sitting across from founders, seeing how they think on their feet, how they react to challenges, and whether there’s real chemistry and trust. AI can accelerate the prep, but it can’t replace the nuance of those interactions. Over time, I expect we’ll keep weaving AI deeper into our process, but always as a tool to enhance judgment, not substitute for it.

Have you already had any significant exits from AI companies? If so, what were the key characteristics of those companies?

SentinelOne — which we backed as their first check — brought AI into endpoint security and became a standout success of the previous AI wave. The new GenAI and agentic AI era is still too early for major exits, but we believe the defining companies are being built now, and we’re backing founders with the vision and patience to create lasting businesses.

In our current fund, nearly every company we’ve backed is built around a strong vertical AI thesis, whether in security, healthcare, legal, infrastructure, and more. One example is Zenity, a security platform for enterprise AI environments, providing visibility, policy enforcement, and threat detection across copilots and low-code tools. The best of these companies move fast, adopt new technologies aggressively, and build defensibility through unique data, integrated workflows, and exceptional user experience — delivering step-change improvements, not incremental gains.

Is identifying promising AI startups different from evaluating companies in your more traditional investment domains? If so, how does that difference manifest?

In many ways, the way we evaluate AI startups is no different from any other company we back: it always starts with the team. We’re looking for founders who are thoughtful, resourceful, and have real insight into the problem they’re solving.

That said, when it comes to AI, we do pay closer attention to defensibility. Models alone aren’t enough. We’re looking at how the company is building moats around proprietary data, embedding into critical workflows, designing for real usage, and delivering a product experience that’s hard to rip out. In a category where speed and competition are intense, creating a lasting advantage matters even more.

What specific financial performance indicators (KPIs) do you examine when assessing a potential AI company? Are there any AI-specific metrics you consider particularly important?

At the stage we invest, there usually aren’t any meaningful financial KPIs to measure. What we’re really evaluating is the team, the size of the opportunity, and how tightly those two are connected. We’re asking: do the founders have a deep understanding of the space? Are they uniquely positioned to build in this domain? Can they move fast and stay focused while the ground shifts beneath them?

If there’s anything we do pay close attention to in AI startups, it’s early signals of usage quality, not just vanity metrics. Are users returning? Are they using it to make real decisions? Are they relying on the AI for real decisions or outcomes? Even in the absence of revenue, that kind of engagement can be a leading indicator of value.

How do you approach the valuation of early-stage AI startups, which often lack significant revenues but possess strong technological potential?

We don’t think about AI valuations all that differently from other early-stage investments. At the end of the day, it still has to make sense for a seed fund, both in terms of risk and ownership. We’re not trying to price the technology itself; we’re backing the team and the opportunity, and setting a structure that gives the company a real shot at the next round.

Of course, AI is a hot category, and valuations can sometimes get ahead of traction. But we try to stay disciplined. The goal isn’t to win the deal at any price, but to find alignment early and build something enduring together.

What financial risks do you associate with investing in AI companies, beyond the usual technological risks?

For us, the bigger financial risk in early AI companies isn’t infrastructure or regulation — it’s when the technology gets ahead of the business model. We’ve seen founders build impressive capabilities that the market isn’t yet ready to adopt, which can burn through capital quickly. In high-stakes sectors, there’s also a trust curve to climb before customers will commit. And in fast-moving categories, today’s edge can become tomorrow’s commodity unless the company locks in adoption and defensibility early.

Do you focus on particular subdomains within AI?

We don’t box ourselves into a single subdomain. Our focus is on the problem being solved and whether AI is the right lever to solve it. That’s led us to back companies across a wide array of opportunities. Some use generative AI, some computer vision, some entirely different approaches. The common thread is a big, urgent problem, founders with deep domain insight, and AI applied in a way that creates a lasting competitive advantage.

How do you view AI’s impact on traditional industries? Are there specific AI technologies you believe will be especially transformative in certain sectors?

