QuantSpark AI

Improving decision-making through analytics and data science

[Vipul Ved Prakash @vipulved]

It has been clear to many of us, and now it’s becoming clear more tangibly, that AI models will commoditize to various degrees. This is of course a difficult business reality if your core business depends on exclusivity on intelligence.

But commodity markets are not communism. They are the largest markets on earth. Oil, grain, steel, electricity, memory: trillions clear through them every year, priced by competition among thousands of suppliers. In economic terms, communism is one provider and no price. A commodity market is the precise inverse. The world that actually resembles central planning is the one Dean argues for: a set of protected incumbents, access gated by the state, agencies instructed to manufacture FUD until every regulated buyer, and transitively every tool maker upstream, backs away from cheaper competitors.

Open weights don't deter capex. They move it. When the model layer commoditizes, spend shifts to inference, data, tooling, and applications, and builds far broader industrial infrastructure rather than concentrating capital in a handful of companies. Most of our digital infrastructure today, hyperscalers included, runs on open source. The businesses built atop it keep excellent margins and compound at extraordinary rates. Open-weights intelligence will likely rank among the most important economic accelerations in history. It won't be kind to every early incumbent, Linux wasn't kind to Sun Microsystems, but it will be very good for almost everyone else. I suspect OpenAI and Anthropic, given their positions, excellent products, resources and talent density, will be just fine. They will simply hold a little less pricing power.

The security theater around Mythos continues to do damage. Of course, there is no evidence for the hysterical claims. The evidence is in fact so thin that proponents of AI's existential risks now openly recommend FUD as the strategy. That should be telling.

[Andrew Ng @AndrewYNg]

“Loop engineering” is a hot buzzphrase after mentions of it by Boris Cherny (Claude Code’s creator) and Peter Steinberger (OpenClaw's creator) went viral on social media. Loops are now a key part of how we get AI agents to iterate at length to build software. In this letter, I’d like to share my 3 key loops, shown in the image below, for building 0-to-1 products. These loops guide not just how I build software, but also how I decide what software to build.

Agentic coding loop: Given a product specification and optionally a set of evals (that is, a dataset against which to measure performance), we can have an AI agent write code, test its work, and keep iterating until the code is bug-free and meets its specification. This idea of closing the loop took off around the end of last year, and it has been a game changer in enabling coding agents to work longer productively without human intervention. For example, over the weekend, I was building an app for my daughter to practice typing, and my coding agent could easily work for around an hour, using a web browser to check what it had built multiple times before getting back to me, without needing my intervention.

The engineering loop executes quickly. Every few minutes, the coding agent might build and test a new version of the software. I hear frequently from developers who are finding new ways to engineer more effective engineering loops. This is an active area of invention!

Developer feedback loop: In this loop, a developer examines the current product and steers the coding agent to improve it. Last year, a lot of developers (including me) were acting as the QA (quality assurance) function for our coding agents, manually finding bugs and then asking the agent to fix them. But with coding agents much more able to test their own code, the amount of time we need to spend on this function has decreased significantly. This allows us to make higher-level product decisions, such as what key features to offer, where the UI needs improvement, and so on.

The developer-feedback loop operates over time intervals between tens of minutes and hours — that's how frequently a developer might review a product and give feedback. In the case of the typing app, I changed my mind a few times about the visual design, what cat costumes she can unlock as she learns (she loves cats), and the user flow for a grown-up to log in and steer the child's learning experience.

When a developer has a clear vision for what to build, it is still a lot of work to translate that vision into a specification for a coding agent to implement. Further, after the developer has seen an implementation, they might update (or perhaps clarify) the spec to steer it toward what they want. If you find that the system repeatedly runs into certain problems, building a set of evals for the agent becomes useful.

AI-native teams are increasingly using AI to help shape product direction, for example, automating the gathering and analysis of usage data, summarizing written and verbal customer feedback, or carrying out competitive analysis. However, for pretty much all the products I’m involved in, I see humans as having a significant context advantage over current AI systems — we know a lot more than the AI system about the users and the context the product has to operate in — and thus humans play a critical role. Many people describe this human contribution as “taste,” but I prefer to think of it as humans having a context advantage, since that gives us a clearer path to helping AI systems get better. This also speaks to why this step can’t be automated: So long as the human knows something the AI does not, human-in-the-loop is needed to to inject that knowledge into the system.

External feedback loop: This includes a wide range of tactics like asking a few friends for feedback, launching to alpha testers, or putting the code into production with A/B testing. These tactics are usually slow, rarely taking less than hours and sometimes taking days or even weeks. This data informs the developer vision, which in turn continues to drive the detailed product spec, which in turn drives the coding agent.

