Investing & Tech →Newsletter/Podcast @Sourceryy | HUGE Fan @brexhq @Turingcom @public @deel
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Molly O’Shea_ retweeted
If chips, models, networks & agent architectures all become dramatically more efficient..
Do we still really need $50-100 Billion data centers?
"I think you're gonna see this new wave of companies that are doing distributed data centers. These data centers are mid-size.
It's gonna be even more important as you go into this agentic world because you're not dealing with a single model and a single prompt. These agents are orchestrating with each other."
NEW: Premium Inference 101
The Economics & Infrastructure Behind Running Trillion Parameter Models
@RodrigoLiang, CEO & Co-Founder of @SambaNovaAI
"Inference has arrived. 70-80% of those racks are running inference."
"[Inference services] are generating lots of revenue, but not enough margin. In order for them to sustain, they've gotta be more profitable."
"With SambaNova, that min quantum is down to 1 rack. Where if you have other service providers, [with] say, a DeepSeek model, now 1.5 trillion parameters, to run that, the min for some of the other providers might be 10-20 racks."
SambaNova builds full-stack inference infrastructure. 16 chips to a 10kW air-cooled rack that runs trillion parameter models, where a GPU rack pulls 130kW.
They just demonstrated the fastest MiniMax M2.7 inference in the world, as benchmarked by Artificial Analysis.
The demo paired one NVIDIA H200 rack for prefill with one SambaRack SN50 for decode. Disaggregated inference: GPUs load the context, RDUs generate the tokens.
Now serving JPMorgan, SoftBank, Saudi Aramco & DOE national labs, just valued at $11B on a $1B Series F led by General Atlantic.
We Cover:
› Why inference will need orders of magnitude more chips than training ever did
› The 10kW rack vs the 130kW rack, & why air cooling decides geography
› Running a 1T parameter model in one rack at full precision, no quantization
› The agent latency problem: 20 agents, 2 seconds each, 40 seconds gone
› Revenue per rack, & why inference providers have revenue but no margin
› JPMorgan, sovereignty, & the move back to on-prem
Filmed at the @RaiseSummit in Paris.
Thank you to Brex, MongoDB & AssemblyAI for helping make this trip & content series happen.
𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒
(00:00) Rodrigo Liang , Co-Founder & CEO at SambaNova Systems
(00:59) SambaNova’s Series F: $1B raise at an $11 billion valuation
(03:00) The Inference problem nobody saw coming
(04:52) SambaNova's chip evolution
(07:19) Running a trillion-parameter model on a single rack
(11:00) Do $100 billion data centers actually make sense?
(14:14) What "premium inference" really means
(18:28) Speed is about to become AI's biggest price tag
(20:43) Starlink, edge computing, & AI reaching every corner of the planet
(24:27) Working alongside NVIDIA & rival chipmakers
(27:49) How customers actually measure inference performance
(32:07) The biggest bottlenecks in AI's global land grab
(35:12) Justifying the billion-dollar AI valuations
(37:53) Why SambaNova refuses to build its own cloud
(41:03) The "AI sovereignty" debate
(43:48) Data privacy fears are driving the return to on-prem AI
(47:55) How to actually get ROI out of AI spend
(51:16) The one question every business should be asking about AI
(56:09) The mentors & lessons behind a 32-year career in chips
(58:02) Unveiling SambaNova's newest chip, the SN50
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Molly O’Shea_ retweeted
"Scale often has surprising emergent properties. Compounding exponentials are magic. In particular, you really want to build a business that gets a compounding advantage with scale." - Sam Altman
SambaNova CEO Rodrigo Liang says AI is a "land grab" for customers right now and scale is the only thing that matters:
"It's all about scaling. It's all about who can get to scale faster."
"We've seen over history, the large players globally or regionally end up having this enduring lasting impact in the market."
"So people are investing a lot to go and grab the users, grab the customers, because usually once you're in, once you're using Microsoft or Google Gemini, you're pretty much in that ecosystem for a while."