AI is transforming both the infrastructure layer and the user experience, but what’s really speeding things up is how visible it is to end users. People are seeing the value for themselves outside of work – often in simple, intuitive interfaces – and then demanding the same capabilities inside their companies. That kind of bottom-up pull is driving adoption faster than anything we’ve seen in years, and it’s one reason we’ve backed vertical AI companies in security, legal, healthcare, manufacturing, and government. We’re excited to keep looking across multiple traditional industries for the next wave of AI-driven transformation.

What specific AI trends in Israel do you see as having strong exit potential in the next five years? Are there niches where you believe Israeli startups particularly excel?

Cybersecurity is an obvious one — it’s a long-time Israeli strength, and AI is creating entirely new attack surfaces. Companies like Zenity are already showing what’s possible. I also think we’ll see more defense-related AI startups emerge here, given the talent and experience base.

Beyond that, I’m especially bullish on infrastructure and vertical AI. Israeli founders are great at building in complex, high-stakes environments, and I expect to see smart, defensible AI deployments across multiple industries where technical depth and domain expertise can translate into meaningful exits over the next 5-10 years..

Are there gaps or missing segments in the Israeli AI landscape that you’ve identified? What types of AI founders are you especially looking to back right now in Israel?

One gap we see is the shortage of AI founders who combine deep technical skill with a deep understanding of the verticals they’re targeting, especially in the US. Israel has world-class talent in building tech, but the biggest breakthroughs happen when you understand the customer’s world as well as the code. That depth of market understanding is what turns strong technology into a category-defining business.

We’re looking for founders who can bridge that gap, bringing the technology, the market insight, and a global mindset from day one. When those elements come together, the potential for impact and outsized outcomes is huge. That’s exactly where we want to lean in: backing founders who can turn AI into real, global businesses.

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Tools & Platforms

AI in Hydrogen Operations: Powering the Future of Clean Energy

Published

7 minutes ago

September 2, 2025

The Editors

The global energy landscape is undergoing a dramatic transformation, and hydrogen is emerging as a cornerstone of the clean energy revolution. But producing, storing, and transporting hydrogen safely and efficiently is no small feat. Enter artificial intelligence (AI), a technological ally that is revolutionizing the way industries manage hydrogen operations. From optimizing production processes to enhancing storage safety and streamlining logistics, AI is redefining what’s possible in the hydrogen sector.

Rising Momentum for AI in Hydrogen

The AI in hydrogen operations market is gaining rapid traction worldwide. Climate concerns, the expansion of green hydrogen facilities, and significant research and development efforts by leading nations are fueling this growth. AI technologies such as machine learning, predictive analytics, digital twins, and automation are being integrated across hydrogen production, storage, and transportation to enhance operational efficiency, safety, and cost-effectiveness.

Europe currently dominates this market, holding 37.1% of the global share in 2024, thanks to stringent decarbonization policies and government-backed initiatives like the EU Hydrogen strategy and the European Green Deal. Meanwhile, Asia Pacific is poised to witness the fastest growth, driven by ambitious projects in countries like India, China, and Japan targeting carbon neutrality through hydrogen adoption.

Key Trends Shaping the Market

Safety & Reliability Through AI: Hydrogen is highly volatile, making safety a top priority. AI-powered monitoring systems can predict equipment failures and detect leaks in real time, minimizing downtime and financial losses. Hybrid models combining fluid dynamics and machine learning are increasingly used to predict leak behavior and prevent accidents.

Optimized Supply Chains: AI streamlines the complex logistics of hydrogen distribution. By analyzing data on transportation routes, geographical factors, and economic considerations, AI identifies the most efficient and cost-effective paths from production facilities to end-users.

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Driving Forces Behind Market Growth

High-Performance Electrolysers: AI optimizes electrolysers — essential for producing green hydrogen from renewable energy. By analyzing real-time sensor data such as temperature, current density, and pressure, AI dynamically adjusts operations for maximum efficiency. Companies like Honeywell are introducing AI-powered solutions like Protonium to make green hydrogen production scalable, cost-effective, and energy-efficient.

