Pushpal Maheshwari

devx AI labs

[Pushpal Maheshwari @pushpal_95]

What we do at devx - explained better than we can to be fair.

[vas @vasuman]

https://t.co/oxa2EB0xtY

[Pushpal Maheshwari @pushpal_95]

@krishramineni - I have fireflies join all my calls. I have deleted my email account and changed my domain. There's no way to login via email - it's so bad - even when I deny it to enter, it keeps asking for permissions again. Almost evil.

[Pushpal Maheshwari @pushpal_95]

All AI tools - Claude, Gemini, ChatGPT - they have this unique way of sharing a PoV through some framework. The framework creates a pseudo impression that the solution/proposed strategy is thought through - we then start using that as the base of our thinking. Big mistake.

[Pushpal Maheshwari @pushpal_95]

Either @claudeai is now stupid or I'm now smart.

[Pushpal Maheshwari @pushpal_95]

Fantastic post! We’re doing something similar.

@levie - can we see a detailed case study somewhere?

[Aaron Levie @levie]

The past couple months we may be witnessing what the Applied AI layer will look like at scale. Despite some of the initial critique that this would just be a thin layer on the LLM, it’s turning out that actually driving agentic workflows in an enterprise is far more complex. And anywhere there’s complexity you generally gain a moat and value over time.

Here are a few of the components that appear to make up the playbook based on the examples we’re collectively seeing in coding, legal, healthcare, customer support, financial services and other fields:

* Build the features that bridge the gap between the intelligence and the workflow. Some workflows can be automated by simply going to a general purpose interface, but others need tuned interfaces and features tied to the work they’re augmenting or automating. They need features that are specific to capturing the kind of data that’s needed as context for the agent. And they need a variety of bespoke tools for the agent to use, and unique interfaces for the human-in-the-loop UX. Going far deeper than just presenting the output tokens is clearly critical, and the more depth there is here definitionally the more sustaining value.

* Act as the model router balancing frontier intelligence with cheaper models. A natural advantage that any model neutral platform has is that it can naturally (in a business model-aligned way) leverage whatever level of intelligence is necessary for the workflows they’re automating to get done. There are plenty of scenarios where you need GPT-5.5 or Fable level capability, and also lots of workloads where a more efficient closed or open weights do the trick. Only the companies that have deep evals on specific tasks across all models, and the ability business model wise to leverage them, are in a great position.

* Drive the actual implementation and change management via FDE or equivalent. A big reason the applied layer works at scale is that most enterprises need some degree of help and support with change management in implementing agents for their workflows. Data has to be cleaned up and moved to modern systems, processes have to be re-engineered and documented, workflows have to be evaled, SLAs have to get achieved, and so on. All of this is going to be unique for every type of process that gets implemented, which means the companies that have expertise in a given domain and come with all the relevant best practices will be in a strong position.

* Implement domain specific GTM that creates expertise in that field. Beyond FDEs the companies that can build sales and GTM motions aligned to their domains also have a natural advantage. Most IT and line of business leaders have too many things to do in any given day; so if you’re not on their agenda, likely someone else is. Depending on the industry, there are entirely different sets of language you use, ways of working through security and compliance, regulatory controls you have to support, industry events that companies convene at, different system integrator and consulting partners you need to work with, and so on. The more generalized this gets the less you can speak the customers language, which is where the applied layer has a leg up.

A final note. There remains a view that a lot of this is all mitigated by model intelligence alone, and the bitter lesson solves all of this in the limit. That’s possibly true, but enterprises need help changing *today*. And many aspects of how to bring intelligence to real world work don’t only depend on the axis of the pure capability of the model, so most of what you’re doing now to win ends up being important no matter how good the models get.

[Pushpal Maheshwari @pushpal_95]

Sovereign AI and humans?

[Ramesh Srivats @rameshsrivats]

US policy is becoming clear. They don’t want natural intelligence coming in or artificial intelligence going out.

[ericosiu @ericosiu]

AI service firms are commanding 30x multiples right now. Yes, thirty.

That's why a16z, Sequoia, and YC are chasing services, not SaaS.

Most agencies will see this and reach for the wrong move. They'll keep selling hours, bolt on AI, and cut headcount to pad the margin.

But that's playing the small game.

