Gleb Budman

We're launching the inaugural edition of Beyond Benchmarks, a deep dive into the metrics and trends observed across the early-stage enterprise cloud market. This is the first look at some of the business data around GenAI in SaaS. Some initial observations: 1) 60% of SaaS companies have already integrated GenAI into their offerings (60% of these as part of their current product and 40% as a new product), and an additional 20% are planning to do so this year. Almost half aren't monetizing these features. Those that are are primarily experimenting with both usage-based and flat fee pricing models. 2) Most companies are utilizing OpenAI as their primary LLM, but many are experimenting with multiple models - and we are noticing an early trend towards intelligently routing GenAI inference requests to different models based on cost, performance, and security. 3) Though we are in the initial innings of GenAI, we are seeing some positive signals - companies that implemented GenAI had 7% higher NDR than those that did not. Still early days, but we'll be doing this benchmarking report regularly to track and share how things progress in this new era. Link to report in comments.

104

6 Comments

AI memory is fundamental for conversational AI and AI agents. It is critical for building personalized experiences and improving reasoning. Check out Letta’s course to understand how to use AI memory to enhance the customization and quality of your AI app.

15

ex Google CEO Eric Schmidt spoke at Stanford this week. Lots of nuggets here, but most interesting for the near term, Schmidt believes in the next year AI is set to make a big leap forward. He predicts AI will combine three important features: 1. Vast knowledge retention (1 million token models) 2. Text-to-action capability 3. Personal AI agent fleets While the full impact is unknown, everyone may soon command their own AI teams. Here's an example: Imagine you're developing new quantum computing algorithms. Your AI assistants could: - Continuously scan and synthesize global quantum research papers - Generate novel algorithm ideas based on this knowledge - Simulate and test these algorithms across various quantum architectures - Automatically document findings and prepare draft papers - Identify potential collaborators and draft outreach emails All initiated by a command like "Advance our quantum algorithm research." This could dramatically accelerate R&D cycles and scientific breakthroughs.. https://lnkd.in/eJbg6cnD #frontiertech #AI #futurefrontier

22

9 Comments

PE - How to Use AI to Unlock Growth in Portfolio Companies All PE firms and portfolio companies should be thinking about unlocking GTM and growth via AI (i.e. not merely ChatGPT or other LLMs but actually implementing apps that are "AI Agents"). 1. What are "AI Agents"? - AI agents are autonomous systems powered by ML and natural language processing, designed to perform tasks with minimal human intervention - They can interpret data, determine the workflow & direction (i.e. make decisions), and execute actions across various functions, continuously improving through self-learning 2. Examples of using AI Agents in PE portfolio companies: - For Sales: AI agents handle lead qualification, respond to inquiries, book meetings, and provide personalized product recommendations, ensuring no opportunities are missed - For Marketing: Generate campaign briefs, segment audiences, create tailored content, and analyze performance to optimize outreach efforts - For Customer Success & Support: Provide 24/7 support, resolve customer issues autonomously, escalate complex cases to human agents with full context, and ensure consistent, brand-aligned responses 3. How PE portfolio companies benefit: - Efficiency: Automate repetitive tasks, enabling teams to focus on high-value activities - Scalability: Handle growing customer volumes without increasing headcount - Personalization: Deliver tailored experiences using real-time data insights - $ Cost Savings: Reduce operational costs by streamlining workflows 4. Business Impact for PE Portfolio Companies: - Accelerate GTM Execution: Faster lead conversion, better customer retention, and enhanced marketing ROI - Improve Metrics: Boost Customer NPS, CSAT, and efficiency - Enable Growth: Scale operations seamlessly while improving team productivity. 5. How PE Firms Must Think About Adoption - Define clear portfolio objectives to use AI agents (not just LLMs) - Ensure integration with existing CRM and data systems - Prioritize high-quality data and ongoing performance monitoring - Use AI agents to complement existing GTM teams, focusing them on automation ------------------ #pe #privateequity #investing #ai

