Here’s a summary of some of the most important points covered in the discussion:

• Flock collaborates with law enforcement and local authorities to pinpoint crime problems and develop tailored solutions. The company’s technologies work to improve crime clearance rates, which are low in many cities—primarily because there’s a dearth of objective evidence. Eyewitness testimony can be subjective and is often inaccurate. Police are often understaffed as well.

• As an example, Langley cites a case in Tennessee involving an armed robbery of a vehicle. The driver of the vehicle was held at gunpoint and gave police a description of the assailant that later proved inaccurate. However, with Flock, the police were able to obtain a list of all pickup trucks in the area of the crime at the time when it was committed. The police were then able to obtain license plate information, narrow the list to a suspect, then obtain a search warrant.

• This example shows that Flock can not only help law enforcement solve more crimes, but also inject more objectivity into the crime solving process. Detectives can focus more on objective evidence instead of having to rely solely on subjective testimony.   

• There are many traditional solutions available for gathering this kind of evidence, but they are costly and decentralized. Flock aims to address these cost barriers by providing more affordable, effective solutions.

• Langley attributes Flock’s success partly to good timing and to leveraging smartphone technology—particularly camera technology. The dramatic reduction in cloud compute costs and ongoing innovation in AI have also contributed to Flock’s success in leveraging cloud technology for their imaging platform.

• Flock is also using AI to identify and track suspicious vehicles for crime prevention. For example, it utilizes 400 devices in San Francisco for real-time vehicle monitoring and law enforcement notifications. It captures vehicle fingerprint and attributes for accurate identification and tracking.

• By helping to improve crime clearance rates, Flock is helping reduce crime rates. This is because the severity of penalties for various crimes does not significantly affect crime rates as much as getting caught does.

• Flock creates a statewide hot list for tracking vehicles and helps in quickly locating missing children. The system notifies the nearest police department and relevant authorities when a hit is found, enabling the safe return of a missing child. The technology connects multiple police departments and has a significant impact in reuniting families and solving crimes.

• Balancing company decisions with societal values is important for Langley. Flock chooses not to build facial recognition despite its effectiveness. It respects societal decision-making, like the specific data retention policies applicable in the communities in which it works. Privacy is also important, and concerns can be addressed with reasonable tradeoffs. Flock provides a public transparency portal that helps police departments communicate exactly how they are using Flock technologies, what data is collected, how it is stored, and other details the public has a right to know.

• Flock’s success in selling to local government is notable, since it’s a market once considered underserved and non-venture-backable. However, investors are now recognizing this market’s potential.

• Langley emphasizes the importance of selecting the right team members to work with. He dislikes traditional job descriptions and values immediate measurable impact during the onboarding process. The company creates actionable 90-day plans for new employees during interviews to align them for success from the start.

• Effective management involves aligning and directing the energy of the team. Managers should not just hire smart people and let them run, but instead drive alignment within the team. Identifying and addressing resourcing issues leads to improvements, and uncomfortable changes can result in happy outcomes.

• Langley expressed his dislike for only receiving polished goods; he prefers to see raw progress and iterative work.

• Fostering meaningful in-person interactions with employees is a priority. Employees are expected to have at least two in-person touchpoints a year, one with their team and one with the whole company, and the company is intentional about the design of these touchpoints for maximum impact. The focus is on assessing whether employees are genuinely contributing substantively or if they are better at managing up and communicating.

• Langley describes himself as a “macro optimist but micro pessimist.” He believes in the success of Flock over the long term, but the executive team focuses more on challenges and problems. He believes that presenting only the optimistic perspective can undermine team confidence, because it may not reflect their reality. Employees are living in the messy day-to-day business of customer complaints and product delays—they’re in the weeds, so it is important for executives to acknowledge challenges. Optimism is valued, but it must be backed by a plan for success.

• Financial metrics are not a primary focus for employees at the company. The majority of employees are new to working at a venture-backed company and find the company’s worth irrelevant. They value Flock’s mission and impact instead.

• Langley highlights the importance of gaining life experience before starting a company. Starting a company at a young age can lead to poor decision-making. Learning from experienced people in early days of a company can make you a better CEO or founder.

• After early success in his career, he experienced a period of uncertainty and reflection. One of his realizations was the importance of working with people you like.

• Impact also matters greatly to Langley as an entrepreneur. He feels a strong calling to Flock’s mission to improve public safety and values his team, co-founders, customers—and the unsolved challenges that Flock is addressing. He also believes successful fundraising is more fulfilling when the company makes a real impact.