Increasing Hydrogen Production: AI’s predictive capabilities help maximize hydrogen output while minimizing waste and energy consumption. From detecting leaks to optimizing storage and transportation, AI opens doors to safer, more efficient, and cost-effective hydrogen operations globally.

Technology Spotlight

Machine Learning & Deep Learning: Dominating the market with a 28.5% share in 2024, ML algorithms analyze vast datasets to improve electrolysis efficiency, accelerate catalyst discovery, and reduce energy consumption.

Digital Twin Technology: Expected to grow at the fastest rate, digital twins simulate entire hydrogen plants, allowing operators to optimize operations and anticipate challenges before they arise.

Applications & Deployment

Hydrogen Production Optimization: AI-driven optimization remains the largest application segment, enhancing energy efficiency, reducing waste, and supporting sustainable hydrogen production.

Hydrogen Storage Management: The fastest-growing segment, AI ensures safe and efficient storage, a critical factor given hydrogen’s volatile nature.

Cloud & Hybrid Deployment: Cloud-based solutions dominate due to scalability and cost-efficiency, while hybrid deployments are gaining momentum for their flexibility, speed, and security.

End-Use Industries

The energy & power sector is the largest adopter of AI-powered hydrogen operations, leveraging AI for predictive maintenance, grid stability, and renewable energy integration. Meanwhile, transportation & mobility is the fastest-growing segment, where AI optimizes hydrogen refueling logistics, ensuring safety and cost-effectiveness.

Global Market Insights

Europe: Strong decarbonization policies and government incentives drive Europe’s leadership in AI-powered hydrogen operations.

Asia Pacific: Ambitious national programs in India, China, and Japan are accelerating growth, targeting millions of tons of green hydrogen production and carbon neutrality by 2030-2060.

Spotlight on Innovation

Recent developments highlight AI’s transformative impact. In July 2025, the world’s largest green hydrogen and ammonia facility was launched in China, fully powered and managed by AI-based renewable energy systems, producing 320,000 tons of green ammonia annually. Similarly, researchers at the University of Toronto leveraged AI to discover new alloys, enhancing the efficiency and affordability of hydrogen production.

Leading Players in the Market

Key companies driving innovation include IBM, Microsoft, Google, Amazon Web Services, Siemens Energy, Schneider Electric, Honeywell, ABB, Rockwell Automation, and Tata Consultancy Services, among others. These players are combining AI, digital twins, and predictive analytics to unlock the full potential of hydrogen as a clean energy source.

AI is no longer just a technological enhancement — it is a strategic necessity for the hydrogen economy. From safer operations to optimized production, AI enables hydrogen to emerge as a reliable, sustainable, and scalable energy solution for a decarbonized future. With global investments pouring in and technological innovations accelerating, the next decade promises a transformative journey for AI in hydrogen operations.
You can place an order or ask any questions. Please feel free to contact us at sales@precedenceresearch.com |+1 804 441 9344

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Tools & Platforms

Latest News In Cloud AI – Rezolve Ai Expands Retail Power With Brain Suite Technology

Published

37 minutes ago

September 2, 2025

Simply Wall St

Rezolve Ai recently announced strong results for the first half of 2025, underscoring the rapid adoption of its AI platform in the global retail sector. The company highlighted its successful integration of AI innovations, crypto-enabled checkout, and its expansion efforts in the U.S. and Europe as key drivers of accelerated revenue growth and increased market presence. Positioned at the intersection of AI and retail, Rezolve Ai is capitalizing on a $30 trillion global opportunity by enhancing customer engagement and operational efficiency through its Brain Suite technology. This development underlines the growing influence and application of AI in retail, reflecting a broader trend toward AI-driven solutions across industries.

Elsewhere in the market, Alibaba Group Holding (NYSE:BABA) was a notable mover up 12.9% and finishing the session at $135.00. At the same time, Ruijie Networks (SZSE:301165) lagged, down 11.6% to end the day at CN¥91.54.

Apple’s expansion in emerging markets and service ecosystem growth could rapidly stabilize margins. Discover more about this investment narrative by exploring the full thesis.

For a deeper understanding, revisit our Market Insights article on Agentic AI, highlighting current opportunities and urgent challenges across industries.

