MAROKO133 Breaking ai: AWS launches Kiro powers with Stripe, Figma, and Datadog integratio

📌 MAROKO133 Update ai: AWS launches Kiro powers with Stripe, Figma, and Datadog in

Amazon Web Services (AWS) has introduced Kiro powers, a system that allows software developers to give their AI coding assistants instant, specialized expertise in specific tools and workflows — addressing what the company calls a fundamental bottleneck in how AI agents operate today.

AWS announced Kiro powers at its annual re:Invent conference in Las Vegas. The capability marks a departure from how most AI coding tools work today. Typically, these tools load every possible capability into memory upfront — a process that burns through computational resources and can overwhelm the AI with irrelevant information. Kiro powers takes the opposite approach, activating specialized knowledge only at the moment a developer actually needs it.

"Our goal is to give the agent specialized context so it can reach the right outcome faster — and in a way that also reduces cost," Deepak Singh, VP of developer agents and experiences at Amazon, told VentureBeat in an exclusive interview.

The launch includes partnerships with nine technology companies: Datadog, Dynatrace, Figma, Neon, Netlify, Postman, Stripe, Supabase and AWS's own services. Developers can also create and share their powers with the community.

Why AI coding assistants choke when developers connect too many tools

Kiro powers comes amidst growing tension in the AI development tool market.

Modern AI coding assistants rely on Model Context Protocol (MCP) to connect with external tools and services. When a developer wants their AI assistant to work with Stripe for payments, Figma for design and Supabase for databases, they connect MCP servers for each service.

The problem: Each connection loads dozens of tool definitions into the AI's working memory before it writes a single line of code. According to AWS documentation, connecting just five MCP servers can consume more than 50,000 tokens — roughly 40% of an AI model's context window — before the developer even types their first request.

Developers have grown increasingly vocal about this issue. Many complain that they don't want to burn through their token allocations just to have an AI agent figure out which tools are relevant to a specific task. They want to get to their workflow instantly — not watch an overloaded agent struggle to sort through irrelevant context.

This phenomenon, which some in the industry call "context rot," leads to slower responses, lower-quality outputs and significantly higher costs — since AI services typically charge by the token.

Inside the technology that loads AI expertise on demand

Kiro powers addresses this by packaging three components into a single, dynamically-loaded bundle.

The first is a steering file, POWER.md, which functions as an onboarding manual. It tells the AI agent what tools are available and, crucially, when to use them. The second component is the MCP server configuration itself — the actual connection to external services. The third includes optional hooks and automation that trigger specific actions.

When a developer mentions "payment" or "checkout" in their conversation with Kiro, the system automatically activates the Stripe power, loading its tools and best practices into context. When the developer shifts to database work, Supabase activates while Stripe deactivates. The baseline context usage when no powers are active approaches zero.

"You click a button and it automatically loads," Singh said. "Once a power has been created, developers just select 'open in Kiro' and it launches the IDE with everything ready to go."

How AWS is bringing elite developer techniques to the masses

Singh framed Kiro powers as a democratization of advanced development practices. Before this capability, only the most sophisticated developers knew how to properly configure their AI agents with specialized context — writing custom steering files, crafting precise prompts and manually managing which tools were active at any given time.

"We've found that our developers were adding in capabilities to make their agents more specialized," Singh said. "They wanted to give the agent some special powers for a specific problem. For example, they wanted … the agent to become an expert at backend-as-a-service."

This observation led to a key insight: If Supabase or Stripe could build the optimal context configuration once, every developer using those services could benefit.

"Kiro powers formalizes things that only the most advanced people were doing, and allows anyone to get those kinds of skills," Singh said.

Why dynamic loading beats fine-tuning for most AI coding use cases

The announcement also positions Kiro powers as a more economical alternative to fine-tuning, or the process of training an AI model on specialized data to improve its performance in specific domains.

"It's much cheaper" compared to fine-tuning, Singh. "Fine-tuning is very expensive, and you can't fine-tune most frontier models."

This is a significant point. The most capable AI models from Anthropic, OpenAI and Google are typically "closed source," meaning developers cannot modify their underlying training. They can only influence the models' behavior through the prompts and context they provide.

"Most people are already using powerful models like Sonnet 4.5 or Opus 4.5," Singh said. "Those models need to be pointed in the right direction."

The dynamic loading mechanism also reduces ongoing costs. Because powers only activate when relevant, developers aren't paying for token usage on tools they're not currently using.

Where Kiro powers fits into Amazon's bigger bet on autonomous AI agents

Kiro powers arrives as part of a broader push by AWS into what the company calls "agentic AI" — AI systems that can operate autonomously over extended periods.

At re:Invent, AWS also announced three "frontier agents" designed to work for hours or days without human intervention: Kiro autonomous agent for software development, AWS security agent and AWS DevOps agent. These represent a different approach from Kiro powers — tackling large, ambiguous problems rather than providing specialized expertise for specific tasks.

The two approaches are complementary. Frontier agents handle complex, multi-day projects that require autonomous decision-making across multiple codebases. Kiro powers, by contrast, gives developers precise, efficient tools for everyday development tasks where speed and token efficiency matter most.

The company is betting that developers need both ends of this spectrum to be productive.

What Kiro powers reveals about the future of AI-assisted software development

The launch reflects a maturing market for AI development tools. GitHub Copilot, which Microsoft launched in 2021, introduced millions of developers to AI-assisted coding. Since…

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🔗 Sumber: venturebeat.com

📌 MAROKO133 Breaking ai: Gong study: Sales teams using AI generate 77% more revenu

The debate over whether AI belongs in the corporate boardroom appears to be over — at least for those responsible for generating revenue.