• In fundraising, the investor class changes at certain points in time, and so the investor perspective on the business changes. These inflection points occur at the Series B and pre-IPO stages. Series B funding requires more than just a compelling story. Metrics become crucial for Series B investments. Flock faced fundraising challenges at certain points in its history—at the worst point, it was less than three months away from running out of capital.

• Langley faced skepticism and challenges in fundraising at certain points, but successfully proved doubters wrong by tripling business and addressing feedback from investors.

• The venture community demands clear storytelling and proof of success. Investors need benchmarks for future goals, making it challenging for startups in uncharted territory and new verticals. Flock overcame this challenge by setting ambitious goals—for example, scaling from $16 million to $50 million in annual revenue—and then posting a few quarters demonstrating that trajectory. This led to an inflection point where the investor community started to believe in the company and its expertise.

The post Garrett Langley Shares How His $4B Startup Is Solving Crime with AI appeared first on John Avirett.

Here’s a summary of the conversation:

• Guo has found that, in recruiting talent to her firm, evaluating early-stage venture capitalists is difficult due to lack of track records. The Important traits she seeks are core technology understanding, judgment, and business insight, with judgment being the most difficult to assess among early-career talent.

• In her venture career,, her earlier work with the companies Awake (network analysis security) and Figma (a design tool) were instructive. These experiences taught her about company-building and gave her confidence in identifying long-term potential in new markets and teams.

• Her decision to start Conviction was influenced by her passion for AI and its transformative potential. Founding the company was a process; the key steps included leaving Greylock responsibly, building relationships with LPs, and crafting a focused strategy. The first investment was Harvey, an AI-driven legal application company.

• Whether in the AI space or elsewhere, Guo thinks the core framework for evaluating software companies is the same: distribution, quality of the people, and size of opportunity matter. Contrary to the narrative that there’s no value in startups working at the application layer of the stack (as opposed to those interested in developing foundation models), Guo has long thought that this “last mile” technology was important. She doesn’t advise building tech that the foundation models are going to replicate; that’s wasted effort.  

• Building useful AI applications requires leveraging domain-specific knowledge (e.g., law, in the case of Harvey, her first investment at Conviction) and avoiding commoditization.

• She’s betting that large incumbents won’t be able to win every market, particularly those markets that are secondary to their primary revenue drivers. Entrepreneurs should therefore focus more on “avoiding the path of incumbent strengths.” The underlying technology might be general, but it’s the applications built on top that matter, because they bridge the gap from the underlying model to the end user. This is where there’s space for entrepreneurs to excel.  

• She prefers teams with deep research expertise. She emphasizes minimum viable quality (MVQ) for AI applications to meet a baseline of practical and user-expectation standards. Entrepreneurs need to validate their AI’s quality by engaging directly with customers, and understanding whether the AI’s output is “good enough” is essential for achieving product-market fit. MVQ is a moving target, as new use cases emerge, and quality expectations evolve. It also differs across different domains.

• Some AI applications have already crossed the “uncanny valley” and surpass human capabilities in very specific contexts; writing, however, isn’t there yet. The cost of managing errors and improving AI outputs from 80% quality to acceptable levels must be minimized for broader adoption. Verification and ranking systems that evaluate multiple outputs are critical for improving end-user experiences and building trust.

• Building applications that are quick and easy to replicate is not a sustainable strategy unless there is a defensible distribution mechanism. Initial traction with simple tools may be a good starting point but cannot be relied upon for long-term enterprise value. Many entrepreneurs approach AI applications with this kind of short-term thinking, failing to plan for competition and scalability. Without deeper engagement, these startup teams risk being overtaken.

• For Guo, challenges in the AI space right now include improving multistep reasoning and addressing hallucination in models. AI’s adoption in conservative sectors (e.g., legal, healthcare, government) is progressing slowly, but holds potential for significant impact. Guo is hopeful that the next generation of models will be more able to tell when their output is correct—if the model knows whether it has a good answer or not, it’s much more useful.

• High costs in training and deploying large models remain a challenge as well; efficiency in model development and deployment is critical for future scalability.

• Large-scale AI training workloads expose flaws in alternative chips that aren’t apparent during smaller-scale testing. Companies like Google succeed in chip development because they can test at scale within their own workloads. Entrepreneurs don’t have this luxury. Testing alternative chips requires significant investment and deployment at scale, making it hard for new entrants to compete.