Here's why:

00:00 Why Services Beat SaaS
01:13 The $1 Software vs $6 Services Opportunity
02:52 Why Managed Growth Loops Matter
04:49 Agents, Loops, and Human Judgment
06:43 How Single Brain Powers AI Service Businesses
07:22 The Services-as-Software Manifesto
08:41 The New AI-Native Org Chart
10:13 Building Outcome-Based Offers
11:13 Final Thoughts

[Pushpal Maheshwari @pushpal_95]

We’re just getting started🚀

[Yash Thakker @yzthakker]

It's been a crazy few months building at @devxAIlabs .

Key highlights:

Doubling down on our key learning: the only way to drive real business impact with AI (beyond personal productivity) is to rebuild your experience layer, applications and processes from first principles. That's core of what we do: First Principles Execution Consulting

Building an AI-native org: we have a clear vision for what an AI-native organization built from the ground up should look like. As we solve our partners' problems, we're equally committed to staying AI-native at our own core. Down the line, that's what we want to be known for - the model of one of the most efficient way to structure and run your organization.

For those who know me, I've passionately argued for years that enterprises should invest in strong product management to reimagine their internal and external experiences. In an AI-native world, this matters even more almost the entire application layer is going to get rebuilt over the next few years. Our bet on outcome management(forward deployed and otherwise) as a new, core function of the org is already driving real results for our customers.

Building the team: we celebrated 3 years a couple of weeks back, and getting our global teams together to step back and reflect on the culture we're building was a highlight. Deep gratitude as we keep adding top-percentile talent with taste, commercial judgement and outcome obsession.

And we're just getting started and currently talent-crunched to support our hypergrowth. If you want to challenge yourself and reimagine how businesses operate, we're hiring across roles in Singapore, India and the Philippines. I can assure you it will be fun!

@pushpal_95

[Pushpal Maheshwari @pushpal_95]

I’m calling it.
OpenAI and Anthropic have got it wrong.

You’ll need FDOs (Forward Deployed Outcome Managers) - basically Product Managers who want to consult instead of FDEs to help enterprise w AI transformation.

We know this because we have been spending time on the ground.

@sama @DarioAmodei

[Pushpal Maheshwari @pushpal_95]

NOT AI

[Pushpal Maheshwari @pushpal_95]

@anshulbhide + we’re also expanding our GTM through the partner model. This article is like the description of how we operate as a company today @yzthakker fyi

[Pushpal Maheshwari @pushpal_95]

@anshulbhide 100% agree. Even we are moving from a pyramid structure to a diamond team structure. Easier to do when you’re young and small - super tough to do when you have existing long term contracts based on seats/TnM.

[Pushpal Maheshwari @pushpal_95]

@anshulbhide @yzthakker - basically your entire India trip :)

[Pushpal Maheshwari @pushpal_95]

Box cricket!
#INDvsNZ

[Pushpal Maheshwari @pushpal_95]

I foresee this headline before 2027 July.

<Top Indian IT services Company> announces India's largest job cut ever with 20k+ software developers laid off.

Top 100 IT services companies in India employ about 60L people. I'd think 40L of them are software developers. A 15% job cut is super conservative. That's about 6L people. Only from top 100 companies.

Plus, hiring from undergrad colleges will go down significantly. It's a double whammy.

I'm in the middle of the madness and trust me, we're not ready. India is not ready. All job functions will be impacted- but software development will be the first and the worst hit. I genuinely believe Software Development AGI is achieved.

I don't have a playbook on how you can be ready for this but I have a directional sense. I’ll share more here soon.

[Pushpal Maheshwari @pushpal_95]

Read this 9 minutes ago and I’m still laughing!

[Arindam Paul @arindam___paul]

Everytime Anthropic releases an update/feature, many companies/industries feel “claude lag gaye”

[Pushpal Maheshwari @pushpal_95]

@skirani This is well put, thanks for sharing. Good framework to create a PoV on ideas.

How do you identify which service would continue to be externally sourced vs internally built? (Basically, what’s the future of services agencies?)

[Pushpal Maheshwari @pushpal_95]

Introducing https://t.co/RlE27E6Qjm
AI Consulting for Retail and E-Commerce brands.

@yzthakker

[Pushpal Maheshwari @pushpal_95]

True story.

[Naval @naval]

It’s not about junior vs senior, it’s about “good with AI” vs “not good with AI.”

[Pushpal Maheshwari @pushpal_95]

@tejeshwi_sharma Agree, 100%
How do you see AI-native services evolving - specifically in software development industry?