" People forget, as much as Nvidia costs: it's commodity."
NEW: Premium Inference 101
The Economics & Infrastructure Behind Running Trillion Parameter Models
@RodrigoLiang, CEO & Co-Founder of @SambaNovaAI
"Inference has arrived. 70-80% of those racks are running inference."
"[Inference services] are generating lots of revenue, but not enough margin. In order for them to sustain, they've gotta be more profitable."
"With SambaNova, that min quantum is down to 1 rack. Where if you have other service providers, [with] say, a DeepSeek model, now 1.5 trillion parameters, to run that, the min for some of the other providers might be 10-20 racks."
SambaNova builds full-stack inference infrastructure. 16 chips to a 10kW air-cooled rack that runs trillion parameter models, where a GPU rack pulls 130kW.
They just demonstrated the fastest MiniMax M2.7 inference in the world, as benchmarked by Artificial Analysis.
The demo paired one NVIDIA H200 rack for prefill with one SambaRack SN50 for decode. Disaggregated inference: GPUs load the context, RDUs generate the tokens.
Now serving JPMorgan, SoftBank, Saudi Aramco & DOE national labs, just valued at $11B on a $1B Series F led by General Atlantic.
We Cover:
› Why inference will need orders of magnitude more chips than training ever did
› The 10kW rack vs the 130kW rack, & why air cooling decides geography
› Running a 1T parameter model in one rack at full precision, no quantization
› The agent latency problem: 20 agents, 2 seconds each, 40 seconds gone
› Revenue per rack, & why inference providers have revenue but no margin
› JPMorgan, sovereignty, & the move back to on-prem
Filmed at the @RaiseSummit in Paris.
Thank you to Brex, MongoDB & AssemblyAI for helping make this trip & content series happen.
𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒
(00:00) Rodrigo Liang , Co-Founder & CEO at SambaNova Systems
(00:59) SambaNova’s Series F: $1B raise at an $11 billion valuation
(03:00) The Inference problem nobody saw coming
(04:52) SambaNova's chip evolution
(07:19) Running a trillion-parameter model on a single rack
(11:00) Do $100 billion data centers actually make sense?
(14:14) What "premium inference" really means
(18:28) Speed is about to become AI's biggest price tag
(20:43) Starlink, edge computing, & AI reaching every corner of the planet
(24:27) Working alongside NVIDIA & rival chipmakers
(27:49) How customers actually measure inference performance
(32:07) The biggest bottlenecks in AI's global land grab
(35:12) Justifying the billion-dollar AI valuations
(37:53) Why SambaNova refuses to build its own cloud
(41:03) The "AI sovereignty" debate
(43:48) Data privacy fears are driving the return to on-prem AI
(47:55) How to actually get ROI out of AI spend
(51:16) The one question every business should be asking about AI
(56:09) The mentors & lessons behind a 32-year career in chips
(58:02) Unveiling SambaNova's newest chip, the SN50
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Molly O’Shea_ retweeted
Kimi K3 gives the open vs closed source debate a run for its money..
"Even the open source models are already 1-2 trillion parameter models."
"These models are being valued significantly for the output they generate because if you can trust it to produce good output, you don't have to invest as much human energy to go double-check it."
@SambaNovaAI CEO Rodrigo Liang:
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Molly O’Shea_ retweeted
"The real “Sputnik moment” would be an open-source frontier model that was also token efficient unlike Kimi K3 which is 50-70% more expensive to run than GPT 5.6 per Artificial Analysis." @GavinSBaker
How costs play out at the inference layer:
"Most providers purchase per rack, they operate per rack, & so they wanna generate *revenue per rack*."
"If I put a rack of hardware, I'm just seeing how many tokens am I generating in a particular model.. that model has a price per token."
"Multiply that by 30days/mo, 24 hrs/day, number of tokens per second, & you can figure how much money that rack is generating.. you look at how much it's costing you to operate.
