Ned Lowe’s Post

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Why do so few Generative AI Proof of Concepts make it to production? It's causing a bit of a "it's all hype" backlash. I spent some time thinking about this, and now present the AI Adoption Pyramid. It is rooted in the understanding that we need to align on 𝐏𝐨𝐥𝐢𝐜𝐲 & 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 first, without which any project will struggle to survive in a complex organisation. One policy is agreed, the 𝐎𝐫𝐠𝐚𝐧𝐢𝐬𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 needs to be determined. What tools & models are allowed for use that follow our policies. Who are the team members responsible for evangelising the advantages of safe AI? Only when those two layers of the pyramid are in place can we start choosing 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬. Choosing the right projects isn't as straightforward as we might think either! Read the article for more, including a framework for choosing projects that are likely to succeed in a world of possible AI hallucinations. Let me know your thoughts about other aspects to this that I've missed in the comments! https://lnkd.in/g7zazPZd

I attended the Snowflake World Tour Singapore yesterday, and had an interesting epiphany. One of my favourite articles is Shawn Wang’s “Eating the Cloud from Outside In” (https://lnkd.in/gyq-CW6E), which talks about how Cloudflare ‘surrounded’ the opposition (AWS et al) through incremental, complementary offerings (Cloudflare Workers, R2 object storage, etc) and then won the customer. It is clear that Snowflake is trying the same strategy - it’s simply not a database provider any more, it’s a cloud platform. I like to picture data having mass, and in turn gravity - the larger the dataset, the more it pulls other data to it, along with the applications to process it. However, we often try to push data to applications, rather than allow the applications to run on the (static) data. This is what leads to fragmentation, data lineage issues, and subtle differences in representation. So how do we get the applications to connect to the data? Snowflake Container Services allow you to run your containers 𝐢𝐧𝐬𝐢𝐝𝐞 Snowflake. That’s a pure compute play, and a direct competitor to AWS or Azure hosted Kubernetes. Snowpark allows you to run Spark jobs (SnowPARK, Spark, geddit?). Snowflake Snowsight is basically hosted Jupyter with Streamlit components. Again, running directly on your data - no infrastructure needed. If my guess is correct - and the economics work out - then they’re probably not that far away from having an “all-in” customer; a company that hosts all their application infrastructure within the Snowflake environment. Needs an API Gateway and probably a better Cloud Function implementation (Snowpark doesn’t count) - but that’s within grasp. If Cloudflare is aiming to be the fourth major cloud provider, is Snowflake going to be the fifth?

I'm either deep in thought here, or trying to harness my inner Professor X to spread the Fractional CTO message to the world! 🧠✨🌎 One of the challenges organisations at all stages encounter is bringing in experienced technical leaders who can navigate complicated situations, but also understand what "Think Like A Startup" actually means. Because they've... been in a startup. Getting access can be difficult, but the Fractional model allows you to bring in talent you wouldn't normally have access to, or perhaps don't need full time. Huge impact can be delivered in a fraction of the work week by injecting super-concentrated experience, at a commensurate fraction of the cost. If you have problems where you think an experienced CTO, CISO or CDO would be able to help, reach out! This LinkedIn post was beamed directly into your mind. You didn't read it.

Watch these videos! The MISSION+ lunch and learn is one of the highlights of my week 🥰 And Happy National Day to all my friends in Singapore 🇸🇬 I celebrate as a PR this year - I have now become a PR twice, that must be a record! 🚀