Best Cloud AI Stocks

Alphabet (NasdaqGS:GOOGL) closed at $212.91 up 0.6%, near its 52-week high.
Apple (NasdaqGS:AAPL) closed at $232.14 down 0.2%.
On Monday, the company previewed its newest store, Apple Hebbal, in Bengaluru, marking its first in South India.
Microsoft (NasdaqGS:MSFT) closed at $506.69 down 0.6%.
This week, the company announced its first in-house AI models, MAI-Voice-1 and MAI-1 Preview, marking its move toward developing proprietary AI technology.

Turning Ideas Into Actions

This article by Simply Wall St is general in nature. We provide commentary based on historical data
and analyst forecasts only using an unbiased methodology and our articles are not intended to be financial advice. It does not constitute a recommendation to buy or sell any stock, and does not take account of your objectives, or your
financial situation. We aim to bring you long-term focused analysis driven by fundamental data.
Note that our analysis may not factor in the latest price-sensitive company announcements or qualitative material.
Simply Wall St has no position in any stocks mentioned.

Sources:

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Tools & Platforms

7 Essential AI Terms You Need to Know in 2025

Published

1 hour ago

September 2, 2025

Julian Horsey

What if the future of technology wasn’t just about tools, but about systems that think, learn, and act with purpose? Artificial intelligence (AI) is no longer a distant frontier, it’s here, reshaping industries and redefining what’s possible. Yet, as AI becomes more integrated into our lives, the language surrounding it can feel complex and overwhelming. Terms like Agentic AI or Retrieval-Augmented Generation (RAG) might sound like jargon, but they hold the key to understanding how AI is transforming everything from healthcare to e-commerce. If you’ve ever felt like you’re standing on the edge of a technological revolution without a map, you’re not alone. This report is here to help you decode the essential concepts driving AI innovation.

In the following sections, IBM Technology discuss seven pivotal AI terms that are shaping the future of technology. From autonomous systems that make decisions independently to semantic search tools that understand meaning rather than just words, these concepts reveal the innovative advancements powering today’s AI breakthroughs. Whether you’re a tech enthusiast, a professional navigating AI-driven industries, or simply curious about what’s next, this guide will provide clarity and insight. By the end, you’ll not only recognize these terms but also grasp their significance in the broader AI landscape. Understanding these ideas isn’t just about keeping up, it’s about staying ahead in a world where intelligence is no longer exclusively human.

Key AI Concepts Explained

TL;DR Key Takeaways :

Agentic AI: Autonomous systems capable of perceiving, reasoning, and acting independently to achieve specific goals, enhancing efficiency in industries like healthcare, logistics, and software development.
Large Reasoning Models: Advanced AI models designed for step-by-step problem-solving, excelling in fields requiring precision such as law, finance, and scientific research.
Vector Databases: Enable semantic search by analyzing contextual meaning, improving personalization and user experience in applications like e-commerce, image recognition, and natural language processing.
Retrieval-Augmented Generation (RAG): Combines knowledge retrieval with language generation to produce accurate, contextually informed AI outputs, benefiting industries like customer service and journalism.
Artificial Superintelligence (ASI): A theoretical stage where machines surpass human intelligence, offering potential solutions to global challenges but raising significant ethical and safety concerns.

Agentic AI: Autonomous Systems with Purpose

Agentic AI refers to autonomous systems capable of perceiving their environment, reasoning through complex scenarios, and taking purposeful actions to achieve specific goals. These systems operate independently, making decisions without requiring constant human input.

For example:

Autonomous vehicles rely on agentic AI to navigate roads, adapt to traffic conditions, and ensure passenger safety.
Virtual assistants act as travel planners or data analysts, learning from user interactions and improving over time.

Their ability to adapt in real-time makes them invaluable in dynamic fields such as logistics, healthcare, and software development. By reducing the need for human intervention, agentic AI enhances efficiency and decision-making across industries.