So that's as simple as that."
"That's what we focus on. We're very focused on making sure that when you deploy a rack of SambaNova you generate great margins relative to the model... you're still generating a significant number of tokens per month so that you're making profit on that rack."
@SambaNovaAI CEO @RodrigoLiang
NEW: Premium Inference 101
The Economics & Infrastructure Behind Running Trillion Parameter Models
@RodrigoLiang, CEO & Co-Founder of @SambaNovaAI
"Inference has arrived. 70-80% of those racks are running inference."
"[Inference services] are generating lots of revenue, but not enough margin. In order for them to sustain, they've gotta be more profitable."
"With SambaNova, that min quantum is down to 1 rack. Where if you have other service providers, [with] say, a DeepSeek model, now 1.5 trillion parameters, to run that, the min for some of the other providers might be 10-20 racks."
SambaNova builds full-stack inference infrastructure. 16 chips to a 10kW air-cooled rack that runs trillion parameter models, where a GPU rack pulls 130kW.
They just demonstrated the fastest MiniMax M2.7 inference in the world, as benchmarked by Artificial Analysis.
The demo paired one NVIDIA H200 rack for prefill with one SambaRack SN50 for decode. Disaggregated inference: GPUs load the context, RDUs generate the tokens.
Now serving JPMorgan, SoftBank, Saudi Aramco & DOE national labs, just valued at $11B on a $1B Series F led by General Atlantic.
We Cover:
› Why inference will need orders of magnitude more chips than training ever did
› The 10kW rack vs the 130kW rack, & why air cooling decides geography
› Running a 1T parameter model in one rack at full precision, no quantization
› The agent latency problem: 20 agents, 2 seconds each, 40 seconds gone
› Revenue per rack, & why inference providers have revenue but no margin
› JPMorgan, sovereignty, & the move back to on-prem
Filmed at the @RaiseSummit in Paris.
Thank you to Brex, MongoDB & AssemblyAI for helping make this trip & content series happen.
𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒
(00:00) Rodrigo Liang , Co-Founder & CEO at SambaNova Systems
(00:59) SambaNova’s Series F: $1B raise at an $11 billion valuation
(03:00) The Inference problem nobody saw coming
(04:52) SambaNova's chip evolution
(07:19) Running a trillion-parameter model on a single rack
(11:00) Do $100 billion data centers actually make sense?
(14:14) What "premium inference" really means
(18:28) Speed is about to become AI's biggest price tag
(20:43) Starlink, edge computing, & AI reaching every corner of the planet
(24:27) Working alongside NVIDIA & rival chipmakers
(27:49) How customers actually measure inference performance
(32:07) The biggest bottlenecks in AI's global land grab
(35:12) Justifying the billion-dollar AI valuations
(37:53) Why SambaNova refuses to build its own cloud
(41:03) The "AI sovereignty" debate
(43:48) Data privacy fears are driving the return to on-prem AI
(47:55) How to actually get ROI out of AI spend
(51:16) The one question every business should be asking about AI
(56:09) The mentors & lessons behind a 32-year career in chips
(58:02) Unveiling SambaNova's newest chip, the SN50
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Molly O’Shea_ retweeted
SambaNova Co-Founder & CEO @RodrigoLiang agrees with Palantir CEO Alex Karp: AI sovereignty is a massive issues for both companies and countries.
"Whether it's sovereignty at a national level or sovereignty at a corporate level, I don't want my data trained into a model and have that model shipped worldwide."
"Can you imagine if your bank account information starts showing up in ChatGPT in some other place in the world without your permission?"
"That's what people are thinking about: how do we protect our information in a way that it doesn't accidentally become part of the models?"
"What a lot of countries are doing is they're saying, 'We don't want to base off of a global model or an American model. We want to base it off of our own national model.'"
"Countries have started doing this work. You see this in Japan, and Korea announced the same thing."