14

I like this idea of "technical taste". It gets into where software engineering is as much creative art as it is science. Four great takeaways (IMHO) for software devs in this era in particular: 1. Aspiring engineers should cultivate a sense of curiosity, experiment with different tools and technologies, and embrace a mindset of continuous learning. 2. AI has the potential to streamline processes and enhance productivity for engineers, but it may also lead to disruptions in traditional software development workflows. 3. Developing technical taste and judgment is essential for making informed decisions about which technologies and approaches to pursue. 4. Collaboration and open-mindedness are key to leveraging the full potential of AI and staying ahead of technological advancements. I also really liked these two quotes from Sam Schillace: 1. "The right time to do something is when you have that feeling in the pit of your stomach that's like, 'oh, this is a great idea and it's going to suck to build because nothing's ready yet.'" 2. "Technical taste is like, 'how well have you consolidated that set of experiences and heuristics into judgment that you can apply accurately when you see new things?'" 3. "It may be the case that very small teams can do very large projects, or like we were talking about before, it may be the case we're just going to get really ambitious about what we try to do with the same size of teams, which is kind of where I would put my money." Great job on continuing to put out great episodes, Brett Berson and team First Round Capital! #ai #engineering #softwaredevelopment

5

We just concluded Embiggen X’s strategy for 2025. For almost two years, we’ve experienced conversations and priorities shift for large companies - more focus on innovating the core the business and less on non-core innovation initiatives. We’ve seen large global companies in the west close down their innovation labs, corporate venturing units, and let go of non-core innovation teams — or at the very least, decreased spending in venturing activities. This is also true for large corporates in Southeast Asia. Simultaneously, we’ve been getting into conversations with CEO’s asking more questions on AI & Digital Business Transformation. No, I’m not talking about building apps. We’re getting questions around: - How do we rewire the “guts” of our operations for us to be able to “scale profitably”? - How do we fix our “spaghetti & siloed” operations? - How do we make sure that we don’t just “buy more” off-the-shelf tech platforms that don’t talk to each other and complicating our already inefficient ops? - How do we increase our NPS score by transforming our customer experience? - Can you help us build a holistic digital business transformation strategy and help us execute too? - How and where can we use AI to become grow better? This is why Q4 of last year in the midst of business challenges, we decided to shift focus from Corporate Venture Building and spin off an AI & Digital Business Transformation company, Embiggen X (https://lnkd.in/geVM4BCF), from the Embiggen Group. Within less than a year, we’ve expanded our operations in Southeast Asia to Europe and 2024 as our highest revenue & profit generating year. We’ve served companies from Insurance, Supply Chain, Telco, Conglomerates, Construction, and Consumer Services to date. We help from Strategy to Execution. I’m beyond grateful to the operating team that made this new chapter of our journey possible. Embiggen X Managing Partner & COO, Xavier (X) Marzan; Partner & Head of Europe, Nicolas Souvlakis (calling in from Switzerland); Junior Partner & CAO Bien Herrera; Partner & CTO Robert Newgen; Chief Growth Advisor Gary de Ocampo, GICD; Advisory Board Member and Incoming Board Director Cliff Eala; Head of Artificial Intelligence Miguel Testa (calling in from Canada); as well as more partners, clients, and supporters. Big goals & challenges up ahead. Excited for whats to come. Onwards & upwards. 🚀

118

5 Comments

Instacart is a market leader when it comes to leveraging AI tools to boost productivity at work. I had an amazing conversation with JJ Zhuang, Instacart’s Chief Architect, who has shepherded over 50 AI-driven initiatives to market across the org. We explored what catapulted Instacart to the forefront of AI integration, supporting radically diverse use cases across various functional areas. One striking insight (echoed by Ethan Mollick and others elsewhere) is that you often don't need specialized tools for specialized work. Frontier models like GPT-4 can seamlessly adapt to a wide range of functions and use cases. They're not just for engineers; they're incredibly versatile tools used by marketing, comms, and legal teams. From generating code to proofreading and ideation, GPT-4 empowers every department to leverage AI for enhanced productivity and innovation. By integrating LLM’s like GPT-4 into their workflows, teams can streamline processes, automate repetitive tasks, and unlock bandwidth for new creative possibilities. How can you harness the power of AI in your own work to boost productivity? Share your thoughts below 👇 Want to hear more insights from JJ? Check out the full podcast episode by clicking the link in the comments.