Large Reasoning Models: Advanced Problem-Solving

Large reasoning models are a specialized subset of large language models (LLMs) designed to excel at step-by-step reasoning. Unlike general-purpose LLMs, these models break down complex problems into smaller, manageable steps, allowing more precise and logical outcomes.

Applications include:

Mathematical proofs and solving intricate calculations.
Legal document analysis to identify critical clauses and inconsistencies.
Scientific research requiring high-level cognitive processing and hypothesis testing.

By using vast training data and reasoning capabilities, these models are becoming indispensable tools in industries that demand precision, such as law, finance, and academia.

Agents, RAG, ASI & More Explained

Advance your skills in AI by reading more of our detailed content.

Vector Databases: Unlocking Semantic Search

Vector databases store information as numerical vectors, allowing efficient semantic similarity searches. Unlike traditional keyword-based searches, vector databases analyze the contextual meaning of data points, making them ideal for applications requiring nuanced understanding. Examples of use cases:

E-commerce platforms suggesting products based on user preferences and browsing history.
Image recognition systems identifying visually similar images for cataloging or security purposes.
Natural language processing tools improving search accuracy by understanding the intent behind queries.

This technology enhances personalization and user experience across industries, from retail to media, by delivering more relevant and context-aware results.

Retrieval-Augmented Generation (RAG): Merging Knowledge and Language

Retrieval-Augmented Generation (RAG) combines knowledge retrieval with language generation, creating AI systems capable of producing accurate, contextually informed responses. By integrating vector databases with LLMs, RAG enriches AI outputs with relevant external information. Practical applications include:

Customer support chatbots retrieving specific product details to provide precise and helpful answers.
AI tools generating reports enriched with real-time data, making sure relevance and accuracy.

This approach enhances the reliability of AI-generated content, making it a valuable tool for industries such as customer service, journalism, and research.

Model Context Protocol (MCP): Standardizing AI Integration

The Model Context Protocol (MCP) provides a standardized framework for connecting AI models to external tools and data sources. By defining clear integration protocols, MCP simplifies how AI systems interact with APIs, databases, and other platforms. Benefits of MCP include:

Streamlined workflows across diverse systems, reducing integration complexity.
Enhanced functionality for AI applications in sectors like finance, healthcare, and logistics.

For instance, MCP enables an AI-powered financial advisor to seamlessly integrate with real-time market data, improving decision-making efficiency and accuracy.

Mixture of Experts (MoE): Optimizing Neural Networks

The Mixture of Experts (MoE) architecture optimizes neural networks by selectively activating specific subnetworks, or “experts,” tailored to the task at hand. This targeted approach reduces computational overhead while maintaining high performance. Key advantages of MoE:

Improved efficiency for large-scale AI models, allowing them to handle complex tasks with reduced resource usage.
Scalability for applications such as natural language understanding, image processing, and speech recognition.

By engaging only the necessary parts of the network, MoE ensures resource efficiency without compromising accuracy, making it a critical innovation for scaling AI technologies.

Artificial Superintelligence (ASI): Theoretical Frontiers

Artificial Superintelligence (ASI) represents a hypothetical stage where machines surpass human intelligence across all domains. ASI would possess the ability to improve itself recursively, potentially leading to exponential technological advancements. While ASI remains speculative, its potential implications are profound:

Solving global challenges such as climate change, disease eradication, and resource scarcity.
Raising ethical concerns about control, safety, and societal impact, requiring careful oversight and regulation.

Understanding ASI is essential for preparing for its possible emergence and addressing the risks it may pose. Its development could redefine the boundaries of human achievement and responsibility.

Understanding the Future of AI

These seven AI concepts, Agentic AI, Large Reasoning Models, Vector Databases, Retrieval-Augmented Generation, Model Context Protocol, Mixture of Experts, and Artificial Superintelligence, highlight the fantastic potential of artificial intelligence. By familiarizing yourself with these terms, you gain a deeper understanding of the technologies shaping the future and their implications for industries and society. Staying informed about these foundational ideas equips you to navigate the complexities and opportunities of AI as it continues to evolve.

Media Credit: IBM Technology

Filed Under: AI, Top News

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