"Other parts of the world are investing a significant amount of money to train from scratch their own national model for use cases in the government, for their own citizens, so it's not derived from an American model."
NEW: Premium Inference 101
The Economics & Infrastructure Behind Running Trillion Parameter Models
@RodrigoLiang, CEO & Co-Founder of @SambaNovaAI
"Inference has arrived. 70-80% of those racks are running inference."
"[Inference services] are generating lots of revenue, but not enough margin. In order for them to sustain, they've gotta be more profitable."
"With SambaNova, that min quantum is down to 1 rack. Where if you have other service providers, [with] say, a DeepSeek model, now 1.5 trillion parameters, to run that, the min for some of the other providers might be 10-20 racks."
SambaNova builds full-stack inference infrastructure. 16 chips to a 10kW air-cooled rack that runs trillion parameter models, where a GPU rack pulls 130kW.
They just demonstrated the fastest MiniMax M2.7 inference in the world, as benchmarked by Artificial Analysis.
The demo paired one NVIDIA H200 rack for prefill with one SambaRack SN50 for decode. Disaggregated inference: GPUs load the context, RDUs generate the tokens.
Now serving JPMorgan, SoftBank, Saudi Aramco & DOE national labs, just valued at $11B on a $1B Series F led by General Atlantic.
We Cover:
› Why inference will need orders of magnitude more chips than training ever did
› The 10kW rack vs the 130kW rack, & why air cooling decides geography
› Running a 1T parameter model in one rack at full precision, no quantization
› The agent latency problem: 20 agents, 2 seconds each, 40 seconds gone
› Revenue per rack, & why inference providers have revenue but no margin
› JPMorgan, sovereignty, & the move back to on-prem
Filmed at the @RaiseSummit in Paris.
Thank you to Brex, MongoDB & AssemblyAI for helping make this trip & content series happen.
𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒
(00:00) Rodrigo Liang , Co-Founder & CEO at SambaNova Systems
(00:59) SambaNova’s Series F: $1B raise at an $11 billion valuation
(03:00) The Inference problem nobody saw coming
(04:52) SambaNova's chip evolution
(07:19) Running a trillion-parameter model on a single rack
(11:00) Do $100 billion data centers actually make sense?
(14:14) What "premium inference" really means
(18:28) Speed is about to become AI's biggest price tag
(20:43) Starlink, edge computing, & AI reaching every corner of the planet
(24:27) Working alongside NVIDIA & rival chipmakers
(27:49) How customers actually measure inference performance
(32:07) The biggest bottlenecks in AI's global land grab
(35:12) Justifying the billion-dollar AI valuations
(37:53) Why SambaNova refuses to build its own cloud
(41:03) The "AI sovereignty" debate
(43:48) Data privacy fears are driving the return to on-prem AI
(47:55) How to actually get ROI out of AI spend
(51:16) The one question every business should be asking about AI
(56:09) The mentors & lessons behind a 32-year career in chips
(58:02) Unveiling SambaNova's newest chip, the SN50
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Molly O’Shea_ retweeted
"The winning AI companies will be those that offer the most intelligence per $ over time." - @GavinSBaker
Delivering AI profitably is becoming just as important as building great models..
"Today, inference services, they're not making enough margin."
"They're generating lots of revenue, but not generating enough margin. In order for them to sustain, they have to be more profitable."
"What we do is we generate more margins by giving them better inference service at a much lower cost."
SambaNova Co-Founder & CEO @RodrigoLiang:
Kimi K3 may be an important inflection point for AI. Potentially negative for Anthropic and OpenAI while being net positive for essentially every other company in the world. I mean that very literally. Although the real “Sputnik moment” would be an open-source frontier model that was also token efficient unlike Kimi K3 which is 50-70% more expensive to run than GPT 5.6 per Artificial Analysis.