8

2 Comments

AI is reshaping how enterprise software delivers—and prices—value. Companies like Zendesk, Intercom, and Forethought are shifting to outcome-based pricing, charging only when AI autonomously resolves issues. I've been investing in AI for over a decade. The shift to outcome-based pricing in enterprise software is a seismic change. As I told Jon Victor at The Information, software companies now face a critical decision: Does a company decide to change business models at the expense of losing revenue? It's a necessary risk. With cheap access to LLMs, businesses can build their own enterprise apps. Adapt or die. Why this pricing shift matters: → Vendor success ties directly to customer outcomes → Short-term revenue risks enable long-term market expansion → Industry faces widespread pressure to demonstrate measurable value Pitching AI-powered automation software as a clear cost-reduction tool could ultimately lead to greater revenue for vendors. This is because customers may view AI solutions as fundamentally different from traditional enterprise apps, whose business value is often harder to quantify directly. It's a huge market expansion opportunity. Vendors that nail outcome-based pricing could see massive growth as AI transforms enterprise operations. Those that don't? 😬🫣 How do you see this impacting sectors beyond customer support? Leave a comment. And if you're building in this space, send me a message.

35

3 Comments

During yesterday's LibreQoS APNIC webinar, I posed the question: won't more bandwidth solve these problems? As Herbert Wolverson said, yes, more bandwidth is good, but, still these problems remain if queueing is done incorrectly. Here's my take on why bandwidth alone is not as good as bandwidth + good queueing policies: Generally it was believed that 'data is important, so don't throw it away; hang on to it and send it later'. In practice, this has proven to be suboptimal as two issues may emerge: 1) latency increases for some flows due to heavy demand from other flows using the same bottleneck link, 2) even a single flow can have excessive latency due to aspects of typical TCP behavior (referred to bufferbloat) when the buffers grew large enough that the data being buffered was retransmitted anyway. It turns out that not all data is equally important. Active Queue Management is the art of deciding priorities, both in deciding what data to throw away, but also in allowing some later arriving data to be transmitted ahead of data in another connection that arrived before it. These problems have been studied, and good solutions have been found by using certain queueing policies in routers and switches, referred to as “fq_codel’ and “cake”. These track the different flows not by classifying the data, but by watching the behavior. Flows that send relatively little data (DNS lookups), or at a measured pace (video chat) have priority over flows that send a lot of data as quickly as possible (App and System updates, Video and ISO downloads).

10

5 Comments

ISP Strategic Leadership, Engineering, and Operations As an organization scales, there is often a need to separate strategic technology leadership under the CTO from internal execution under engineering and operations management. This allows the CTO to focus on outward-facing duties while enabling streamlined operational efficiency. However, it's crucial to recognize the differing motivations and needs of these distinct technical roles. On one hand, engineers thrive on tackling interesting, challenging projects that push their abilities and knowledge. They value working alongside capable peers, genuine recognition of technical accomplishments, and contributing to products/services that reach the market. In contrast, operations teams should strive for repeatable processes, continual improvement, and driving efficiency at scale. Their priorities revolve around standardizing workflows, minimizing disruptions, and incrementally optimizing existing systems and procedures to maintain consistent service delivery as the company grows. An effective organizational structure acknowledges this dichotomy, fostering an environment that fuels engineering innovation while providing operations with the stability and process-orientation they require. Striking this balance enables the company to stay agile and innovative through its engineering efforts, while simultaneously scaling operational execution in a reliable and repeatable manner. Competitive compensation remains an important motivator across both domains. By properly aligning structural elements, motivational factors, and leadership priorities, a rapidly growing ISP can position itself to attract and retain top talent across engineering and operations, driving sustainable growth and success in the dynamic technology landscape.

