Next in Tech

The RSAC Conference, a major cybersecurity gathering in the spring, is coming up and the impacts of agents will be on full display. Scott Crawford, Brenon Daly, and Dan Kennedy join host Eric Hanselman to explore their expectations and look at what's been taking place in both the marketplace, investments and M&A activity. Agents are automating tasks, not jobs, and there are a great set of use cases, but they're not a panacea. There will be disruption, but it will be in specific areas, rather than a universal replacement of existing tooling.
Are we industrializing the automated creation of software? Will agents really replace SaaS applications? We're clearly in the early days, but these questions are causing massive market shifts. A better question is how agentic interactions will change how we interact with the applications that drive businesses today.
Join the team at RSAC and get all the details we didn't have time to cover. The annual 451 Research breakfast will be on, as always, so you can meet the team in person. 
 
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Credits:
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Guests: Scott Crawford, Brenon Daly, Daniel Kennedy
Producer/Editor: Feranmi Adeoshun
Published With Assistance From: Sophie Carr, Kyra Smith

Wildfires have grabbed the headlines recently and AI is being put to work to assess risk aid strategic planning in reducing events. The next in our CEO series brings Bryan Spear, the CEO of Technosylva, in to talk with host Eric Hanselman about not only wildfire, but also the ways in which AI can address flood and extreme weather risks, as well. There are interesting commonalities in the datasets that are used and AI has helped them to pivot into new areas as they dig deeper into what, in many cases, was data they already had.
Their computational requirements have driven them to invest in a dedicated compute environment. They're running analyses on scale where they can keep utilization high enough that the costs make sense. It's a calculus that many enterprises are making as they trade off the convenience of cloud with the predictable costs of owning one's own infrastructure. 
 
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Credits:
Host/Author: Eric Hanselman
Guest: Bryan Spear
Producer/Editor: Feranmi Adeoshun
Published With Assistance From: Sophie Carr, Kyra Smith

We continue our CEO series with Douwe Kiela, the CEO of Contextual AI, who is addressing the challenges of building effective agentic applications. The shift to agentic amplifies the need for enterprises to improve their data management capabilities and infrastructure scaling. The best models won't perform well, if there isn't well built context to support them. Much like people, if there's not enough of the right information, decision making is going to suffer. There's an evolution from the prompt engineering needed to generate better results from LLM's, to the context engineering that crafts the right data to feed agents.
This is an area where agents can also help to tackle the data quality problem that many enterprises face. Standing the old computing paradigm on its head, effective agentic applications ought to take garbage in and put information out. Well built agentic architectures can understand data characteristics and not only evaluate its quality, but also classify it and apply the appropriate security controls to its use. The scope and scale of agentic potential demands much greater thought to achieve its full value. We need only look to the recent Open Claw project to see both the up and downsides.
 
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Credits:
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Producer/Editor: Feranmi Adeoshun
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The next in our series of discussions with CEO's of companies that are putting AI to work has Fransico Martin-Rayo of Helios discussing the agricultural supply chain with host Eric Hanselman. Helios is leveraging AI to generate insights in the complex dynamics of food production and sourcing at a dramatically finer level of granularity. AI not only enables more complex analysis but also allows customized delivery of the results. Shifting interfaces from legacy dashboards and static reports to queryable constructs lets users explore the analyses in ways that better fit their needs. AI can deliver custom insights at scale in ways that weren't possible with traditional application interfaces.
One of the principal shifts accelerating AI, is the availability of better data. Helios integrates massive weather data sets with market history and global events to generate forecasts. While the volumes of data are growing it's seen significant reductions in the cost per token in their infrastructure. Improving efficiency can expand the depth of analysis, as well as the frequency of forecast updates.
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We're kicking off a new series of discussions with CEO's of companies that are putting AI to work to tackle complex problems. Sean Kelly of Amperon joins host Eric Hanselman to dig into how they're using AI for energy grid forecasting. The combination of weather, changes in generation capacity with renewables, and now increased data center demand is making forecasting a critical requirement for grid stability, as well as energy trading. While this might have been a problem that could be tackled with spreadsheets back in the day, scope and scale of the problem has grown to a size that is outstripping traditional methods. AI-based approaches can scale, but there are challenges with managing the size of the datasets being used and the computational costs that some models demand.
Effective integration of AI into complex problem solving demands not only a deep understanding of the problem space, but also innovation in sourcing data and scaling its applications. The payoff can be much faster results with greater perspective depth. But that requires investment in automation and careful engineering to get there. 
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