Deepa Buddhavarapu @DeepaBuddha
Passionate Marketing and Growth Leader Always Questing for Insights & Inspiration. Talks about #marketing #growth #mindsets #kindness #life about.me/deepa.buddhava… San Francisco Bay Area Joined July 2011-
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One of the great thinkers said, “Give the model enough context to understand the problem, but not so much context that you prescribe the solution space.” Love it. A lot of what I learned about bias through experimentation is coming in handy here. Here's how I see it 👇
Funny, when I was mulling over local maxima and global maxima, the same quote playing on my mind appeared in my podcast reels: youtube.com/watch?v=tq6vdD… Joy and fun for the win!!
What startups taught me. σ → 0. You can optimize toward zero variance. Eventually. Just know what you're optimizing out. Exploration. Serendipity. Outliers. Unexpected paths. Innovation lives in the variance. Variance shouldn't be justified during exploration stages.
Someone who knows you will know the choices to reject on your behalf and to say, "There isn't a good match at this time." That simply isn't there in the LLM architecture. They still operate on the legacy jobs-to-be-done: being helpful is survival and becoming part of the tribe.
The future of currency might be LLM tokens. 😂😭
Somewhere, a thoughtful marketer is paying attention to details. Love it! 🥰 Never cared about the biological age feature in Function, since the data-science skeptic in me questioned its inputs. But the email, though, is a work of genius. Their product team needs to catch up. ;)
Not sure who else feels this way. I see no conversations. My prompt below. I think of this a lot as I think about automation & how to solve for it. When the response range is so narrow, you can't really expect innovation or unique solutions. Then, how effective is your marketing?
Retention is the driver of authenticity, which often remains as a brand value but never gets activated under the performance pressure of growth. AI slop is just a downstream problem. Human slop is a choice.
There is a lot of talk about AI slop and how to avoid it. I think the real conversation should be about human slop and how to avoid it. There is a delicate balance between growth and retention. You see the degradation the moment growth becomes the sole focus. 👇
1. How are you measuring success- time, money, throughput? 2. How is that being redeployed? 3. Can we account for a probability range and settle on an average based on bets we are making this year?
What if the right question to ask is: "How are you deploying the incremental gains from AI usage?" And break it down further into...👇
Driving business outcomes ultimately boils down to asking the right questions. Saw a conversation thread in the CMO circles about a survey question: "How are you budgeting for tokens?" 👇
Yes
Elon Musk thinks the entire education system is built on a broken assumption. That every student should learn the same thing. At the same speed. In the same order. At the same time. Musk: “Everyone goes through from like 5th grade to 6th grade to 7th grade like it’s an assembly
How marketing ideas are born (good or bad) 😂
POV comprises the quality of thought & the quality of writing. What excites me is ALSO being able to bring in a measurement approach that removes artificial constraints that creative teams are sometimes forced to operate under, owing to false precision in measurement models.
Your best ideas won't come from a dashboard. It comes from the willingness to be wrong and psychological safety to take big, bold risks despite what the data says. That might just be what saves your "brand" in the AI world.
But the original question-- what should we be doing? --that's a judgment question, not a data question. It requires pattern recognition across domains, intuition built on experience, an understanding of human desire and culture, and a willingness to make a call under uncertainty.
Most people have no clue what role data plays in business. Data is there to guide business decisions. The strategic bets, innovations, and big ideas don't come from data. In fact, data sometimes adds psychological constraints.
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