Rationale:
A world where there are only 2-3 dominant frontier labs with 90% inference margins is net negative for every other layer while being awesome for those 2-3 labs. Those labs would become monopsonies for power, data centers, semiconductors and hyperscalers and would obviously vertically integrate over time into all those layers while also completely subsuming the application/software layers.
Anything that lowers margins and increases competition at the model layer is good for every other AI layer: power, semiconductors, hyperscalers, neoclouds and yes even software.
This is why Jensen is so supportive of open-source. An open-source model requires the *exact* same amount of compute to run as a closed frontier model of similar size and architecture. Kimi K3 is roughly the same price as GPT 5.6 Terra on a per token basis, which actually suggests that it is less computationally efficient as I am sure that GPT 5.6 is priced to a higher margin than K3. And given that K3 is a token wastrel, i.e. token inefficient, it is significantly more expensive per task than GPT 5.6 and Grok 4.5, which are much more token efficient. Cost per token and token efficiency (i.e. intelligence density per token) are the drivers of intelligence per unit of cost. The winning AI companies will be those that offer the most intelligence per $ over time.
Lower margin % at the model layer = more margin $ at every part of the infrastructure layer and is a godsend for software. This can happen either through open-source models like K3 at the frontier *or* having a vertically integrated model company like Meta, SpaceX or Google at the frontier. Both outcomes result in a lower margin % at the model layer as vertically integrated model companies don’t really care where the margin $ come from. This is why it was so painful for OpenAI and Anthropic when Google was right there with them from a model competitiveness perspective and why Grok 4.5 and Muse 1.1 were just as important as Kimi K3.
The reason Kimi K3 is only *potentially* negative for Anthropic and OpenAI is 1) the @ericvishria point that the Claude and ChatGPT products and harnesses may be more important than their models today and 2) the hypothesis that they have much more advanced model checkpoints internally that are already being used for RSI. In the latter scenario, reaching RSI even a few months ahead of other labs might be enough to cement a permanent lead.
Time will tell on both points. And likely fairly quickly.
Caveat would be that since Kimi K3 is not token efficient and thereby actually more expensive than ChatGPT 5.6, we may need to see a more token efficient open-source model at the frontier or see Grok 5/Composer 4/Muse 2 at multiple points on the Pareto frontier for this potential risk to Anthropic and OpenAI to play out. And I am sure they will both vertically integrate as quickly as possible while continuing the product/harness strength they have shown over the last 8 months.
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Molly O’Shea_ retweeted
NEW: Premium Inference 101
The Economics & Infrastructure Behind Running Trillion Parameter Models
@RodrigoLiang, CEO & Co-Founder of @SambaNovaAI
"Inference has arrived. 70-80% of those racks are running inference."
"[Inference services] are generating lots of revenue, but not enough margin. In order for them to sustain, they've gotta be more profitable."
"With SambaNova, that min quantum is down to 1 rack. Where if you have other service providers, [with] say, a DeepSeek model, now 1.5 trillion parameters, to run that, the min for some of the other providers might be 10-20 racks."
SambaNova builds full-stack inference infrastructure. 16 chips to a 10kW air-cooled rack that runs trillion parameter models, where a GPU rack pulls 130kW.
They just demonstrated the fastest MiniMax M2.7 inference in the world, as benchmarked by Artificial Analysis.
The demo paired one NVIDIA H200 rack for prefill with one SambaRack SN50 for decode. Disaggregated inference: GPUs load the context, RDUs generate the tokens.
Now serving JPMorgan, SoftBank, Saudi Aramco & DOE national labs, just valued at $11B on a $1B Series F led by General Atlantic.
We Cover:
› Why inference will need orders of magnitude more chips than training ever did
› The 10kW rack vs the 130kW rack, & why air cooling decides geography
› Running a 1T parameter model in one rack at full precision, no quantization
› The agent latency problem: 20 agents, 2 seconds each, 40 seconds gone
› Revenue per rack, & why inference providers have revenue but no margin
› JPMorgan, sovereignty, & the move back to on-prem
Filmed at the @RaiseSummit in Paris.