With coding agents speeding up software development, more engineers are starting to play a partial product management role. For many engineers who are growing into this role, the hardest part is shaping the product vision and striking a balance between building (bridging the gap between vision and spec) and getting user feedback to evolve the vision. It is important to do both!

I will write more about how to do this in future posts, but for now, I find it encouraging that engineers are playing an expanded role (just as product managers and designers now do more engineering).

[Original text: The Batch]

[Shreyas Doshi @shreyas]

Worth saving this article as an example of excellent writing.

[Andrew Curran @AndrewCurran_]

Qwen is launching a 2.4T model, and they are making it open-weight. There are rumors the next Opus - arriving soon - is extremely capable to the point it surpasses Fable 5. It will need to be, anything below current Opus capability is going to have an increasingly tough market.

[David Sacks @DavidSacks]

Narrative violation: A new study of 21,559 firms in the U.S. finds that “companies that adopt AI tend to grow faster following adoption”.

“Firms making the largest AI investments grow employment by roughly 10% following adoption, while low-intensity adopters see no statistically significant change.”

“Entry-level headcount rises 12% for high-intensity adopters.”

“Gains emerge gradually and are broad across roles, including engineering, sales, administration, and customer service.”

“The results counter predictions that AI adoption will lead to broad job loss.”

The study is based on observed AI spending from Ramp card and bill pay data linked to Revelio Labs workforce records.

[QuantSpark AI @quantspark]

Deploying WebSockets to gamify and automate a resource-intensive process https://t.co/JUvx07CKGU

[QuantSpark AI @quantspark]

The economics of churn reduction: Why analytics for SaaS is so vital for valuation https://t.co/T8DK3hZ86T

[QuantSpark AI @quantspark]

Predictive churn algorithm helps plastics manufacturer prioritise CRM engagement https://t.co/O8VgXK3bkV

[QuantSpark AI @quantspark]

Machine learning improves sales forecast by 30% for orthopaedics supplier https://t.co/5talnYXnj3

[Kaye Wiggins @kayewiggins]

Here’s a deep-dive from a year ago, when this was just kicking off

https://t.co/Wkp1Ckdnw7

[QuantSpark AI @quantspark]

There's an increasing trend of private equity groups buying companies from themselves in "continuation funds". This is partly a result of fierce competition for new acquisitions and the reluctance to sell successful assets prematurely.

#privateequity

https://t.co/x1RLNHDIVj

[QuantSpark AI @quantspark]

QuantSpark’s Monthly Update - December 2021 https://t.co/l5OGznhNvc - Summary of QuantSpark's recent Analytics projects

[CTEC @CTECMIIS]

The CTEC team is proud to announce that we're partnering with the amazing @techvsterrorism team to address the networked nature of online accelerationist activity.

Networked approaches to understanding accelerationism are crucial.

Expect a lot in 2022!

[QuantSpark AI @quantspark]

At today’s @RealDealsEU conference on private equity value creation we were particularly inspired by John Gilligan’s no-nonsense approach to impact investment at @BigIssueInvest - in particular the “elephant test” for understanding impact of the S in #ESG

[QuantSpark AI @quantspark]

We’re excited to be at today’s value creation conference run by @RealDealsEU - now listening to @ecipartners @ForesightGroup @mobeusequity and @Investcorp discussing the lay of the land in #privateequity

[Women in Technology @womenintech]

What an amazing chart of #WomenInSTEM careers! Feeling inspired 🌟🌟

[BluWave Partners @BluWavePartners]

The #CFO position is a vital leadership position in companies. While you're searching for the perfect permanent fit, we have the PE-grade interim CFOs that you need for a stop-gap solution.

What you should know about candidates to ensure the right fit: https://t.co/vO4g1jN9B9

[QuantSpark AI @quantspark]

In this week's analytics insight article we argue that the shrewd use of analytics and data engineering is key to ESG investment. For more detail see below
https://t.co/bQ8Spq3vHp

[ForbesWomen @ForbesWomen]

The 17th annual @Forbes list of the World's 100 Most Powerful Women is out now!

From fighting the pandemic to reengineering American politics, these influential women are making history.

https://t.co/dRsQc4L4kg #PowerWomen

[CatalystInc @CatalystInc]

Today's #BlackHistoryMonth hero is Sojourner Truth, a preacher, abolitionist, and women's rights activist, who spoke out against the injustices affecting the Black community. Learn her incredible story. https://t.co/TLPbr7J2x1