• Nevertheless, Guo foresees a more competitive AI ecosystem in the future, with multiple foundational model providers and hardware providers (e.g., alternatives to NVIDIA GPUs). She cites development approaches like that of Mistral. Its open-source and efficiency-driven approach is gaining traction – efficiency in model training and inference is increasingly important due to constraints like data center size, power limits, and costs.

• Domains like material science and enterprise software configuration hold untapped potential. Bridging gaps between technical innovation and domain expertise is critical.

• In terms of ethical and safety concerns, near-term risks include fraud, misinformation, and misuse of AI technologies. Long-term risks involve runaway systems and biosecurity concerns. AI adoption could lead to significant productivity gains, but also societal challenges like job displacement and regulatory issues. Guo, however, is ultimately an optimist about the transformative potential of the technology.

The post Venture Capitalist Sarah Guo Shares Insights on the AI Landscape appeared first on John Avirett.

Here are highlights from the conversation:

• Creatio’s platform allows clients to implement workflow automation and go live faster than with other vendors, and it offers a unified solution for automating front and middle office functions.

• Originally, the company was focused on business process management (BPM); it rebranded from BPM Online to Creatio, but that BPM engine remains the “crown jewel” product around which Creatio was built.

• Creatio integrates AI into two functions within its own operations: automating simple tasks (particularly those related to sales, call center operations, and marketing) and building applications and automating workflows without human intervention. The focus is on leveraging AI to enhance the platform-building process.

• AI is an accelerant for Creatio’s no-code platform. While no-code platforms already bridge the gap between demand for workflow applications and developer capacity, AI takes this further by providing new ways to create applications and workflows, making development even more accessible and scalable.

• The company recently raised $200M in a second equity round, bringing its valuation to $1.2B. The decision to raise capital was driven by two factors: the vast potential of AI, especially the possibility of embedding advanced AI features like autopilot into the Creatio platform; and the need to support global growth.

• The company was bootstrapped for many years, and she considered carefully the risks and responsibilities in raising outside capital, particularly the need to commit to investors’ ambitious targets.

• Katherine waited to raise capital until Creatio had achieved strong product-market fit and a better understanding of how to scale efficiently. She prioritized getting the product and business operations right. Waiting until Creatio had this proven track record of success allowed the team to approach investors with confidence in the company’s ability to meet growth expectations.

• She also believes the company’s strong philosophy of delivery and commitment—focusing on fulfilling every promise they make—also helped build trust with investors and contributed to the successful capital raise.

• The company’s core values include passion, genuine care, striving for excellence, and growing every day. They return to these values often—in meetings, one-on-ones, and during hiring.

• Leaders are Creatio are guided by five principles:
  • Results: Delivering results is paramount
  • Zero Ego: Operating with humility and being focused on the future, no matter what happened in the past.
  • Trust, vulnerability, and accountability: Building trust while keeping teams accountable
  • Stepping up the pace: Leaders set the tone and pace for the organization, creating energy and intensity that drive people forward.
  • Raising the bar: Constantly improving standards.

• Hiring at Creatio follows a structured process based on values alignment, hard skills, and soft skills. She trusts her gut, but also believes that hiring is 70% science, 30% art. It’s a competency-based approach that allows the company to maintain its culture across a geographically dispersed team.

• Katherine’s leadership style is situational—she adjusts her approach based on the context, but prioritizes strategic thinking and delegation while being ready to wade into the details for critical projects or when things go wrong. Hiring the right people is key to her success as a leader.

• Over 10+ years of leading Creatio, she’s gone from what she describes as a small business entrepreneur, to a mid-sized organizational leader, to the CEO of a large enterprise. She prefers the excitement of her job right now, as she works to scale the company to billions in revenue, over her concerns during the company’s early days, when they were trying to find product-market fit. She found this stage personally challenging due to her preferred structured approach.

• A balance of trust and accountability—building strong relationships with her team while also demanding results—defines the Creatio leadership dynamics.

• For her, the biggest challenge as a CEO is the need for constant growth and adaptation; she believes that as a CEO, you must evolve every year to meet the changing demands of the business and the market, and be willing to change processes and operations to stay relevant. You must make change a habit.

• She’s passionate about the transformative impact of AI on software development—she believes we will be the last generation to build apps without AI, and that future developers will rely more on natural language, instead of programming languages, to code. 

• Over the next five, 10, and 15 years, Katherine’s goal for Creatio is to expand into more geographies (the company has clients and partners in 100 countries and employees in 25) as it continues to serve large organizations with complex processes, particularly banking and credit unions. She sees Creatio’s workflow automation and no-code AI tech as having significant value in these industries.

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