Thank you to Brex, MongoDB & AssemblyAI for helping make this trip & content series happen.
𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒
(00:00) Rodrigo Liang , Co-Founder & CEO at SambaNova Systems
(00:59) SambaNova’s Series F: $1B raise at an $11 billion valuation
(03:00) The Inference problem nobody saw coming
(04:52) SambaNova's chip evolution
(07:19) Running a trillion-parameter model on a single rack
(11:00) Do $100 billion data centers actually make sense?
(14:14) What "premium inference" really means
(18:28) Speed is about to become AI's biggest price tag
(20:43) Starlink, edge computing, & AI reaching every corner of the planet
(24:27) Working alongside NVIDIA & rival chipmakers
(27:49) How customers actually measure inference performance
(32:07) The biggest bottlenecks in AI's global land grab
(35:12) Justifying the billion-dollar AI valuations
(37:53) Why SambaNova refuses to build its own cloud
(41:03) The "AI sovereignty" debate
(43:48) Data privacy fears are driving the return to on-prem AI
(47:55) How to actually get ROI out of AI spend
(51:16) The one question every business should be asking about AI
(56:09) The mentors & lessons behind a 32-year career in chips
(58:02) Unveiling SambaNova's newest chip, the SN50
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Molly O’Shea_ retweeted
Kimi-K3 this, Muse Spark 1.1 that..
Thinky Inkling this, Claude Fable 5 that..
GPT-5.6 Sol this, Grok-4.5 that.
ok but what are your plans for premium inference?
Interview with @SambaNovaAI CEO & Co-Founder Rodrigo Liang coming soon..
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Molly O’Shea_ retweeted
Fmr SpaceX CIO Ken Venner joined in 2012 when it was *only* a $1B company.
Today, post-IPO $SPCX briefly touched ~$3T in market cap..
"SpaceX wasn't a name. I had no idea who they are."
All it took was a factory tour & @elonmusk's vision to "save humanity from itself by colonizing another planet" to win him on the mission.
Here's the story behind why Ken took the job:
"He invited me up one day to just do a tour of the factory. I wasn't looking for a job, but he invited me up.
I did this tour of the factory, which is in Hawthorne, downtown LA-ish. Here they are building big metal objects supposedly cheaper than anyone else in the world in downtown LA, and I'm like, 'Well this is crazy.'
But I have manufacturing in my blood. My entire career's been in manufacturing. I'm like, 'Well this is a pretty interesting place.'
I have no idea who they are. I don't know what they do for a living, 'cause SpaceX wasn't a name back in those days, and they're an hour at least away from where I live.
So he offered me a job, and I'm like, 'I don't know that I wanna drive an hour up to this place.'
But it was such an interesting story that Elon was going to save humanity from itself by colonizing another planet, 'cause we're gonna screw this one up & we need to keep humankind going.
And it was another scaling opportunity, so I thought about it, talked to my wife, and in the end decided to bite the bullet and take it.
It was a great move."
"Elon had hired me 'cause he wanted to build the digital nervous system for the 21st century rocket company."
BREAKING: @Senra_Systems Raises $65M Series B co-led by @lowercarbon & Interlagos
Total funding hits $112M+
Full interviews:
› Co-Founder & CEO @jordan__black
› Ken Venner Chief Tech & Product Officer (fmr SpaceX CIO)
› Factory tour
Wire harnesses are the nervous system of every rocket, car, & missile. Up to 25% of a vehicle's cost, & still 100% built by hand.
Senra Systems is rebuilding wire harness manufacturing around software.