45

10 Comments

Insightful breakdown from my great Partner Omri Amirav-Drory on the simple factors that really separate the top 1% of investors from the rest, and how more operators should become investors! https://lnkd.in/gEiZdT2h

16

3 Comments

Founderville believes one of the most significant opportunities is emerging in legacy system applications. We look for manual tasks to leverage AI for efficient automation. Rather than chasing massive TAM, which will be addressed by larger VCs, we focus on smaller market domains that won't be chased as hard. Think of "boring" processes that require human intervention or manualized tasks. Those are opportunities. Keep your eyes open if you have deep domain expertise in a non-automated and fragmented industry. #entrepreneurs #venturecapital #startups #saas

17

7 Comments

In vertical SaaS, the very best companies landed with a product solving a specific, burning pain point that horizontal solutions solved badly and then became broad product suites over time (ServiceTitan, Veeva Systems, Doximity, etc). My partner Gordon Ritter calls this “layering the cake”. In vertical AI, the landing wedges are much more brittle. While the painpoints they solve are very real (and often unaddressable by SaaS alone), they’re also much more likely to commodotize in the near term as the tech advances. 👀 Therefore, the need to layer the cake in vertical AI is much more urgent than in vertical SaaS. 👀 The best vertical AI CEOs not only recognize the dynamic, but talk about it openly. Helps those around them keep up the speed of innovation and their eyes on the bigger prize.

83

13 Comments

There are two interesting and relatively new/emerging revenue models in hardware space. (1) Software Enabled Hardware Hardware is sold at lower but slowly improving gross margins (eg 20-40%), and software recurring revenues layered on top. Software sells at higher margins, enabling blended margins to appear in 50%+ category. These are applied in situations where hardware is not expected to be replaced or greatly improved upon in deployment for a significant number of years to come. Consider your car, or military equipment, or even infrastructure equipment. As always, there are often multiple business/revenue models utilized by different players in the same industry. (2) Hardware As A Service Hardware and software are bundled and sold with annually recurring revenues. Often multi-year contracts are signed to be able to amortize hardware over time, and enable blended gross margins over the length of the contract to be high (approaching 60-75%). This is more common in situations where vendor takes responsibility to upgrade hardware as needed along the way to provide maximum and highest functionality to the customer. Hardware inventory costs are, over time, passed on to suppliers or third party financing parties in lieu of few gross margin points. Consider robots as a service in factories (ala Formic), or AI-enabled equipment (eg Evolv, Lumafield etc). Hardware innovation will require deeper investigation, experimentation, and understanding of business/revenue models to fit customer needs as well as to maximize value for the innovating company. Strong and experienced management teams work hard to minimize sales friction, inventory costs etc, but also maximize gross margins, customer stickiness, and ability to upsell during the life of a contract.

339

37 Comments

Elevate your networking game with our #SiliconValley VC contact list. Access 200+ #VC firms and 1800+ professionals, along with 1500 emails to help you make valuable connections. Learn more: https://payhip.com/b/eUIyc #venturecapital #startups #funding #startupfunding #startupinvestors #privateequity #founders #entrepreneurs

1

💡 Why do we need so many evaluation tools for LLMs? As engineers, we build 'production-ready' LLM products using these metrics. But what happens next? How do we maintain control and ensure reliability? At Feedback Intelligence, we’ve crafted a cookbook to keep your LLMs reliable and aligned with user expectations. 🍲 📖 Give it a read and let’s chat!