Seven in 10 enterprise revenue leaders now trust AI to regularly inform their business decisions, according to a sweeping new study released by revenue intelligence company Gong. The finding marks a dramatic shift from just two years ago, when most organizations treated AI as an experimental technology relegated to pilot programs and individual productivity hacks.

The research, based on an analysis of 7.1 million sales opportunities across more than 3,600 companies and a survey of over 3,000 global revenue leaders spanning the U.S., UK, Australia and Germany, paints a picture of an industry in rapid transformation. Organizations that have embedded AI into their core go-to-market strategies are 65% more likely to increase their win rates than competitors still treating the technology as optional.

"I don't think people delegate decisions to AI, but they do rely on AI in the process of making decisions," Amit Bendov, Gong's co-founder and chief executive, said in an exclusive interview with VentureBeat. "Humans are making the decision, but they're largely assisted."

The distinction matters. Rather than replacing human judgment, AI has become what Bendov describes as a "second opinion" — a data-driven check on the intuition and guesswork that has traditionally governed sales forecasting and strategy.

Slowing growth is forcing sales teams to squeeze more from every rep

The timing of AI's ascendance in revenue organizations is no coincidence. The study reveals a sobering reality: After rebounding in 2024, average annual revenue growth among surveyed companies decelerated to 16% in 2025, marking a three-percentage-point decline year over year. Sales rep quota attainment fell from 52% to 46% over the same period.

The culprit, according to Gong's analysis, isn't that salespeople are performing worse on individual deals. Win rates and deal duration remained consistent. The problem is that representatives are working fewer opportunities — a finding that suggests operational inefficiencies are eating into selling time.

This helps explain why productivity has rocketed to the top of executive priorities. For the first time in the study's history, increasing the productivity of existing teams ranked as the number-one growth strategy for 2026, jumping from fourth place the previous year.

"The focus is on increasing sales productivity," Bendov said. "How much dollar-output per dollar-input?"

The numbers back up the urgency. Teams that regularly use AI tools generate 77% more revenue per representative than those that don't — a gap Gong characterizes as a six-figure difference per salesperson annually.

Companies are moving beyond basic AI automation toward strategic decision-making

The nature of AI adoption in sales has evolved considerably over the past year. In 2024, most revenue teams used AI for basic automation: Transcribing calls, drafting emails, updating CRM records. Those use cases continue to grow, but 2025 marked what the report calls a shift "from automation to intelligence."

The number of U.S. companies using AI for forecasting and measuring strategic initiatives jumped 50% year over year. These more sophisticated applications — predicting deal outcomes, identifying at-risk accounts, measuring which value propositions resonate with different buyer personas — correlate with dramatically better results.

Organizations in the 95th percentile of commercial impact from AI were 2 to 4X more likely to have deployed these strategic use cases, according to the study.

Bendov offered a concrete example of how this plays out in practice. "Companies have thousands of deals that they roll up into their forecast," he said. "It used to be based solely on human sentiment, believe it or not. That's why a lot of companies miss their numbers: Because people say, 'Oh, he told me he'll buy,' or 'I think I can probably get this one.'"

AI changes that calculus by examining evidence rather than optimism. "Companies now get a second opinion from AI on their forecasting, and that improves forecasting accuracy dramatically — 10 [or] 15% better accuracy just because it's evidence-based, not just based on human sentiment," Bendov said.

Revenue-specific AI tools are dramatically outperforming general-purpose alternatives

One of the study's more provocative findings concerns the type of AI that delivers results. Teams using revenue-specific AI solutions — tools built explicitly for sales workflows rather than general-purpose platforms like ChatGPT — reported 13% higher revenue growth and 85% greater commercial impact than those relying on generic tools.

These specialized systems were also twice as likely to be deployed for forecasting and predictive modeling, the report found.

The finding carries obvious implications for Gong, which sells precisely this type of domain-specific platform. But the data suggests a real distinction in outcomes. General-purpose AI, while more prevalent, often creates what the report describes as a "blind spot" for organizations — particularly when employees adopt consumer AI tools without company oversight.

Research from MIT suggests that while only 59% of enterprise teams use personal AI tools like ChatGPT at work, the actual figure is likely closer to 90%. This shadow AI usage poses security risks and creates fragmented technology stacks that undermine the potential for organization-wide intelligence.

Most sales leaders believe AI will reshape their jobs rather than eliminate them

Perhaps the most closely-watched question in any AI study concerns employment. The Gong research offers a more nuanced picture than the apocalyptic predictions that often dominate headlines.

When asked about AI's three-year impact on revenue headcount, 43% of respondents said they expect it to transform jobs without reducing headcount — the most common response. Only 28% anticipate job eliminations, while 21% actually foresee AI creating new roles. Just 8% predict minimal impact.

Bendov frames the opportunity as reclaiming lost time. He cited Forrester research indicating that 77% of a sales representative's time is spent on activities that don't involve customers — administrative work, meeting preparation, researching accounts, updating forecasts and internal briefings.

"AI can eliminate, ideally, 77% of the drudgery work that they're doing," Bendov said. "I don't think it necessarily eliminates jobs. People are half productive right now. Let's make them fully productive, and whatever you're paying them will translate to much higher revenue."

The transformation is already visible in role consolidation. Over the past decade, sales organizations splintered into hyper-specialized functions: One person qualifies leads, another sets appointments, a third closes deals, a fourth handles onboarding. The result was customers interacting with five or six different people across their buying journey.

"Which is not a great buyer experience, because every time I meet a new person that might not have the full con…

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🔗 Sumber: venturebeat.com