Series B participation from General Catalyst, Sequoia Capital, Andreessen Horowitz, Founders Fund, Dylan Field, CIV, 8VC, The Friedkin Group, Jaws Estates Capital, Sozo Ventures, & Alumni Ventures
We cover:
› $65M round & Factory 3
› Ken Venner on scaling Broadcom & SpaceX
› Quitting SpaceX to build harnesses on an apartment carpet
› Supplying the fastest-scaling defense companies in America
› Amp, their operating system
› Where the name Senra comes from
𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒
(00:00) Jordan Black, Co-Founder & CEO & Ken Venner, CTPO at Senra Systems
(00:57) Senra raises a $65M Series B
(01:36) What Senra Systems actually builds
(05:18) Why they built two very different factories
(07:00) The reindustrialization of America
(09:09) Commercial vs. Government: who's really buying?
(09:52) The origin story
(17:35) Turning a 2-year training pipeline into 4 weeks
(25:06) Bringing on SpaceX legend Ken Venner
(29:23) Why Jordan became obsessed with wire harnessing
(31:46) The investors betting big on wire harnesses
(34:16) The real story behind the name "Senra"
(36:49) Will the SpaceX IPO fuel Senra's growth?
(39:32) Ken Venner
(41:40) Scaling lessons from Broadcom and SpaceX
(43:21) Why Ken left Broadcom for SpaceX
(44:40) Life as SpaceX's CIO
(46:11) The secret to Elon Musk's management style
(47:15) What it's really like working with Gwynne Shotwell
(51:28) Scaling with AI vs. pure headcount
(55:04) Factory tour with Jordan
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Molly O’Shea_ retweeted
This pullback on $MSFT will soon be looked back upon as the greatest dip-buy opportunity ever seen…
$MSFT has officially neared its 200WMA which has been tested just 3 times in the past 13 years.
Every time following this re-test, $MSFT ran over 150%+ higher.
This time around will be no different.
$600+ is incoming this year…
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Molly O’Shea_ retweeted
$AMD shares spike 14%+ in pre market after announcing a deal with $META to sell them up to $100B worth of AI chips.
This deal secures up to 6 gigawatts of capacity, & potentially a 10% stake in $AMD.
Additionally $META will get a warrant for up to 160M $AMD shares.
Huge news…
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Molly O’Shea_ retweeted
Notable names seeing significant % drawdowns from all time highs
S&P 500 $SPY: -2%
Microsoft $MSFT: -30%
Amazon $AMZN: -20%
ServiceNow $NOW: -58%
Synopsys $SNPS: -36%
Palanatir $PLTR: -38%
Robinhood $HOOD: -53%
Adobe $ADBE: -65%
Salesforce $CRM: -52%
Oracle $ORCL: -60%
Buying the dip on such significant drawdowns on fundamentally strong companies is how generational wealth is made.
Save this post to look back on later this year…
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Molly O’Shea_ retweeted
I’ve said it before, & I’ll say it one final time…
All indicators have officially aligned for $SPY to see a -12% crash heading into April/May.
This doesn’t mean to sell; rather reposition into more stable assets and stocks.
This will be your ONLY dip buying opportunity for 2026.
Save this for later…
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Molly O’Shea_ retweeted
I AM FINISHING THE FEBRUARY $100 TO $100,000 ACCOUNT CHALLENGE THIS WEEK!
I TURNED $100 INTO $128,000+ IN 27 DAYS LAST YEAR; MILLIONAIRES WERE MADE SIMPLY BY FOLLOWING ME.
LIKE THIS POST IN ORDER TO GET ACCESS TO THE FREE ACCOUNT CHALLENGE GROUP FOR ALL TRADES POSTED!! ❤️
$SPY
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Molly O’Shea_ retweeted
The link to join the free discord for the February $100 account challenge is right below!!
discord.gg/abGS9U6Cz
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Molly O’Shea_ retweeted
The President, White House, & even Sec of War have been quite literally telling you what to buy…
The next sector to see euphoric upside will be Drone technology.
These names are next up to squeeze:
1. $ONDS
2. $AVAV
3. $KTOS
Don’t miss out on this upcoming sector boom…
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