16

Congrats to the team at Rockset on their acquisition by OpenAI! 🚀 We’re excited to see how their tech and expertise will boost OpenAI’s real-time capabilities. Unfortunately, this means the Rockset commercial product will be discontinued. If you're a Rockset customer looking for alternatives, we'd love to help you. Seamlessly migrate your Rockset data to Imply Polaris, a fully managed, cloud-native analytics database built from #ApacheDruid. 🧙🏼‍♂️ https://lnkd.in/ejQqPbw2 #Rockset #OpenAI #ChatGPT #AI #LLM #realtimeanalytics #streamingdata #Apache

5

2 Comments

What I learned from Jerry Yang & Larry Page TL;DR: Technology companies launching disruptive products should religiously focus on end-users above everything else, including partners. Solve end-user problems and everything else follows– adoption, revenue, and partners. Yahoo! & Jerry Yang (2006) In 2006, phone calls were expensive and based on time & distance pricing. Skype changed all that with VoIP, which bypassed telco networks and made super cheap calls using the Internet. They already amassed 34 million users and were growing at insane rates. At the same time, Yahoo Messenger had over +110 million users worldwide. We could leverage this userbase and create a VOIP product to challenge Skype. Yahoo had all the advantages on paper: the brand, the users, and the technology. So, with the leadership of Brad Garlinghouse, we built a competing product with a very disruptive pricing plan – 1 cent/min; Skype's pricing ranged from 2-3 cents/min. However, a week before the launch, Jerry Yang paused the project at a partner’s request. ATT-SBC was concerned our pricing would cannibalize its own voice business. Yang did not want to upset that relationship. Ultimately, Yahoo missed its opportunity, and Skype was off to dominate the VoIP market. Google & Larry Page (2008) Before the iPhone, companies had to negotiate with mobile carriers to get their apps on phones. Google was struggling to secure deals with Verizon for Google Maps and a new phone concept, Android. Around the same time, the FCC planned to auction 3G spectrum. Google pushed for open application and open device requirements on the carriers who won the auction. This infuriated the carriers. They argued that such conditions would decrease the spectrum's value below the $4.8 billion auction floor set by the US government. The FCC was leaning towards the carriers. The agency did not want to risk the auction not meeting the government's financial goals. Google's only option was to participate in the auction to ensure the minimum bid. Inside Google, the decision to bid was intensely debated. Android founder Andy Rubin argued the carriers would hate us and never partner with Android; Chris Sacca argued that bidding was the only way to guarantee Google's future in mobile. Page sided with Sacca. Page’s rationale: “. . . the carriers already hate us. If we don’t bid, they would hate us and not respect us.”  We bid, the openness provisions were triggered, and we secured a path for Google apps on 3G. Sixteen months later, Andy got his deal Verizon –  Verizon Droid! Page was willing to sacrifice mobile carrier deals to ensure Google’s products were universally accessible to its users. He played spectrum poker with $4.8 billion to achieve that result. (cont. below)

29

11 Comments

🪦 RPA is dead – right? Yesterday, Andreessen Horowitz posted an investment thesis about the end of Robotic Process Automation (RPA), and the subsequent rise of intelligent automation. Kimberly Tan made great points, and I think the thesis is pretty spot on. RPA as it exists today has fallen drastically short of what it promised to achieve. There’s a couple key problems: 1. Long and expensive (!!!) implementation process. If you want to implement RPA within your organization today, you have to go through a multimillion dollar implementation phase that can take months. Unless you’re a massive company, there’s no way you can make this work. 2. Brittle, hardcoded flows. RPAs today are heavily dependent on hardcoded rules. These automations break as soon as the underlying websites change, which kind of defeats the whole purpose of having an automation in the first place When you combine the two, you quickly reach a cost structure that makes a positive ROI impossible to achieve. As a result, the majority of repetitive, back-office workflows are still un-automated. LLM driven RPA will close this gap and deliver what the first wave of RPA companies promised 15 years ago. I don’t think RPA is ‘dead’, per se. But, I do think that new, vertical specific automation tools are going to force incumbents to decide between cannibalizing their business models or slowly fading into irrelevance. I can’t think of a more exciting space to be building in. —----- If anyone has anything to add or thoughts on these I'd love to hear them below!

20

7 Comments